Relevant bibliographies by topics / Segmentation algorithms assessment

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Academic literature on the topic 'Segmentation algorithms assessment'

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Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Segmentation algorithms assessment"

Barboriak, Daniel, Katy Peters, Allan Friedman, Henry Friedman, and Annick Desjardins. "NEIM-03. FEASIBILITY OF AUTOMATED ASSESSMENT OF PROGRESSIVE ENHANCEMENT ON MRI IN PATIENTS WITH NEWLY DIAGNOSED HIGH-GRADE GLIOMA USING A FEATURE-BASED ALGORITHM." Neuro-Oncology Advances 3, Supplement_4 (2021): iv7. http://dx.doi.org/10.1093/noajnl/vdab112.024.

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Abstract BACKGROUND Approximately 50% of patients with newly diagnosed high-grade glioma (HGG) develop progressive enhancement between their post-operative MRI scan and 12 weeks after radiation and temozolomide. Inter-reader variability on the assessment of progressive enhancement in this patient group is a significant barrier in designing multi-center biomarker trials to distinguish true progression from pseudoprogression. Although enhancement segmentation algorithms have become more widely available, more automated and reproducible techniques to identify patients who develop progressive enhancement are needed to facilitate acquisition of non-standard of care biomarkers when this occurs. We explored the feasibility of using a feature-based algorithm in tandem with freely available / open source automated segmentation algorithms to identify this subset of patients. METHODS An automated algorithm using subtraction of registered segmentations to detect new areas of localized thickness of enhancement was developed. Criteria for feasibility (50% within 95% CI of percent patients identified, and sensitivity of &gt;85% of patients assessed as progressed [P+] identified) were determined prospectively. The algorithm was implemented across five different automated enhancement segmentation techniques, then evaluated using a retrospective dataset of 73 patients with newly diagnosed HGG (age 50.8±13.2 years, 37 men, 36 women, 50 GBM, 23 Grade III). Standardized post-baseline brain tumor imaging protocol MR acquisitions were obtained on 1.5T and 3T scanners (GE and Siemens). On chart review, 53% of patients were assessed by neuroradiologists and/or neuro-oncologists as P+ (progression vs. pseudoprogression). RESULTS 50% was within the 95% CI of percent of patients identified for all five segmentation algorithms. Sensitivity was over 85% for three segmentation algorithms, with the MIC-DKFZ algorithm having highest sensitivity of 92%. For this algorithm, specificity was 77%, PPV was 81% and NPV was 90%. CONCLUSION A feature-based algorithm in tandem with open source segmentation algorithms showed preliminary feasibility for automated identification of patients with progressive enhancement.

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De Kerf, Thomas, Navid Hasheminejad, Johan Blom, and Steve Vanlanduit. "Qualitative Comparison of 2D and 3D Atmospheric Corrosion Detection Methods." Materials 14, no. 13 (2021): 3621. http://dx.doi.org/10.3390/ma14133621.

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In this article, we report the use of a Confocal Laser Scanning Microscope (CLSM) to apply a qualitative assessment of atmospheric corrosion on steel samples. From the CLSM, we obtain high-resolution images, together with a 3D heightmap. The performance of four different segmentation algorithms that use the high-resolution images as input is qualitatively assessed and discussed. A novel 3D segmentation algorithm based on the shape index is presented and compared to the 2D segmentation algorithms. From this analysis, we conclude that there is a significant difference in performance between the 2D segmentation algorithms and that the 3D method can be an added value to the detection of corrosion.

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Węgliński, Tomasz, and Anna Fabijańska. "Survey of Modern Image Segmentation Algorithms on CT Scans of Hydrocephalic Brains." Image Processing & Communications 17, no. 4 (2012): 223–30. http://dx.doi.org/10.2478/v10248-012-0050-y.

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Abstract Paper presents the concept of applying image segmentation algorithms for precise extraction of cerebrospinal fluid (CSF) from CT brain scans. Accurate segmentation of the CSF from the intracranial brain area is crucial for further reliable analysis and quantitative assessment of hydrocephalus. Presented research was aimed at the comparison of effectiveness of three modern segmentation approaches used for this purpose. Specifically, random walk, level set and min-cut/max-flow algorithms were considered. The visual and numerical comparison of the segmentation results leads to conclusion that the most effective algorithm for the considered problem is level set, although the positive medical verification of the results revealed that either of considered algorithms can be successfully applied in the diagnostic applications.

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Warfield, Simon K., Kelly H. Zou, and William M. Wells. "Validation of image segmentation by estimating rater bias and variance." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1874 (2008): 2361–75. http://dx.doi.org/10.1098/rsta.2008.0040.

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The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a ‘ground truth’ or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically, these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labelling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, among others, surface, distance transform or level-set representations of segmentations, and can be used to assess whether or not a rater consistently overestimates or underestimates the position of a boundary.

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Sudarvizhi, D., and M. Akila. "Wound Assessment in Pedobarography Using Image Segmentation Techniques." Journal of Medical Imaging and Health Informatics 11, no. 5 (2021): 1403–9. http://dx.doi.org/10.1166/jmihi.2021.3657.

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Pedobarography is elementary for kinetic gait analysis along with the analysis and exploration of multiple neurological and musculoskeletal diseases. One person among 11 adults suffer from Diabetes Mellitus. Also, Foot ulcers (FU) is a most harmful as well as associated chronic complications springing from diabetes mellitus (DM). Recently, there has been an evolving awareness that understanding the biomechanical factors beneath the diabetic ulcer in a better manner could result in improving the control activities over the disease, with considerable socio-economic effects. Diabetic Foot Ulcers (DFU) is a primary concern of this health issue, and if this is not addressed right can result in amputation. So in this research, the Image segmentation algorithms and Perimeter pixel comparison is carried out for wound classification depending on the simulation algorithm like Adaptive K-means, Clustering K means, Fuzzy C means, and Region growing approaches and among them, Fuzzy C means is found to achieve greatest accuracy of perimeter pixel values, which are 603, 462 and 356 pixel values in stages one, two and three. The time taken for execution among all the four simulation algorithms are observed and it can be revealed that Adaptive K means yields the least execution time for carrying out the simulation of foot ulcer. An evaluation on the self-assessment of wounds caused during diabetic foot ulcer employing image segmentation is developed. It is ultimately found that the objective of the image analysis pertaining to the ulcer in foot is the dynamic evaluation and definition of regions of high pressure in a diabetic patient’s foot depending on the estimations made on the perimeter pixel comparison and execution time.

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Liu, Hongbing, Xiaoyu Diao, and Huaping Guo. "Quantitative analysis for image segmentation by granular computing clustering from the view of set." Journal of Algorithms & Computational Technology 13 (January 2019): 174830181983305. http://dx.doi.org/10.1177/1748301819833050.

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As partition method of set, granular computing clustering is applied to image segmentation evaluated by global consistency error, variation of Information, and Rand index from the view of set. Firstly, quantitative assessment of clustering is evaluated from the view of set. Secondly, granular computing clustering algorithms are induced by the distance formulas, the granules with different shapes are defined as the forms of vectors by different distance norms, especially, the atomic granule is induced by a point of space, the union operator realizes the transformation between two granule spaces and is used to form granular computing clustering algorithms. Thirdly, the image segmentations by granular computing clustering are evaluated from the view of set, such as global consistency error, variation of Information, and Rand index. Segmentations of the color images selected from BSD300 are used to show the superiority and feasibility for image segmentation by granular computing clustering compared with Kmeans and fuzzy c-means by experiments.

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Ghattas, Andrew E., Reinhard R. Beichel, and Brian J. Smith. "A unified framework for simultaneous assessment of accuracy, between-, and within-reader variability of image segmentations." Statistical Methods in Medical Research 29, no. 11 (2020): 3135–52. http://dx.doi.org/10.1177/0962280220920894.

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Medical imaging is utilized in a wide range of clinical applications. To enable a detailed quantitative analysis, medical images must often be segmented to label (delineate) structures of interest; for example, a tumor. Frequently, manual segmentation is utilized in clinical practice (e.g., in radiation oncology) to define such structures of interest. However, it can be quite time consuming and subject to substantial between-, and within-reader variability. A more reproducible, less variable, and more time efficient segmentation approach is likely to improve medical treatment. This potential has spurred the development of segmentation algorithms which harness computational power. Segmentation algorithms’ widespread use is limited due to difficulty in quantifying their performance relative to manual segmentation, which itself is subject to variation. This paper presents a statistical model which simultaneously estimates segmentation method accuracy, and between- and within-reader variability. The model is simultaneously fit for multiple segmentation methods within a unified Bayesian framework. The Bayesian model is compared to other methods used in literature via a simulation study, and application to head and neck cancer PET/CT data. The modeling framework is flexible and can be employed in numerous comparison applications. Several alternate applications are discussed in the paper.

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Ramot, Yuval, Gil Zandani, Zecharia Madar, Sanket Deshmukh, and Abraham Nyska. "Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice." Toxicologic Pathology 48, no. 5 (2020): 702–7. http://dx.doi.org/10.1177/0192623320926478.

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Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditional semiquantitative pathology assessment can be enhanced with artificial intelligence (AI) algorithms. Using glass slides of mice liver as a model for nonalcoholic fatty liver disease, a deep learning AI algorithm was developed. This algorithm uses a segmentation framework for vacuole quantification and can be deployed to analyze live histopathology fields during the microscope-based pathology assessment. We compared the manual semiquantitative microscope-based assessment with the quantitative output of the deep learning algorithm. The deep learning algorithm was able to recognize and quantify the percent of fatty vacuoles, exhibiting a strong and significant correlation ( r = 0.87, P < .001) between the semiquantitative and quantitative assessment methods. The use of deep learning algorithms for difficult quantifications within the microscope-based pathology assessment can help improve outputs of toxicologic pathology workflows.

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Sun, H., Y. Ding, Y. Huang, and G. Wang. "CRITICAL ASSESSMENT OF OBJECT SEGMENTATION IN AERIAL IMAGE USING GEO-HAUSDORFF DISTANCE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 187–94. http://dx.doi.org/10.5194/isprsarchives-xli-b4-187-2016.

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Aerial Image records the large-range earth objects with the ever-improving spatial and radiometric resolution. It becomes a powerful tool for earth observation, land-coverage survey, geographical census, etc., and helps delineating the boundary of different kinds of objects on the earth both manually and automatically. In light of the geo-spatial correspondence between the pixel locations of aerial image and the spatial coordinates of ground objects, there is an increasing need of super-pixel segmentation and high-accuracy positioning of objects in aerial image. Besides the commercial software package of eCognition and ENVI, many algorithms have also been developed in the literature to segment objects of aerial images. But how to evaluate the segmentation results remains a challenge, especially in the context of the geo-spatial correspondence. The Geo-Hausdorff Distance (GHD) is proposed to measure the geo-spatial distance between the results of various object segmentation that can be done with the manual ground truth or with the automatic algorithms.Based on the early-breaking and random-sampling design, the GHD calculates the geographical Hausdorff distance with nearly-linear complexity. Segmentation results of several state-of-the-art algorithms, including those of the commercial packages, are evaluated with a diverse set of aerial images. They have different signal-to-noise ratio around the object boundaries and are hard to trace correctly even for human operators. The GHD value is analyzed to comprehensively measure the suitability of different object segmentation methods for aerial images of different spatial resolution. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for extensive research in automating object detection and classification of aerial image in the nation-wide geographic census. It is also promising for the optimal design of operational specification of remote sensing interpretation under the constraints of limited resource.

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Dissertations / Theses on the topic "Segmentation algorithms assessment"

Yang, Huiqi. "Development of computer-based algorithms for unsupervised assessment of radiotherapy contouring." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/284463.

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INTRODUCTION: Despite the advances in radiotherapy treatment delivery, target volume delineation remains one of the greatest sources of error in the radiotherapy delivery process, which can lead to poor tumour control probability and impact clinical outcome. Contouring assessments are performed to ensure high quality of target volume definition in clinical trials but this can be subjective and labour-intensive. This project addresses the hypothesis that computational segmentation techniques, with a given prior, can be used to develop an image-based tumour delineation process for contour assessments. This thesis focuses on the exploration of the segmentation techniques to develop an automated method for generating reference delineations in the setting of advanced lung cancer. The novelty of this project is in the use of the initial clinician outline as a prior for image segmentation. METHODS: Automated segmentation processes were developed for stage II and III non-small cell lung cancer using the IDEAL-CRT clinical trial dataset. Marker-controlled watershed segmentation, two active contour approaches (edge- and region-based) and graph-cut applied on superpixels were explored. k-nearest neighbour (k-NN) classification of tumour from normal tissues based on texture features was also investigated. RESULTS: 63 cases were used for development and training. Segmentation and classification performance were evaluated on an independent test set of 16 cases. Edge-based active contour segmentation achieved highest Dice similarity coefficient of 0.80 ± 0.06, followed by graphcut at 0.76 ± 0.06, watershed at 0.72 ± 0.08 and region-based active contour at 0.71 ± 0.07, with mean computational times of 192 ± 102 sec, 834 ± 438 sec, 21 ± 5 sec and 45 ± 18 sec per case respectively. Errors in accuracy of irregularly shaped lesions and segmentation leakages at the mediastinum were observed. In the distinction of tumour and non-tumour regions, misclassification errors of 14.5% and 15.5% were achieved using 16- and 8-pixel regions of interest (ROIs) respectively. Higher misclassification errors of 24.7% and 26.9% for 16- and 8-pixel ROIs were obtained in the analysis of the tumour boundary. CONCLUSIONS: Conventional image-based segmentation techniques with the application of priors are useful in automatic segmentation of tumours, although further developments are required to improve their performance. Texture classification can be useful in distinguishing tumour from non-tumour tissue, but the segmentation task at the tumour boundary is more difficult. Future work with deep-learning segmentation approaches need to be explored.

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Youmaran, Richard. "Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images." Thesis, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19729.

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Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

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Fernandez, Mariela Atausinchi. "Classificação de imagens de plâncton usando múltiplas segmentações." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-29052017-141908/.

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Plâncton são organismos microscópicos que constituem a base da cadeia alimentar de ecossistemas aquáticos. Eles têm importante papel no ciclo do carbono pois são os responsáveis pela absorção do carbono na superfície dos oceanos. Detectar, estimar e monitorar a distribuição das diferentes espécies são atividades importantes para se compreender o papel do plâncton e as consequências decorrentes de alterações em seu ambiente. Parte dos estudos deste tipo é baseada no uso de técnicas de imageamento de volumes de água. Devido à grande quantidade de imagens que são geradas, métodos computacionais para auxiliar no processo de análise das imagens estão sob demanda. Neste trabalho abordamos o problema de identificação da espécie. Adotamos o pipeline convencional que consiste dos passos de detecção de alvo, segmentação (delineação de contorno), extração de características, e classificação. Na primeira parte deste trabalho abordamos o problema de escolha de um algoritmo de segmentação adequado. Uma vez que a avaliação de resultados de segmentação é subjetiva e demorada, propomos um método para avaliar algoritmos de segmentação por meio da avaliação da classificação no final do pipeline. Experimentos com esse método mostraram que algoritmos de segmentação distintos podem ser adequados para a identificação de espécies de classes distintas. Portanto, na segunda parte do trabalho propomos um método de classificação que leva em consideração múltiplas segmentações. Especificamente, múltiplas segmentações são calculadas e classificadores são treinados individualmente para cada segmentação, os quais são então combinados para construir o classificador final. Resultados experimentais mostram que a acurácia obtida com a combinação de classificadores é superior em mais de 2% à acurácia obtida com classificadores usando uma segmentação fixa. Os métodos propostos podem ser úteis para a construção de sistemas de identificação de plâncton que sejam capazes de se ajustar rapidamente às mudanças nas características das imagens.<br>Plankton are microscopic organisms that constitute the basis of the food chain of aquatic ecosystems. They have an important role in the carbon cycle as they are responsible for the absorption of carbon in the ocean surfaces. Detecting, estimating and monitoring the distribution of plankton species are important activities for understanding the role of plankton and the consequences of changes in their environment. Part of these type of studies is based on the analysis of water volumes by means of imaging techniques. Due to the large quantity of generated images, computational methods for helping the process of image analysis are in demand. In this work we address the problem of species identification. We follow the conventional pipeline consisting of target detection, segmentation (contour delineation), feature extraction, and classification steps. In the first part of this work we address the problem of choosing an appropriate segmentation algorithm. Since evaluating segmentation results is a subjective and time consuming task, we propose a method to evaluate segmentation algorithms by evaluating the classification results at the end of the pipeline. Experiments with this method showed that distinct segmentation algorithms might be appropriate for identifying species of distinct classes. Therefore, in the second part of this work we propose a classification method that takes into consideration multiple segmentations. Specifically, multiple segmentations are computed and classifiers are trained individually for each segmentation, which are then combined to build the final classifier. Experimental results show that the accuracy obtained with the combined classifier is superior in more than 2% to the accuracy obtained with classifiers using a fixed segmentation. The proposed methods can be useful to build plankton identification systems that are able to quickly adjust to changes in the characteristics of the images.

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Book chapters on the topic "Segmentation algorithms assessment"

Boot, Thomas, and Humayun Irshad. "Diagnostic Assessment of Deep Learning Algorithms for Detection and Segmentation of Lesion in Mammographic Images." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59719-1_6.

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Senthilnath, J., H. Vikram Shenoy, S. N. Omkar, and V. Mani. "Spectral-Spatial MODIS Image Analysis Using Swarm Intelligence Algorithms and Region Based Segmentation for Flood Assessment." In Advances in Intelligent Systems and Computing. Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-1041-2_14.

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Nurhudatiana, Arfika. "A Computer-Aided Diagnosis System for Vitiligo Assessment: A Segmentation Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46742-8_30.

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Luo, Jiamin, Alex Noel Joseph Raj, Nersisson Ruban, and Vijayalakshmi G. V. Mahesh. "Segmentation of Optic Disc From Fundus Image Based on Morphology and SVM Classifier." In Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6690-9.ch007.

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Color fundus image is the most basic way to diagnose diabetic retinopathy, papillary edema, and glaucoma. In particular, since observing the morphological changes of the optic disc is conducive to the diagnosis of related diseases, accurate and effective positioning and segmentation of the optic disc is an important process. Optic disc segmentation algorithms are mainly based on template matching, deformable model and learning. According to the character that the shape of the optic disc is approximately circular, this proposed research work uses Kirsch operator to get the edge of the green channel fundus image through morphological operation, and then detects the optic disc by HOUGH circle transformation. In addition, supervised learning in machine learning is also applied in this chapter. First, the vascular mask is obtained by morphological operation for vascular erasure, and then the SVM classifier is segmented by HU moment invariant feature and gray level feature. The test results on the DRIONS fundus image database with expert-labeled optic disc contour show that the two methods have good results and high accuracy in optic disc segmentation. Even though seven different assessment parameters (sensitivity [Se], specificity [Sp], accuracy [Acc], positive predicted value [Ppv], and negative predicted value [Npv]) are used for performance assessment of the algorithm. Accuracy is considered as the criterion of judgment in this chapter. The average accuracy achieved for the nine random test set is 97.7%, which is better than any other classifiers used for segmenting Optical Disc from Fundus Images.

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Sánchez-Fernández, María Dolores, Daniel Álvarez Bassi, and José Ramón Cardona. "Residents' Attitudes in Punta del Este (Uruguay)." In Emerging Economic Models for Global Sustainability and Social Development. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5787-6.ch015.

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Homogeneity studies in recent decades have segmented residents according to their attitudes. The aim of this work is to segment the residents of Punta del Este according to their attitudes toward tourism. Recently, there have been some segmentations of residents in diver's tourist destinations in the world. Resident segmentation has been performed with a cluster analysis using the K-mean algorithm, generating three groups: enthusiastic supporters (33.1%), supporters with nuanced opinion (45.2%), and people without a formed opinion (21.7%). The profile of the groups generated is quite similar and no groups with a clear opposition to tourism have been detected. The overall assessment of the residents surveyed in this research is positive.

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Pauline, Ong, and Zarita Zainuddin. "A Combinational Fuzzy Clustering Approach for Microarray Spot Segmentation." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1776-4.ch012.

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Due to microarray experiment imperfection, spots with various artifacts are often found in microarray image. A more rigorous spot recognition approach in ensuring successful image analysis is crucial. In this paper, a novel hybrid algorithm was proposed. A wavelet approach was applied, along with an intensity-based shape detection simultaneously to locate the contour of the microarray spots. The proposed algorithm segmented all the imperfect spots accurately. Performance assessment with the classical methods, i.e., the fixed circle, adaptive circle, adaptive shape and histogram segmentation showed that the proposed hybrid approach outperformed these methods.

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Swarnalatha, A., K. Palani Thanaraj, A. Sheryl Oliver, and M. Esther Hannah. "A Study on the Examination of RGB Scale Retinal Pictures Using Recent Methodologies." In Biomedical and Clinical Engineering for Healthcare Advancement. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0326-3.ch010.

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Retinal disease/condition examination is one of the significant areas of the medical field. A variety of retinal abnormality assessments based on fundus image-assisted trials are widely proposed by the researchers to examine the parts of the retina. Recently, traditional and soft computing-based approaches are executed to inspect the optic disc and the blood vessels of the retina to discover disease/damages. This work implements (i) A two-phase methodology based on Jaya Algorithm (JA) and Kapur's Entropy (KE) thresholding and level-set segmentation for the optic disc evaluation and (ii) JA-based Multi-scale Matched Filter (MMF) for the blood vessel assessment. During this analysis, various benchmark datasets such as RIM-ONE, DRIVE, and STARE are considered. The experimental study substantiates that JA-assisted retinal picture examination offers better results than other related existing methodologies.

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Lazli, Lilia, and Mounir Boukadoum. "Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization." In Research Anthology on Diagnosing and Treating Neurocognitive Disorders. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3441-0.ch015.

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Segmentation is a key step in brain imaging where clustering techniques are widely used, particularly the fuzzy approach which offers active and robust methods against noise and partial volume effect (PVE). To address those imperfections, this article suggests an automatic segmentation of brain tissues for magnetic resonance and functional images of Alzheimer's patients, based on an efficient and robust genetic-fuzzy-possibilistic clustering scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes. The proposed hybrid clustering process based on: 1) A fuzzy possibilistic c-means algorithm that models the degree of relationship between each voxel and a given tissue. 2) A fuzzy c-means algorithm to initialize the clusters centers, with subsequent optimization by a genetic algorithm. Each stage of the proposed clustering process is validated on real brain data and synthetic images of an Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom. A performance comparison is made with the usual fuzzy techniques. The visual and quantitative results obtained with the proposed approach using various signal-to-noise ratios prove its effectiveness to quantify the tissue volume of images of different modalities types in the presence of noise and PVE. The effectiveness in terms of computational rate is also demonstrated.

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Manic, Kesavan Suresh, Imad Saud Al Naimi, Feras N. Hasoon, and V. Rajinikanth. "Jaya Algorithm-Assisted Evaluation of Tooth Elements Using Digital Bitewing Radiography Images." In Computational Techniques for Dental Image Analysis. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6243-6.ch005.

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A considerable number of heuristic procedures are widely implemented to evaluate biomedical images. This chapter proposes an evaluation procedure for digital bitewing radiography (DBR) images using the Jaya algorithm. The proposed procedure implements an image processing technique by integrating of the multi-thresholding and segmentation procedure to extract the essential tooth elements recorded with DBR. In this paper, 80 dental x-ray images are considered for the evaluation. The performance of the proposed procedure is confirmed using a relative assessment between the extracted section and its corresponding ground-truth. The results of this study confirm that, for most of the DBR cases, the proposed approach offers better values of picture likeliness measures. Hence, this technique can be considered for the automated detection of tooth elements from the DBR obtained from clinics.

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Conference papers on the topic "Segmentation algorithms assessment"

Syed Zaini, Syed Zakwan, Nur Najihah Sofia, Mohd Marzuki, et al. "Image Quality Assessment for Image Segmentation Algorithms: Qualitative and Quantitative Analyses." In 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2019. http://dx.doi.org/10.1109/iccsce47578.2019.9068561.

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Brehler, Michael, Qian Cao, Kendall F. Moseley, et al. "Robust quantitative assessment of trabecular microarchitecture in extremity cone-beam CT using optimized segmentation algorithms." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor Gimi and Andrzej Krol. SPIE, 2018. http://dx.doi.org/10.1117/12.2293346.

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Wijayarathne, Lasitha, and Frank L. Hammond. "Kinetic Measurement Platform for Open Surgical Skill Assessment." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3525.

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Current surgical skill assessment methods are often based on the kinematics of manual surgical instruments during tool-tissue interactions. Though kinematic data are generally regarded as a sufficient basis for skill assessment, the inclusion of kinetic information would allow the assessment of measures such as “respect for tissue” and force control, which are also important aspects of surgical proficiency. Kinetic data would also provide a richer data set upon which automated surgical motion segmentation and classification algorithms can be developed. However, the kinetics of tool-tissue interactions are seldom included in assessments, due largely to the difficulty of mounting small sensors — typically silicon strain gauges — onto surgical instruments to capture force data. Electromagnetic (EM) or optical trackers used for kinematic measurement are often tethered, and thus having tethered force sensors also mounted on the same surgical instruments would complicate the experimental process and could affect/distort the acquired data by impeding the natural manual motions of surgeons. We present a surgical skill assessment platform which places the kinetic sensors in the environment, not on the instruments, to reduce the physical encumbrance of the system to the surgeon. This system can capture kinetic data using a standalone force/torque sensor embedded in a custom designed workspace platform, and kinematic data using EM trackers placed on the instruments. This portable platform enables the empirical characterization of open surgery motion trajectories and corresponding kinetic data without need for a centralized acquisition site, and will eventually be integrated into a completely untethered skill assessment system.

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Yan, Xiyu, Yong Jiang, Shuai Chen, et al. "Automatic Grassland Degradation Estimation Using Deep Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/835.

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Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.

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Ma, Jungmok, and Harrison M. Kim. "Predictive Usage Mining for Sustainability of Complex Systems Design." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34755.

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A new perspective of dynamic LCA (life cycle assessment) is proposed with the predictive usage mining for sustainability (PUMS) algorithm. By defining usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon. Large-scale sensor data of product operation is analyzed in order to mine usage patterns and build a usage model for LCA. The PUMS algorithm consists of handling missing and abnormal values, seasonal period analysis, segmentation analysis, time series analysis, and predictive LCA. A newly developed segmentation algorithm can distinguish low activity periods and help to capture patterns more clearly. Furthermore, a predictive LCA method is formulated using a time series usage model. Finally, generated data is used to do predictive LCA of agricultural machinery as a case study.

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Meister, Sebastian, Mahdieu Amin Mahdieu Wermes, Jan Stueve, and Roger M. Groves. "Algorithm assessment for layup defect segmentation from laser line scan sensor based image data." In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, edited by Daniele Zonta and Haiying Huang. SPIE, 2020. http://dx.doi.org/10.1117/12.2558434.

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Babu, K. S., Kumar Y. B. Ravi, and Sukanta Sabut. "An improved watershed segmentation by flooding and pruning algorithm for assessment of diabetic wound healing." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256683.

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Kumar, S., and F. Taheri. "Neuro-Fuzzy Approaches for FRP Oil and Gas Pipeline Condition Assessment." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-3080.

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Recent advances in ultrasonic, optical and piezoelectric sensors, and computing technologies have led to the development of inspection systems for underground and off-shore structures such as water lines, oil and gas pipes, and telecommunication conduits. It is now possible to use inspection technologies that require no human intervention (i.e., having had to go underground or off-shore); moreover, the inspection process can be fully automated, from data acquisition to data analysis, and eventually to condition assessment and repair. This paper describes the development of an automated data interpretation system for fiber-reinforced polymer composites (FRP) oil and gas pipelines, which would also be applicable to metallic pipes. The interpretation system obtains C-scan image data from so-called “smart pigs” and maps data using Geographic Information System (GIS) and Global Positioning System (GPS). Assessment of health of pipelines using neural networks is then performed to identify the high-risk locations in each pipeline or pipeline network, thus allowing the inspection to be properly targeted. The proposed system utilizes artificial neural networks and genetic algorithm to recognize various types of defects in FRP oil and gas pipelines. Image processing and wavelets techniques are used to find the detail of the damage geometry. An expert system is also developed, using fuzzy Logic, to perform damage condition assessment and suggest an optimum repair protocol. The framework of the developed system, thus includes GIS, risk map, modification of digital images for preprocessing, image feature segmentation, utilization of multiple neural networks for feature pattern recognition, the fusion of multiple neural networks via the use of fuzzy logic systems, and the proposed expert system for suggested repair.

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Farmaki, Cristina, Kostas Mavrigiannakis, Kostas Marias, Michalis Zervakis, and Vangelis Sakkalis. "Assessment of automated brain structures segmentation based on the mean-shift algorithm: Application in brain tumor." In 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010). IEEE, 2010. http://dx.doi.org/10.1109/itab.2010.5687634.

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Padmavathi, G., M. Muthukumar, and Suresh Kumar Thakur. "Implementation and Comparison of different segmentation algorithms used for underwater images based on nonlinear objective assessments." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579301.

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