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Journal articles on the topic 'Segmentation technology'
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1
Zhu, Ke. "Analysis of Chinese Word Segmentation Technology." Applied Mechanics and Materials 687-691 (November 2014): 1540–43. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1540.
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Under the influence of network information resources in exponentially growing, we are entering the information society, and all aspects of our lives has permeated with information technology. But the Chinese word segmentation technology for processing Chinese information becomes more and more important. Therefore, this paper sets out a series of Chinese word segmentation techniques, which mainly consists of Chinese word segmentation technology based on statistic, Chinese word segmentation techniques based on dictionary and hybrid techniques of Chinese word segmentation and segmentation technology based on knowledge and understanding.
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Hu, Chang Jie, and Hong Li Xu. "Face Image Segmentation Technology Research." Advanced Materials Research 846-847 (November 2013): 1339–42. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1339.
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Face contains the very rich information, which is a typical biological feature .It has a wide application prospect in personal identification, intelligent video surveillance and human-computer interaction. Face detection is to determine the number, the location, size and other information of all the faces among the color images that have been input. Firstly, skin color model is established, and then we use the skin color model to convert color image to gray image, and then we can denoise gray image, at last use the Fisher criterion to obtain the dynamic threshold segmentation of the face image, so as to lay a good foundation for the location of the face region. Through the experiment we can see, the selection of dynamic threshold, for different detecting images, obtained better color segmentation.
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Han, Dan, and Zhi Han Yu. "The Critical Technology Development Status of Machine Translation." Advanced Materials Research 791-793 (September 2013): 1622–25. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1622.
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In this article, we mainly introduce some basic concepts about machine translation. Machine translation means translating a natural language text to another by software. It can be divided into two categories: rule-based and corpus-based. IBM's statistical machine translation, Microsoft's multi-language machine translation project, AT & T's voice translation system and CMUs PANGLOSS system are three typical machine translation systems. Due to sentences are constructed by words continuously in Chinese. Chinese word segmentation is very essential. Three methods of Chinese word segmentation: segmentation methods based on string matching, segmentation method based on the understanding and segmentation method based on the statistics.
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Hu, Guangdong, Fengyuan Qian, Longgui Sha, and Zilong Wei. "Application of Deep Learning Technology in Glioma." Journal of Healthcare Engineering 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/8507773.
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A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily magnetic resonance imaging (MRI) oriented, is considered as one of common tools developed for doctors. These doctors use this system to examine, analyse, and diagnose appearance of the glioma’s outward for both patients, i.e., indoor and outdoor. In the literature, a widely utilized approach for the segmentation of glioma is the deep learning-oriented method. To cope with this issue, a segmentation of glioma approach, i.e., primarily on the convolution neural networks, is developed in this manuscript. A DM-DA-enabled cascading approach for the segmentation of glioma, which is 2DResUnet-enabled model, is reported to resolve the problem of spatial data acquisition of insufficient 3D specifically in the 2D full CNN along with the core issue of memory consumption of 3D full CNN. For gliomas segmentation at various stages, we have utilized multiscale fusion approach, attention, segmentation, and DenseBlock. Moreover, for reducing three dimensionalities of the Unet model, a sampling of fixed region is used along with multisequence data of the glioma image. Finally, the CNN model has the ability of producing a better segmentation of tumor preferably with minimum possible memory. The proposed model has used BraTS18 and BraTS17 benchmark data sets for fivefold cross-validation (local) and online evaluation preferably official, respectively. Evaluation results have verified that edema’s Dice Score preferable average, enhancement, and core areas of the segmentation of the glioma with DM-DA-Unet perform exceptionally well on the validation set of BraTS17. Finally, average sensitivity was observed to be high as well, which is approximately closer to the best segmentation model and its effect on the validation set of BraTS1 and has segmented gliomas accurately.
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Yang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, Lin Ma, Strahinja Stojadinovic, Robert Timmerman, Tu Dan, Zabi Wardak, Weiguo Lu, and Xuejun Gu. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (January 19, 2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.
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Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.
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Huo, Chun Bao, Shuai Tong, Li Hui Zhao, and Xiang Yun Li. "Research on Image Segmentation Technology with Tissue Section Cell Segmentation Algorithm." Advanced Materials Research 1046 (October 2014): 88–91. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.88.
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Generally, the effect of cell image that segmented via the threshold value method is not ideal generally; the found cell boundary cannot conform to the cell edge in the original picture well. In this paper, the threshold value segmentation method is improved; apply the judging criterion of gray level difference maximum interval to be the minimum, and conduct secondary treating on the image, and the image’s segmentation effect is more ideal.
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Johansson, David, Patrik Jönsson, Bodil Ivarsson, and Maria Christiansson. "Information Technology and Medical Technology Personnel´s Perception Regarding Segmentation of Medical Devices: A Focus Group Study." Healthcare 8, no. 1 (January 21, 2020): 23. http://dx.doi.org/10.3390/healthcare8010023.
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Objective: Segmentation is one way of improving data protection. The aim of this study was to investigate Information Technology (IT) and Medical Technology (MT) personnel’s perception in relation to ongoing segmentation of medical devices and IT infrastructure in the healthcare sector. Methods: Focus group interviews with 9 IT and 9 MT personnel in a county council in southern Sweden were conducted. The interviews focused on two areas: Positive expectations and misgivings. Digital recordings were transcribed verbatim and analyzed using qualitative content analysis. Results: Responses related to 2 main areas: Information security and implementation of segmentation. Informants stated that network segmentation would increase the overall level of cybersecurity for medical devices, addressing both insider and outsider threats. However, it would also increase the need for administration and the need for knowledge of the communication patterns of medical devices from the manufacturer’s perspective. Conclusion: IT and MT personnel in a county council in southern Sweden believed that segmentation would increase cybersecurity but also increase administration and resource needs, which are important opinions to take into consideration. The present study can be used as a model for others to increase awareness of opinions of healthcare organizations.
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Ramírez-Correa, Patricio E., F. Javier Rondán-Cataluña, and Jorge Arenas-Gaitán. "A Posteriori Segmentation of Personal Profiles of Online Video Games’ Players." Games and Culture 15, no. 3 (April 18, 2018): 227–47. http://dx.doi.org/10.1177/1555412018766786.
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There are diverse segmentations of online players in the literature. Most of them are proposed a priori, and there are no segmentations based on the acceptance of technology and the personal values of the players. The foremost purpose of this study is to obtain a posteriori segmentation of online video games’ players, founded on unified theory of acceptance and use of technology model, and to describe the subsequent segments consistent with the personal values of Schwartz. The measurement model and the structural model were analyzed with partial least squares (PLS). Subsequently, the PLS-prediction-oriented segmentation technique has been devoted to inspecting unobserved heterogeneity and to find players’ segments. Four segments are obtained from the statistical analysis, and data analysis shown that in each of the segments, the explained variance of both use and behavioral intention (which are the endogenous variables of the model) is significantly improved by comparing the results of the global sample.
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Ma, Ling, Xiaomao Hou, and Zhi Gong. "Image Segmentation Technology Based on Attention Mechanism and ENet." Computational Intelligence and Neuroscience 2022 (August 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/9873777.
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With the development of today’s society, medical technology is becoming more and more important in people’s daily diagnosis and treatment and the number of computed tomography (CT) images and MRI images is also increasing. It is difficult to meet today’s needs for segmentation and recognition of medical images by manpower alone. Therefore, the use of computer technology for automatic segmentation has received extensive attention from researchers. We design a tooth CT image segmentation method combining attention mechanism and ENet. First, dilated convolution is used with the spatial information path, with a small downsampling factor to preserve the resolution of the image. Second, an attention mechanism is added to the segmentation network based on CT image features to improve the accuracy of segmentation. Then, the designed feature fusion module obtains the segmentation result of the tooth CT image. It was verified on tooth CT image dataset published by West China Hospital, and the average intersection ratio and accuracy were used as the metric. The results show that, on the dataset of West China Hospital, Mean Intersection over Union (MIOU) and accuracy are 83.47% and 95.28%, respectively, which are 3.3% and 8.09% higher than the traditional model. Compared with the multiple watershed algorithm, the Chan–Vese segmentation algorithm, and the graph cut segmentation algorithm, our algorithm increases the calculation time by 56.52%, 91.52%, and 62.96%, respectively. It can be seen that our algorithm has obvious advantages in MIOU, accuracy, and calculation time.
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Fang, Jie, QingBiao Zhou, and Shuxia Wang. "Segmentation Technology of Nucleus Image Based on U-Net Network." Scientific Programming 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1892497.
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To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.
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Cai, Y. X., Y. Y. Xu, T. R. Zhang, and D. D. Li. "Threshold image target segmentation technology based on intelligent algorithms." Computer Optics 44, no. 1 (February 2020): 137–41. http://dx.doi.org/10.18287/2412-6179-co-630.
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This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.
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Xiong, Hui, Laith R. Sultan, Theodore W. Cary, Susan M. Schultz, Ghizlane Bouzghar, and Chandra M. Sehgal. "The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images." Ultrasound 25, no. 2 (January 25, 2017): 98–106. http://dx.doi.org/10.1177/1742271x17690425.
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Purpose To assess the diagnostic performance of a leak-plugging segmentation method that we have developed for delineating breast masses on ultrasound images. Materials and methods Fifty-two biopsy-proven breast lesion images were analyzed by three observers using the leak-plugging and manual segmentation methods. From each segmentation method, grayscale and morphological features were extracted and classified as malignant or benign by logistic regression analysis. The performance of leak-plugging and manual segmentations was compared by: size of the lesion, overlap area ( Oa) between the margins, and area under the ROC curves ( Az). Results The lesion size from leak-plugging segmentation correlated closely with that from manual tracing ( R2 of 0.91). Oa was higher for leak plugging, 0.92 ± 0.01 and 0.86 ± 0.06 for benign and malignant masses, respectively, compared to 0.80 ± 0.04 and 0.73 ± 0.02 for manual tracings. Overall Oa between leak-plugging and manual segmentations was 0.79 ± 0.14 for benign and 0.73 ± 0.14 for malignant lesions. Az for leak plugging was consistently higher (0.910 ± 0.003) compared to 0.888 ± 0.012 for manual tracings. The coefficient of variation of Az between three observers was 0.29% for leak plugging compared to 1.3% for manual tracings. Conclusion The diagnostic performance, size measurements, and observer variability for automated leak-plugging segmentations were either comparable to or better than those of manual tracings.
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Ruvinskaya, Victoria M., and Yurii Yu Timkov. "DEEP LEARNING TECHNOLOGY FOR VIDEOFRAME PROCESSING IN FACE SEGMENTATION ON MOBILE DEVICES." Herald of Advanced Information Technology 4, no. 2 (June 30, 2021): 185–94. http://dx.doi.org/10.15276/hait.02.2021.7.
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The aim of the research is to reduce the frame processing time for face segmentation on videos on mobile devices using deep learning technologies. The paper analyzes the advantages and disadvantages of existing segmentation methods, as well as their applicability to various tasks. The existing real-time realizations of face segmentation in the most popular mobile applications, which provide the functionality for adding visual effects to videos, were compared. As a result, it was determined that the classical segmentation methods do not have a suitable combination of accuracy and speed, and require manual tuning for a particular task, while the neural network-based segmentation methods determine the deep features automatically and have high accuracy with an acceptable speed. The method based on convolutional neural networks is chosen for use because, in addition to the advantages of other methods based on neural networks, it does not require such a significant amount of computing resources during its execution. A review of existing convolutional neural networks for segmentation was held, based on which the DeepLabV3+ network was chosen as having sufficiently high accuracy and being optimized for work on mobile devices. Modifications were made to the structure of the selected network to match the task of two classes segmentation and to speed up the work on devices with low performance. 8-bit quantization was applied to the values processed by the network for further acceleration. The network was adapted to the task of face segmentation by transfer learning performed on a set of face images from the COCO dataset. Based on the modified and additionally trained segmentation model, a mobile app was created to record video with real-time visual effects, which applies segmentation to separately add effects on two zones - the face (color filters, brightness adjustment, animated effects) and the background (blurring, hiding, replacement with another image). The time of frames processing in the application was tested on mobile devices with different technical characteristics. We analyzed the differences in testing results for segmentation using the obtained model and segmentation using the normalized cuts method. The comparison reveals a decrease of frame processing time on the majority of devices with a slight decrease of segmentation accuracy.
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Liu, Chang. "Research on Words Segmentation Technology in Chinese Full Text Retrieval System." Applied Mechanics and Materials 411-414 (September 2013): 313–16. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.313.
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In order to improve the speed of Chinese full-text retrieval in the premise of ensuring Chinese ambiguity inclusion and length limitation, this paper introduces the application methods of Chinese full-text retrieval system and the current application situation of Chinese word segmentation technology. Based on the existed word segmentation algorithms, this paper proposed an improved Chinese word segmentation algorithm. In the proposed method, the procedure of indexing is to construct the map between the relative words in the context and the dictionary. This paper improves the diction to realize better mapping with relative words, so as to realize Chinese words segmentation. The experiments demonstrate that the proposed Chinese full-text words segmentation algorithm is more effective than the existing methods.
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Sun, Guiling, Xinglong Jia, and Tianyu Geng. "Plant Diseases Recognition Based on Image Processing Technology." Journal of Electrical and Computer Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/6070129.
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A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.
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Iyer, Aditi, Maria Thor, Ifeanyirochukwu Onochie, Jennifer Hesse, Kaveh Zakeri, Eve LoCastro, Jue Jiang, et al. "Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT." Physics in Medicine & Biology 67, no. 2 (January 17, 2022): 024001. http://dx.doi.org/10.1088/1361-6560/ac4000.
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Abstract Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021. Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85–0.89) for the masseters, 0.80 (0.79–0.81) for the medial pterygoids, 0.81 (0.79–0.84) for the larynx, and 0.69 (0.67–0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request via https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. Significance. We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.
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Chu, Dong Xue. "Study on Chinese Word Segmentation Algorithm." Applied Mechanics and Materials 687-691 (November 2014): 1536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1536.
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Today's very popular search engine technology, which is conducive to further analysis and full-text retrieval technology for Chinese word segmentation technology, and Chinese word segmentation is an important technology of Chinese information, the quality of Chinese word segmentation will have a direct impact on the efficiency of Chinese information. Therefore, the related concepts of the Chinese algorithm are discussed in this paper, some specific algorithm for Chinese, like algorithms based on rules and dictionary, statistical algorithms based on large-scale corpus, unity algorithm of statistics and the rule, artificial intelligence word segmentation algorithms and so on, and finally it describes the evaluated basis and difficulty of Chinese word segmentation algorithm.
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Moen, M. A. N., A. P. Doulgeris, S. N. Anfinsen, A. H. H. Renner, N. Hughes, S. Gerland, and T. Eltoft. "Comparison of automatic segmentation of full polarimetric SAR sea ice images with manually drawn ice charts." Cryosphere Discussions 7, no. 3 (June 13, 2013): 2595–634. http://dx.doi.org/10.5194/tcd-7-2595-2013.
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Abstract. In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in-situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.
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Moen, M. A. N., A. P. Doulgeris, S. N. Anfinsen, A. H. H. Renner, N. Hughes, S. Gerland, and T. Eltoft. "Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts." Cryosphere 7, no. 6 (November 7, 2013): 1693–705. http://dx.doi.org/10.5194/tc-7-1693-2013.
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Abstract. In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by available in situ measurements. The segmentation result was then compared to ice charts drawn by ice service analysts. The comparison revealed big discrepancies between the charts of the analysts, and between the manual and the automatic segmentations. In the succeeding analysis, the automatic segmentation chart was labeled into ice types by sea ice experts, and the SAR features used in the segmentation were interpreted in terms of physical sea ice properties. Utilizing polarimetric information in sea ice charting will increase the efficiency and exactness of the maps. The number of classes used in the segmentation has shown to be of significant importance. Thus, studies of automatic and robust estimation of the number of ice classes in SAR sea ice scenes will be highly relevant for future work.
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Mao, Gaga. "Study on Chinese Word Segmentation." Advances in Higher Education 3, no. 3 (November 8, 2019): 1. http://dx.doi.org/10.18686/ahe.v3i3.1393.
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<p>Search engine technology is widely applied currently, which gradually deepens the research of full-text retrieval technology and Chinese word segmentation technology. Chinese word segmentation is one of the key technologies of Chinese languages information, the quality of which directly affects the information processing efficiency of Chinese languages. </p>
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Shen, Jiaqi, Fangfang Huang, and Myers Ulrich. "Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning." Journal of Medical Imaging and Health Informatics 10, no. 8 (August 1, 2020): 1892–98. http://dx.doi.org/10.1166/jmihi.2020.3119.
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Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data are collected to observe the effect of this model. The results show that the three-dimensional multi-scale linear filter has a good effect on carotid artery segmentation in the image segmentation of nerve cell membranes and carotid artery. When analyzing the accuracy of vascular image segmentation from network parameters and training parameters, it is found that the accuracy of the threedimensional multi-scale linear filter can reach about 85%. Therefore, it can be found that the combination of deep learning algorithm and image segmentation technology has a good segmentation effect, and the segmentation accuracy is also high. The experiment achieves the desired effect, which provides experimental basis for the clinical application of the vascular image segmentation technology.
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Kemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, Akshay Chaudhari, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, and Ender Konukoglu. "Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain." Magnetic Resonance Materials in Physics, Biology and Medicine 33, no. 4 (December 23, 2019): 483–93. http://dx.doi.org/10.1007/s10334-019-00816-5.
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Abstract Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (− 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (− 5.6 ± 7.6%, p < 0.001, effect size: 0.73). Discussion Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.
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Li, Wenbo, and Shuang Zhao. "Semantic segmentation of buildings in high-resolution remote sensing images based on DeepLabV3+ algorithm." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2400/1/012037.
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Abstract With the satellite remote sensing technology ushered in a leap of development, the resolution and clarity of satellite images have also been substantially improved, and high-resolution images depict the features more finely and provide more spectral information and texture contour information. The semantic segmentation of remote sensing image is one of the focuses of remote sensing technology research, which is very important for the development of remote sensing technology. To address the problems of imprecise target segmentation and low boundary segmentation accuracy in remote sensing image segmentation, a high-precision segmentation algorithm is proposed which based on DeepLabV3+. The algorithm optimizes the decoding region structure of the original network, adds the attention mechanism module, and improves the segmentation accuracy of remote sensing image.
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Hao, Li, Rong Mo, and Binbin Wei. "CAD Model Segmentation Algorithm Using the Fusion of PERT and Spectral Technology." Mathematical Problems in Engineering 2019 (September 25, 2019): 1–13. http://dx.doi.org/10.1155/2019/4395972.
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For complex CAD models, model segmentation technology is an important support for model retrieval and reuse. In this article, we first propose a novel CAD model segmentation method that uses the fusion of the program/project evaluation and review technique (PERT) and the Laplacian spectrum theory. By means of PERT, spectral theory, and the CAD models’ geometrical and topological information, we transform the b-rep model faces into two-dimensional coordinate points corresponding to the nodes of the attributed adjacent graph (AAG). The k-means approach with the Silhouette coefficient was employed to conduct unsupervised learning of the coordinate points. The experimental results demonstrate that (1) the proposed approach can effectively transform the b-rep model into a two-dimensional coordinate point set; (2) the k-means algorithm can efficiently cluster points to achieve segmentation; and (3) in view of human cognition, the segmentation results are more reasonable. It can effectively divide the point set into several groups to achieve the model segmentation.
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Zhao, Hai Jun. "A Video Image Segmentation Technology Based on Adaptive Thresholding Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 1189–92. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1189.
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Image segmentation is a key step in image processing and image analysis and occupies an important position in image engineering.In this paper, basing on maximum variance between-class, an adaptive and multi-objective image segmentation method is proposed. The concrete implement is to determine adaptively the optimum number of threshold of image using the idea of variance decomposition,while calculating the weighted ratio of within class difference and class difference existing in each classification image. By comparing the ratio, the optimum number of target for image can be get. The experimental results show that the sub-images after segmentation are relatively clear and the differences between classes are obvious.
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Xu, Min, and YouDong Ding. "Fully automatic image colorization based on semantic segmentation technology." PLOS ONE 16, no. 11 (November 30, 2021): e0259953. http://dx.doi.org/10.1371/journal.pone.0259953.
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Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge local features and global features, and the fusion results are input into semantic segmentation network and color prediction network respectively. Finally, the color prediction network obtains the semantic segmentation information of the image through the second fusion module, and predicts the chrominance of the image based on it. Through several sets of experiments, it is proved that the performance of our model becomes stronger and stronger under the nourishment of the data. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural.
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Mei, Shu-Li. "Construction of Target Controllable Image Segmentation Model Based on Homotopy Perturbation Technology." Abstract and Applied Analysis 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/131207.
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Based on the basic idea of the homotopy perturbation method which was proposed by Jihuan He, a target controllable image segmentation model and the corresponding multiscale wavelet numerical method are constructed. Using the novel model, we can get the only right object from the multiobject images, which is helpful to avoid the oversegmentation and insufficient segmentation. The solution of the variational model is the nonlinear PDEs deduced by the variational approach. So, the bottleneck of the variational model on image segmentation is the lower efficiency of the algorithm. Combining the multiscale wavelet interpolation operator and HPM, a semianalytical numerical method can be obtained, which can improve the computational efficiency and accuracy greatly. The numerical results on some images segmentation show that the novel model and the numerical method are effective and practical.
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Wu, Jin-Yuan, Guo-Dong You, Feng-Yuan Sun, Dong-Run Tang, and Tong Wu. "Image Segmentation Technology Application in Diabetic Retinopathy Analysis." Research Journal of Applied Sciences, Engineering and Technology 5, no. 9 (March 20, 2013): 2748–53. http://dx.doi.org/10.19026/rjaset.5.4801.
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Sasaki, Motoharu. "10. Automatic Contour Segmentation Technology in the Radiotherapy." Japanese Journal of Radiological Technology 77, no. 6 (2021): 591–95. http://dx.doi.org/10.6009/jjrt.2021_jsrt_77.6.591.
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Jizheng Chu, and Daguang Jiang. "Improved Scene Detection Technology in Video Frame Segmentation." International Journal of Digital Content Technology and its Applications 7, no. 8 (April 30, 2013): 92–100. http://dx.doi.org/10.4156/jdcta.vol7.issue8.11.
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Chen, Ying, Wenyuan Wang, Zhuang Zeng, and Yerong Wang. "An Adaptive CNNs Technology for Robust Iris Segmentation." IEEE Access 7 (2019): 64517–32. http://dx.doi.org/10.1109/access.2019.2917153.
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Chen, Weixiang, Jianfu Zhao, Zhenhui Dai, Mingyue Lv, Zhenhua Yang, and Yuqin Zhang. "Application of CT Image Technology Based on Nearest Neighbor Propagation Clustering Segmentation Algorithm in Lung Cancer Radiotherapy." Scientific Programming 2021 (December 3, 2021): 1–8. http://dx.doi.org/10.1155/2021/7289102.
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Objective. This paper uses the nearest neighbor propagation clustering segmentation algorithm to explore the impact of PET/CT image segmentation technology on lung cancer radiotherapy planning. Methods. In this paper, PET/CT scan was performed on 12 patients with nonmetastatic lung cancer. The self-written automatic segmentation program based on PCNN model is used to segment the PET target area, and then the tumor target area is manually sketched based on CT images and PET/CT images, and the intensity-modulated radiotherapy plan is formulated with the same parameters. Target volume and dose distribution were analyzed. Results. There was no statistical difference between the PET automatic segmentation target area and the PET manual contouring target area ( P < 0.05 ); the segmentation method was accurate and reliable; the difference between the CT manual contouring target area was statistically significant ( P 0.05 ). Conclusion. Based on the nearest neighbor propagation clustering segmentation algorithm, PET/CT image segmentation technology improves the accuracy of tumor target area delineation. The radiotherapy plan based on the segmentation target area can reduce the normal tissue exposure range and reduce the incidence of complications.
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Ma, Xiqi, Pengyu Zhang, Xiaofei Man, and Leming Ou. "A New Belt Ore Image Segmentation Method Based on the Convolutional Neural Network and the Image-Processing Technology." Minerals 10, no. 12 (December 11, 2020): 1115. http://dx.doi.org/10.3390/min10121115.
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In the field of mineral processing, an accurate image segmentation method is crucial for measuring the size distribution of run-of-mine ore on the conveyor belts in real time0The image-based measurement is considered to be real time, on-line, inexpensive, and non-intrusive. In this paper, a new belt ore image segmentation method was proposed based on a convolutional neural network and image processing technology. It consisted of a classification model and two segmentation algorithms. A total of 2880 images were collected as an original dataset from the process control system (PCS). The test images were processed using the proposed method, the PCS system, the coarse image segmentation (CIS) algorithm, and the fine image segmentation (FIS) algorithm, respectively. The segmentation results of each algorithm were compared with those of the manual segmentation. All empty belt images in the test images were accurately identified by our method. The maximum error between the segmentation results of our method and the results of manual segmentation is 5.61%. The proposed method can accurately identify the empty belt images and segment the coarse material images and mixed material images with high accuracy. Notably, it can be used as a brand new algorithm for belt ore image processing.
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Ma, Yushu, Yingzhe Gao, Zhaolin Li, Ang Li, Yi Wang, Jian Liu, Yao Yu, Wenbo Shi, and Zhenhe Ma. "Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering." Journal of Innovative Optical Health Sciences 14, no. 01 (January 2021): 2140011. http://dx.doi.org/10.1142/s1793545821400113.
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Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single image still fail to reach a satisfactory level. We propose a combination method of structure interpolation and lateral mean filtering (SI-LMF) to improve the signal-to-noise ratio based on one retinal image. Before performing one-dimensional lateral mean filtering to remove noise, structure interpolation was operated to eliminate thickness fluctuations. Then, we used boundary growth method to identify boundaries. Compared with existing segmentations, the method proposed in this paper requires less data and avoids the influence of microsaccade. The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects, which successfully identified the boundaries of 10 physiological layers, consistent with the results based on the manual determination.
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He, Bing Song, Feng Zhu, and Yong Gang Shi. "Medical Image Segmentation." Advanced Materials Research 760-762 (September 2013): 1590–93. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1590.
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Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms. In this paper, image segmentation algorithms are divided into classical image segmentation algorithms and segmentation methods combined with certain mathematical tools, including threshold segmentation methods, image segmentation algorithms based on the edge, image segmentation algorithms based on the region, image segmentation algorithms based on artificial neural network technology, image segmentation algorithms based on contour model and image segmentation algorithm based on statistical major segmentation algorithm and so on. Finally, the development trend of medical image segmentation algorithms is discussed.
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Xu, Lili, and Lilei Wen. "Application Research of Deep Learning Technology in Natural Landscape Animation Design." Journal of Sensors 2022 (April 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/8240900.
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Due to the limitation of technology and cost, the animation design of natural landscape in the past was often dealt with relative simplicity. With the increasing level of audience appreciation and the continuous development of animation technology, new requirements are put forward for the design of natural landscape animation. In order to make the animation design effect of natural landscape more real, the synthetic aperture radar image is firstly analyzed to obtain the location of mountains, farmland, rivers, villages, roads, and buildings. Considering the superiority of U-Net network in image semantic segmentation, this paper constructs a semantic segmentation model based on U-Net structure. In this model, dense connection module is introduced in downsampling, and spatial void pyramid structure is introduced in upsampling to retain more image features, to achieve accurate segmentation of satellite images. Experimental results show that the proposed algorithm has higher segmentation accuracy than other algorithms. After accurate classification of natural scene images, it can provide a guarantee for designing more real natural landscape animation design effects.
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An, Feng-Ping, and Zhi-Wen Liu. "Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN." Contrast Media & Molecular Imaging 2019 (August 1, 2019): 1–13. http://dx.doi.org/10.1155/2019/6134942.
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With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people’s time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.
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Pellicer-Valero, Oscar J., Victor Gonzalez-Perez, Juan Luis Casanova Ramón-Borja, Isabel Martín García, María Barrios Benito, Paula Pelechano Gómez, José Rubio-Briones, María José Rupérez, and José D. Martín-Guerrero. "Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks." Applied Sciences 11, no. 2 (January 18, 2021): 844. http://dx.doi.org/10.3390/app11020844.
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Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.
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Jiang, Xingjian, and Lei Wu. "Sports Video Image Segmentation Based on Fuzzy Clustering Algorithm." Scientific Programming 2022 (March 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/6882291.
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With the development of science and technology, people began to use video image segmentation technology to carry out various research works on sports, to expect effective athlete training effects. Fuzzy clustering algorithm can accurately and quickly extract the distorted data in the process of sports video image segmentation. In order to ensure that the motion curve is not easily affected by noise in the drawing process, this paper studies the sports video segmentation strategy. The fuzzy clustering algorithm can accurately and quickly extract the distortion data in the process of segmentation of sports video images. Therefore, the motion curve is not susceptible to noise during the drawing process, which can be ensured. After completing the above-mentioned sports video segmentation strategy completion experiment, the Release to Manufacturing (RTM) model is used to evaluate the experimental results of the sports video segmentation strategy. According to the RTM model test results, the result of the homogeneity test of variance is that since the result is much larger than 0.10, this can infer that the image quality obtained by the sports video segmentation experiment has reached the Spearman Rank Order Correlation Coefficient (SROCC) standard. Experiments verify the feasibility of applying fuzzy clustering algorithm and moving video image segmentation technology to the segmentation of human model moving video image, so as to obtain more accurate image data.
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Song, Ying Xiao, Ri Tu Wu, Li Xin He, and A. Mei Chen. "Application of Image Process Technology in Diagnosing the Fetal Brain Malformations." Applied Mechanics and Materials 433-435 (October 2013): 342–47. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.342.
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A new method was proposed to solve match and segmentation problem of ultrasonography of the fetus brain for screen the fetal brain malformations. First, obtaining the gray value of the brain of skull, lateral ventricle (LV), and cerebella hemisphere (CH) based on the image process. Then, index of the each parts gray value scope of the health fetus brain important regions were calculated by using the edge detection based random ellipse detection (RED), using the level set method for the segmentations in tested tissues. Mean values of all datasets were calculated and a standard model were established. This standard model can be used to match the gray level of the undiagnosed groups in order to screen the fetal brain malformations. The propose method gets encouraging result of the application in 3 fetuses with hydrocephalus.
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Du, Cheng, and Biao Leng. "Tunnel Face Image Segmentation Optimization." Applied Mechanics and Materials 397-400 (September 2013): 2148–51. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2148.
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With the development of Transportation Highway and railroad build, mining tunnel geological exploration in the road construction in the proportion of great. This paper presents a design of image processing software of Geological Engineering images for automatic analysis and processing. At present, the technology of image processing, most algorithms are based on the specific image information of specific analysis, and the face image is very complicated, different regions, and even the same construction sections in different areas of the face image may have very big difference. For the tunnel excavation face of digital image processing algorithms have little, need to start from scratch. This paper describes the use of digital image processing technology of Geological Engineering image image segmentation, found on the rock face, through the comparison of edge detection operator and Sobel Gauss - Laplasse operator methods advantages and disadvantages, a value of two images as the processing object image processing algorithm. The technology of Geological Engineering image analysis on tunnel construction period prediction plays a very important role.
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Lin, Xin, and RongChun Sun. "Refinement Bilateral Segmentation Network for Semantic Segmentation in Traffic Scenes." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2400/1/012014.
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Abstract Semantic segmentation technology in traffic scenes can help vehicles make accurate analyses and positioning of the road ahead. In traffic scenarios, the trade-off between real-time performance and the accuracy of semantic segmentation is particularly important. This paper proposes a lightweight deep convolutional network, which can be applied to traffic scenes to complete accurate semantic segmentation tasks, considering both real-time performance and accuracy. The Cross Channel Attention Fusion Mechanism proposed in this paper can better integrate the context information and improve accuracy. The Depth-wise Separable Pyramid Module proposed based on the feature pyramid idea can improve the segmentation accuracy and effectively trade off the real-time performance.
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Akimov, Dmytro. "Segmentation of art market in the fine art’ marketing." Collection of scientific works “Notes on Art Criticism”, no. 39 (September 1, 2021): 27–31. http://dx.doi.org/10.32461/2226-2180.39.2021.238676.
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The purpose of the article. Research and analysis of marketing technology algorithms by means of market segmentation in fine arts marketing. The methodology of the study is to apply comparative, empirical, and theoretical methods. This methodological approach allows us to analyze the processes of segmentation of the fine arts market with the subsequent use of research results in the marketing processes of promoting works of art from artist to consumer. The scientific novelty consists in expanding the notions about the technology of segmentation of the art market. The article analyzes the algorithms of marketing technologies in the segmentation of the fine arts market. It should be noted that in the marketing of fine arts the technologies of segmentation of art markets and technologies of the positioning of works of art are purposefully and productively used. The segmentation of art markets enables to highly efficient identify and systematize groups of consumers and admirers of works of fine art in accordance with their goals and motivations. The main purpose of the article: analysis of the specifics of the implementation of classical marketing technologies (marketing researches, segmentation of art markets) in the marketing of fine arts. Conclusions. The analysis of the problems of using traditional marketing technologies in art marketing carried out in the article gives grounds to state that such technologies are used in the art market, but they differ significantly from other market areas. It should be noted that the technology of market segmentation is actively used in the art market in works of museums, galleries, auctions. The technology of market segmentation allows regulating the processes of appearance and satisfaction of demand for works of art and their implementation.
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Wu, Zhenyu, Lin Wang, Yifei Li, Shuhui Dai, and Dongliang Zhang. "Head CT Image Segmentation and Three-Dimensional Reconstruction Technology Based on Human Anatomy." Computational Intelligence and Neuroscience 2022 (June 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/7091476.
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With the continuous development of computer science and technology, the level of medical image processing and analysis technology has been significantly improved. In order to further optimize the medical imaging technology and provide assistance for medical diagnosis and treatment, this study will explore the head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy, using two morphological operation methods of image expansion and image corrosion, as well as the triangulation method based on surface contour, Optimize CT image segmentation technology and three-dimensional reconstruction technology. The results show that the CT image segmentation technology based on human anatomy can obtain the more essential morphology and features of the target image, and significantly improve the image quality. The size of the threshold can have a certain impact on the 3D reconstruction effect and reconstruction time to a certain extent. The larger the threshold, the shorter the reconstruction time, but the worse the 3D reconstruction effect. This shows that the target image after fitting has a good reconstruction effect, but the threshold level should be kept at a low level. The head CT image segmentation technology and three-dimensional reconstruction technology based on human anatomy have good application effects and can be popularized and applied in clinical diagnosis and treatment.
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Chen, Hui Gang, and Ya Li Mi. "Application of Computer Digital Technology in Orthopedic Treatment." Applied Mechanics and Materials 651-653 (September 2014): 376–79. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.376.
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The application of computer digital technology in orthopedic treatment is studied. In the field of orthopedic therapy, computer digital technology need to be applied to process of collecting the lesion area to provide the basis for clinical diagnosis and treatment. This paper presents an orthopedic lesion region image segmentation method based on linear analysis algorithm, this method can be adopted to segment orthopedic lesion region effectively, precise location and lesion extent of lesion area obtained to provide data to support the clinical diagnosis. Experimental results show that orthopedic lesion area image segmentation method based on linear analysis algorithm applied to the process of orthopedic treatment, can improve the cure rate efficiently.
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Jin, Felix Q., Anna E. Knight, Adela R. Cardones, Kathryn R. Nightingale, and Mark L. Palmeri. "Semi-automated weak annotation for deep neural network skin thickness measurement." Ultrasonic Imaging 43, no. 4 (May 11, 2021): 167–74. http://dx.doi.org/10.1177/01617346211014138.
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Correctly calculating skin stiffness with ultrasound shear wave elastography techniques requires an accurate measurement of skin thickness. We developed and compared two algorithms, a thresholding method and a deep learning method, to measure skin thickness on ultrasound images. Here, we also present a framework for weakly annotating an unlabeled dataset in a time-effective manner to train the deep neural network. Segmentation labels for training were proposed using the thresholding method and validated with visual inspection by a human expert reader. We reduced decision ambiguity by only inspecting segmentations at the center A-line. This weak annotation approach facilitated validation of over 1000 segmentation labels in 2 hours. A lightweight deep neural network that segments entire 2D images was designed and trained on this weakly-labeled dataset. Averaged over six folds of cross-validation, segmentation accuracy was 57% for the thresholding method and 78% for the neural network. In particular, the network was better at finding the distal skin margin, which is the primary challenge for skin segmentation. Both algorithms have been made publicly available to aid future applications in skin characterization and elastography.
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Zhang, Lianhua, Zhiying Jia, Xiaoling Leng, and Fucheng Ma. "Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/8830260.
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This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.
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Kintonova, Aliya, Igor Povkhan, Marzhan Mussaif, and Galymzhan Gabdreshov. "Improvement of iris recognition technology for biometric identification of a person." Eastern-European Journal of Enterprise Technologies 6, no. 2 (120) (December 30, 2022): 60–69. http://dx.doi.org/10.15587/1729-4061.2022.269948.
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This topic is very relevant in the field of artificial intelligence as a direction of pattern recognition. In this work, the iris of the eye is considered as an image. Artificial intelligence makes this technology more accessible for use in CCTV cameras, smartphones and various areas of human activity. The article reflects the results of a study of methods and technologies of pattern recognition on the example of the human iris. The aim of the work was to study methods and technologies for human iris recognition and iris recognition of employees of a particular organization using EyeLock equipment by comparing segmentation results with Daugman standard segmentation. Comparison analysis of segmentation results with standard segmentation can be done by directly measuring the number of correctly segmented irises in both methods, or by indirectly measuring the effect of segmentation on iris recognition performance. The method using the Daugman integral-differential operator has the greatest efficiency. The performance of the neural network has been improved. To use a neural network to classify iris profiles, we selected sets of images (images per person) as training images, and the rest of the images were used as test images. Training time (in seconds): for the Daugman method 170.7, and for the parabolic method 204.7. The Daugman integro-differential operator is applied to the captured image to obtain the "maximum integral derivative of the contour" with ever-increasing radius on "successively decreasing scales" in three parameters: center coordinates and radius. Finding the maximum when the search coordinates deviate along an unwinding spiral. Methods and techniques for pattern recognition have been investigated using the human iris
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Xu, Lin, Qin Zhang, Dan Dong Wang, and Jian Zhang. "Research of Chinese Segmentation Based on MMSeg and Double Array TRIE." Advanced Materials Research 225-226 (April 2011): 945–48. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.945.
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Chinese segmentation system is a difficulty in computer Chinese information handling. A deep discussion on methods of MMSeg and double array TRIE Chinese segmentation matching on the basis of existing technology in Chinese segmentation is made. On this basis of it , some im2provements are made in the dictionary construction and segmentation arithmetic , designing a Chinese segmentation system based on MMSeg and double array TRIE. Experiment shows that the improving arithmetic accelerated the speed of Chinese segmentation.
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Chen, Xiang, and Jian Min Wang. "Image Segmentation Approaches of Based on Fractals." Advanced Materials Research 341-342 (September 2011): 773–75. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.773.
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Image segmentation is the key steps of image processing and analysis. In image processing process, first, we need make the image into a number of significant areas and the interested objects; this need use image segmentation technology. This paper summarizes the methods of image segmentation. Mainly the image segmentation approach of based on fractal is analyzed, and this method has received good result of image segmentation, it proved that fractals-based method of image segmentation is an effective way.