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Journal articles on the topic 'Breast Segmentation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Rampun, Andrik, Philip J. Morrow, Bryan W. Scotney, and John Winder. "Fully automated breast boundary and pectoral muscle segmentation in mammograms." Artificial Intelligence in Medicine Vol. 79 (June 9, 2017): 28–41. https://doi.org/10.1016/j.artmed.2017.06.001.

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Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in Computer Aided Diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to sear

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Jiao, Han, Xinhua Jiang, Zhiyong Pang, Xiaofeng Lin, Yihua Huang, and Li Li. "Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI." Computational and Mathematical Methods in Medicine 2020 (May 5, 2020): 1–12. http://dx.doi.org/10.1155/2020/2413706.

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Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to ex

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Pistel, Mona, Luise Brock, Frederik Bernd Laun, et al. "Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions." Diagnostics 14, no. 13 (2024): 1427. http://dx.doi.org/10.3390/diagnostics14131427.

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Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and sha

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Cinarer, Gokalp, Bulent Gursel Emiroglu, and Ahmet Hasim Yurttakal. "Segmentation and Classification of Breast Cancer Tumour." International Journal of Emerging Trends in Health Sciences 2, no. 1 (2018): 14–18. http://dx.doi.org/10.18844/ijeths.v2i1.3802.

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Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The

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Barufaldi, Bruno, Jordy Gomes, Thais G. do Rego, et al. "Impact of Tomosynthesis Acquisition on 3D Segmentations of Breast Outline and Adipose/Dense Tissue with AI: A Simulation-Based Study." Tomography 9, no. 4 (2023): 1303–14. http://dx.doi.org/10.3390/tomography9040103.

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Digital breast tomosynthesis (DBT) reconstructions introduce out-of-plane artifacts and false-tissue boundaries impacting the dense/adipose and breast outline (convex hull) segmentations. A virtual clinical trial method was proposed to segment both the breast tissues and the breast outline in DBT reconstructions. The DBT images of a representative population were simulated using three acquisition geometries: a left–right scan (conventional, I), a two-directional scan in the shape of a “T” (II), and an extra-wide range (XWR, III) left–right scan at a six-times higher dose than I. The nnU-Net wa

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Bruździński, Tomasz, Adam Krzyżak, Thomas Fevens, and Łukasz Jeleń. "Web–Based Framework For Breast Cancer Classification." Journal of Artificial Intelligence and Soft Computing Research 4, no. 2 (2014): 149–62. http://dx.doi.org/10.1515/jaiscr-2015-0005.

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Abstract The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose

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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 (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 marg

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Rubio, Yoshio, and Oscar Montiel. "Multicriteria Evaluation of Deep Neural Networks for Semantic Segmentation of Mammographies." Axioms 10, no. 3 (2021): 180. http://dx.doi.org/10.3390/axioms10030180.

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Breast segmentation plays a vital role in the automatic analysis of mammograms. Accurate segmentation of the breast region increments the probability of a correct diagnostic and minimizes computational cost. Traditionally, model-based approaches dominated the landscape for breast segmentation, but recent studies seem to benefit from using robust deep learning models for this task. In this work, we present an extensive evaluation of deep learning architectures for semantic segmentation of mammograms, including segmentation metrics, memory requirements, and average inference time. We used severa

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Nor Rahim, P. A. S., N. Mustafa, H. Yazid, T. Xiao Jian, S. Daud, and K. S. A. Rahman. "Segmentation of Tumour Region on Breast Histopathology Images for Assessment of Glandular Formation in Breast Cancer Grading." Journal of Physics: Conference Series 2071, no. 1 (2021): 012051. http://dx.doi.org/10.1088/1742-6596/2071/1/012051.

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Abstract Breast cancer is the most silent killer among cancers nowadays. NHG system is widely accepted worldwide as a gold standard in providing the overall grade to breast cancer. One of the breast cancer features used in the NHG system is tubule formation. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology could influence tumour regions detection on breast histopathology images. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to

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Zewei, Zhang, Wang Tianyue, Guo Li, Wang Tingting, and Xu Lu. "An Interactive Method Based on the Live Wire for Segmentation of the Breast in Mammography Images." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/954148.

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In order to improve accuracy of computer-aided diagnosis of breast lumps, the authors introduce an improved interactive segmentation method based on Live Wire. This paper presents the Gabor filters and FCM clustering algorithm is introduced to the Live Wire cost function definition. According to the image FCM analysis for image edge enhancement, we eliminate the interference of weak edge and access external features clear segmentation results of breast lumps through improving Live Wire on two cases of breast segmentation data. Compared with the traditional method of image segmentation, experim

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Wang, Xinzhuo, Raymond Miralbell, Odile Fargier-Bochaton, Shelley Bulling, Jean Paul Vallée, and Giovanna Dipasquale. "Atlas Sampling for Prone Breast Automatic Segmentation of Organs at Risk: The Importance of Patients’ Body Mass Index and Breast Cup Size for an Optimized Contouring of the Heart and the Coronary Vessels." Technology in Cancer Research & Treatment 19 (January 1, 2020): 153303382092062. http://dx.doi.org/10.1177/1533033820920624.

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Objective: Delineation of organs at risk is a time-consuming task. This study evaluates the benefits of using single-subject atlas-based automatic segmentation of organs at risk in patients with breast cancer treated in prone position, with 2 different criteria for choosing the atlas subject. Together with laterality (left/right), the criteria used were either (1) breast volume or (2) body mass index and breast cup size. Methods: An atlas supporting different selection criteria for automatic segmentation was generated from contours drawn by a senior radiation oncologist (RO_A). Atlas organs at

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Yi, Xiaochun, and Jing Hou. "Segmentation of Medical Image Based on Superpixel Boundary Perceptual Convolutional Network in Cancer Diagnosis and Treatment." Journal of Medical Imaging and Health Informatics 11, no. 1 (2021): 254–60. http://dx.doi.org/10.1166/jmihi.2021.3425.

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In order to reduce the computational complexity of breast tumor segmentation algorithms and improve the accuracy of breast segmentation, this paper proposes a breast tumor segmentation method based on super pixel boundary perceptual convolutional network. This method first uses super pixel segmentation convolutional network algorithm to segment breast medical images, and then uses region growth algorithm to achieve breast tumor segmentation at super pixel level. The research results show that in the classification of breast tumors, the fusion efficiency based on the classifier level is better

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Tang, Haoyang, Cong Song, and Meng Qian. "Automatic segmentation algorithm for breast cell image based on multi-scale CNN and CSS corner detection." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (2020): 195–203. http://dx.doi.org/10.3233/kes-200041.

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As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment a

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Samperna, Riccardo, Nikita Moriakov, Nico Karssemeijer, Jonas Teuwen, and Ritse M. Mann. "Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI." Diagnostics 12, no. 7 (2022): 1690. http://dx.doi.org/10.3390/diagnostics12071690.

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Automatic breast and fibro-glandular tissue (FGT) segmentation in breast MRI allows for the efficient and accurate calculation of breast density. The U-Net architecture, either 2D or 3D, has already been shown to be effective at addressing the segmentation problem in breast MRI. However, the lack of publicly available datasets for this task has forced several authors to rely on internal datasets composed of either acquisitions without fat suppression (WOFS) or with fat suppression (FS), limiting the generalization of the approach. To solve this problem, we propose a data-centric approach, effi

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Guo, Ying-Ying, Yin-Hui Huang, Yi Wang, Jing Huang, Qing-Quan Lai, and Yuan-Zhe Li. "Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning." Computational and Mathematical Methods in Medicine 2022 (August 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/2541358.

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Background. Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today’s clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional

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Erandika, D. D. H., and U. A. Piumi Ishanka. "Breast Cancer Detection using Image Processing and Machine Learning: A Comprehensive Review and Improved Segmentation Approach." Sabaragamuwa University Journal 19, no. 2 (2023): 27–45. http://dx.doi.org/10.4038/suslj.v19i2.7791.

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Breast cancer stands as one of the most prevalent health concerns for women. Early detection of breast cancer can significantly improve the chances of survival. In developed countries, more than 19.9% of women will die per year due to breast cancer. Regular breast cancer screening is an important way to detect cancer early. Image processing techniques are highly used for different types of cancer detection applications with medical screening. Segmentation of the breast tumor region is a critical step in image processing related to this manner. Lots of research work can be found on developing w

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Abed, Areej Rebat, and Karim Hussein. "Comparative Analysis of Mammography Image Segmentation Strategies." Journal La Multiapp 3, no. 2 (2022): 37–43. http://dx.doi.org/10.37899/journallamultiapp.v3i2.567.

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Breast cancer is a serious medical problem that affects women all over the world, and it is one of the most well-known tumors that kill women. The specialists of Breast cancer Prefer to use imaging methods such as a mammography to speed up recovery and reduce the risk of breast cancer. An ROI describe the tumor will be retrieved from the image that is entered to detect a malignant tumor. One of the basic techniques used to classify breast cancer is segmentation. Segmentation may be difficult in the presence of noise, blurring or low contrast. Pre-processing aids in the removal of extraneous da

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Al-Faris, Ali. "Some Segmentation Approaches of Breast Magnetic Resonance Imaging Tumor in Computer Aided Detection Systems: A Review." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 11 (February 14, 2023): 1–9. http://dx.doi.org/10.37394/232018.2023.11.1.

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This article discusses the approaches and procedures that have been applied to MRI breast tumor segmentation particularly as well as breast segmentation systems in general. The review begins by outlining the various breast screening methods and contrasting Magnetic Resonance Imaging (MRI) with other methods like Mammography, X-ray, and Ultrasonography. Next, it is emphasized how important Computer Aided Detection (CAD) systems are for Breast MRI. Review and comparison of supervised, unsupervised, and semi-supervised breast MRI tumor segmentation techniques are done. The study concludes with a

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Gomathi, S., K. Malarvizhi, and M. S. Kavitha. "Detection of Mammogram Using Improved Watershed Segmentation Algorithm and Classifying with Feed Forward Neural Network (FNN)." Journal of Medical Imaging and Health Informatics 12, no. 3 (2022): 212–20. http://dx.doi.org/10.1166/jmihi.2022.3939.

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Segmentation of breast tumors with more accuracy using computerized methods is essential for breast cancer monitoring and quantification. Both segmentation and classification of breast tumors using a fully automated or Computer-Aided Diagnosis system poses various problems in terms of imaging properties. In this work, a new hybrid algorithm is proposed for segmentation with a two-step process. Initially, a watershed transformation is applied to separate all basins based on pixel density variation from the mass present in tumors, since it has been quite booming in the presence of tumors in all

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Anghelache Nastase, I.-N., S. Moldovanu, and L. Moraru. "Deep learning-based segmentation of breast masses using convolutional neural networks." Journal of Physics: Conference Series 2701, no. 1 (2024): 012005. http://dx.doi.org/10.1088/1742-6596/2701/1/012005.

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Abstract Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder

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Tayel, Mazhar Basyouni, and Azza Mahmoud Elbagoury. "Breast Infrared Thermography Segmentation Based on Adaptive Tuning of a Fully Convolutional Network." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 5 (2020): 611–21. http://dx.doi.org/10.2174/1573405615666190503142031.

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Background: Accurate segmentation of Breast Infrared Thermography is an important step for early detection of breast pathological changes. Automatic segmentation of Breast Infrared Thermography is a very challenging task, as it is difficult to find an accurate breast contour and extract regions of interest from it. Although several semi-automatic methods have been proposed for segmentation, their performance often depends on hand-crafted image features, as well as preprocessing operations. Objective: In this work, an approach to automatic semantic segmentation of the Breast Infrared Thermograp

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Fu, Xianjun, Hao Cao, Hexuan Hu, et al. "Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms." Applied Sciences 13, no. 2 (2023): 852. http://dx.doi.org/10.3390/app13020852.

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Breast cancer is one of most serious malignant tumors that affect women’s health. To carry out the early screening of breast cancer, mammography provides breast cancer images for doctors’ efficient diagnosis. However, breast cancer lumps can vary in size and shape, bringing difficulties for the accurate recognition of both humans and machines. Moreover, the annotation of such images requires expert medical knowledge, which increases the cost of collecting datasets to boost the performance of deep learning methods. To alleviate these problems, we propose an attention-based active learning frame

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Nguyen, Cong Thanh, and Quang Linh Huynh. "A Combined Segmentation and Classification Pipeline for Breast Tumors Analysis on Ultrasound Image." Journal of Physics: Conference Series 2949, no. 1 (2025): 012003. https://doi.org/10.1088/1742-6596/2949/1/012003.

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Abstract Breast cancer is a life-threatening disease characterized by the abnormal and uncontrollable growth of breast cells, leading to tumor development. Ultrasound is currently an essential non-invasive imaging technique for evaluating the features of breast tumors. During the diagnostic process, radiologists perform tumor segmentation and classification into benign or malignant categories. This manual process presents challenges due to the need for high accuracy to ensure effective diagnosis. Therefore, an automated approach is necessary to enhance precise tumor segmentation and classifica

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Zhang, Yingtao, Min Xian, Heng-Da Cheng, et al. "BUSIS: A Benchmark for Breast Ultrasound Image Segmentation." Healthcare 10, no. 4 (2022): 729. http://dx.doi.org/10.3390/healthcare10040729.

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Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available

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Zhang, Qian, Junbiao Xiao, and Bingjie Zheng. "Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms." Scientific Programming 2023 (December 16, 2023): 1–13. http://dx.doi.org/10.1155/2023/6629189.

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Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors’ time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors bu

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NG, EDDIE Y. K., and Y. CHEN. "SEGMENTATION OF BREAST THERMOGRAM: IMPROVED BOUNDARY DETECTION WITH MODIFIED SNAKE ALGORITHM." Journal of Mechanics in Medicine and Biology 06, no. 02 (2006): 123–36. http://dx.doi.org/10.1142/s021951940600190x.

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Background: Breast cancer is a common and dreadful disease in women. One in five cancers in Singaporean women is due to breast cancer. Breast health is every woman's right and responsibility. In average, every $100 spent on breast mammogram screening, an additional $33 was spent on evaluating possible false-positive results. Thermography, with its non-radiation, non-contact and low-cost basis has been demonstrated to be a valuable and safe early risk marker of breast pathology, and an excellent case management tool available today in the ongoing monitoring and treatment of breast disease. The

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Ali, Mudassar, Tong Wu, Haoji Hu, and Tariq Mahmood. "Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images." PLOS ONE 19, no. 10 (2024): e0309421. http://dx.doi.org/10.1371/journal.pone.0309421.

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Purpose Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetry and complexity of mammography images make segmentation difficult. The objective is to optimize the precision and effectiveness of medical imaging. Methods The study introduces a hybrid strategy combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA), resulting in improved computational efficiency and performance. The i

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Sethi, Mohit. "Breast Cancer Detection using Image Segmentation." International Journal of Primary and Critical Care (PCC) 1, no. 1 (2023): 1–7. http://dx.doi.org/10.54060/pcc.2023.1.

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Breast cancer is the most prevalent kind of malignancy in women, and diagnostic systems that apply artificial intelligence algorithms for breast imaging have shown positive results. Two methods that increase the precision of detecting breast tu-mors from mammography images are a multiclass support vector machine model and a deep convolutional neural network (DCNN) using K-means clustering. To accurately diagnose breast tumors, the pectoral muscle (PM) border must still be dis-tinguished from the rest of the breast tissue. By merging the transfer learning model with a number of pre-trained CNN

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Michael, Epimack, He Ma, Hong Li, Frank Kulwa, and Jing Li. "Breast Cancer Segmentation Methods: Current Status and Future Potentials." BioMed Research International 2021 (July 20, 2021): 1–29. http://dx.doi.org/10.1155/2021/9962109.

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment pl

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Bhanushali, Shweta, Sonia Lad, Vaibhavi Haria, and Poonam Bhogale. "Computer-Aided Breast Tumor Segmentation." International Journal of Computer Applications 115, no. 19 (2015): 33–36. http://dx.doi.org/10.5120/20261-2656.

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Kebede, Samuel Rahimeto, Taye Girma Debelee, Friedhelm Schwenker, and Dereje Yohannes. "Classifier Based Breast Cancer Segmentation." Journal of Biomimetics, Biomaterials and Biomedical Engineering 47 (November 2020): 41–61. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.47.41.

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Breast cancer occurs as a result of erratic growth and proliferation cells that originate in the breast. In this paper, the classifiers were used to identify the abnormalities on mammograms to get the region of interest (ROI). Before classifier based segmentation, noise, pectoral muscles, and tags were removed for a successful segmentation process. Then the proposed approach extracted the brightest regions using modified k-means. From the extracted brightest regions, shape and texture features were extracted and given to classifiers (KNN and SVM) and marked as ROI only those non-overlapping ab

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Khallassi, Ihssane, My Hachem El Yousfi Alaoui, and Abdelilah Jilbab. "SEGMENTATION OF CANCER MASSES ON BREAST ULTRASOUND IMAGES USING MODIFIED U-NET." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 13, no. 3 (2023): 11–15. http://dx.doi.org/10.35784/iapgos.5319.

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Breast cancer causes a huge number of women’s deaths every year. The accurate localization of a breast lesion is a crucial stage. The segmentation of breast ultrasound images participates in the improvement of the process of detection of breast anomalies. An automatic approach of segmentation of breast ultrasound images is presented in this paper, the proposed model is a modified u-net called Attention Residual U-net, designed to help radiologists in their clinical examination to determine adequately the limitation of breast tumors. Attention Residual U-net is a combination of existing models

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Binhssan, Abdulmuhssin. "CHARACTERIZATION OF BREAST LESIONS BY PROCESSING DIGITAL BREAST IMAGE." International Journal of Medical Science and Public Health Research 5, no. 10 (2024): 14–23. http://dx.doi.org/10.37547/ijmsphr/volume05issue10-03.

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This Rendering to the World Health Organization, women in both developed and developing nations are most likely to develop breast cancer. This illness causes breast cells to grow and multiply out of control. According to research institutes and international organizations, there are various screening methods available based on age, and breast cancer can be cured if detected in time. The Breast Imaging Reporting and Data System (BIRADS) is a standardized system that is commonly used in these techniques to report results and findings. Results are sorted by BIRADS into six categories, numbered 0

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Andreassen, Maren Marie Sjaastad, Pål Erik Goa, Torill Eidhammer Sjøbakk, et al. "Semi-automatic segmentation from intrinsically-registered 18F-FDG–PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE–MRI." Magnetic Resonance Materials in Physics, Biology and Medicine 33, no. 2 (2019): 317–28. http://dx.doi.org/10.1007/s10334-019-00778-8.

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Abstract Objectives To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. Materials and methods Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed m

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Alam, Taukir, Wei-Chung Shia, Fang-Rong Hsu, and Taimoor Hassan. "Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework." Biomedicines 11, no. 6 (2023): 1536. http://dx.doi.org/10.3390/biomedicines11061536.

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We present an analysis and evaluation of breast cancer detection and diagnosis using segmentation models. We used an advanced semantic segmentation method and a deep convolutional neural network to identify the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound images. To improve the segmentation results, we used six models to analyse 309 patients, including 151 benign and 158 malignant tumour images. We compared the Unet3+ architecture with several other models, such as FCN, Unet, SegNet, DeeplabV3+ and pspNet. The Unet3+ model is a state-of-the-art, semantic seg

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Khan, Sajid Ullah, Muhammad Nawaz Sharif, Mussaab Ibrahim Niass, Mehtab Afzal, and Muhammad Shoaib. "Comparison of multiple deep models on semantic segmentation for breast tumor detection." Foundation University Journal of Engineering and Applied Sciences <br><i style="color:yellow;">(HEC Recognized Y Category , ISSN 2706-7351)</i> 2, no. 1 (2021): 1–10. http://dx.doi.org/10.33897/fujeas.v2i1.424.

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The early diagnosis of breast tumor detection is the most significant research issue in mammography. Computer-aided diagnosis (CAD) is one of the highly essential methods to prevent breast cancer. This research work explored the effectiveness of deep-based pixel-wise segmentation models for low energy X-rays (mammographic imagery) to detect tumors in the breast region. For this purpose, various semantic segmentation models were incorporated into the experimental procedure. All the models were analyzed using the medical images dataset, which was gathered and annotated from one of the largest te

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R Shwethaa, A., and Divyashree K. "Semi - Automatic Segmentation of Human Breast from Thermal Images for Analysis of Human Breast Cancer." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 1769–72. http://dx.doi.org/10.21275/sr22619232957.

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Goh, Yi, Zhen Lee, and Christopher Lai. "Spatial Distribution and Quantification of Mammographic Breast Density, and Its Correlation with BI-RADS Using an Image Segmentation Method." Life 11, no. 6 (2021): 516. http://dx.doi.org/10.3390/life11060516.

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(1) Background: Mammographic breast density (MBD) and older age are classical breast cancer risk factors. Normally, MBDs are not evenly distributed in the breast, with different women having different spatial distribution and clustering patterns. The presence of MBDs makes tumors and other lesions challenging to be identified in mammograms. The objectives of this study were: (i) to quantify the amount of MBDs—in the whole (overall), different sub-regions, and different zones of the breast using an image segmentation method; (ii) to investigate the spatial distribution patterns of MBD in differ

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Yu, Xiang, Shui-Hua Wang, Juan Manuel Górriz, Xian-Wei Jiang, David S. Guttery, and Yu-Dong Zhang. "PeMNet for Pectoral Muscle Segmentation." Biology 11, no. 1 (2022): 134. http://dx.doi.org/10.3390/biology11010134.

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As an important imaging modality, mammography is considered to be the global gold standard for early detection of breast cancer. Computer-Aided (CAD) systems have played a crucial role in facilitating quicker diagnostic procedures, which otherwise could take weeks if only radiologists were involved. In some of these CAD systems, breast pectoral segmentation is required for breast region partition from breast pectoral muscle for specific analysis tasks. Therefore, accurate and efficient breast pectoral muscle segmentation frameworks are in high demand. Here, we proposed a novel deep learning fr

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Gómez, Kevin Alejandro Hernández, Julian D. Echeverry-Correa, and Álvaro Ángel Orozco Gutiérrez. "Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms." Healthcare Informatics Research 27, no. 3 (2021): 222–30. http://dx.doi.org/10.4258/hir.2021.27.3.222.

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Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological oper

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MUTLU, Fuldem, and Sevda GÜL. "Improving the performance of EM and K-means algorithms for breast lesion segmentation." Anatolian Current Medical Journal 5, no. 4 (2023): 492–97. http://dx.doi.org/10.38053/acmj.1361202.

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Aims: Breast cancer is the most common type of cancer in women and accounts for a large portion of cancer-related deaths. As in the other types of cancer, the prevention and early diagnosis of breast cancer gain importance day after day. For this purpose, the artificial intelligence-based decision support systems become popular in recent years. In this study, an automatic breast lesion segmentation process is proposed to detect breast lesions in the images taken with magnetic resonance imaging (MRI) protocol.  Methods: Two most popular segmentation methods: expectation maximization (EM)

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Pang, Zhiyong, Dongmei Zhu, Dihu Chen, Li Li, and Yuanzhi Shao. "A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/450531.

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This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with Relief

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Ghani, Noor Ain Syazwani Mohd, Abdul Kadir Jumaat, Rozi Mahmud, Mohd Azdi Maasar, Farizuwana Akma Zulkifle, and Aisyah Mat Jasin. "Breast Abnormality Boundary Extraction in Mammography Image Using Variational Level Set and Self-Organizing Map (SOM)." Mathematics 11, no. 4 (2023): 976. http://dx.doi.org/10.3390/math11040976.

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A mammography provides a grayscale image of the breast. The main challenge of analyzing mammography images is to extract the region boundary of the breast abnormality for further analysis. In computer vision, this method is also known as image segmentation. The variational level set mathematical model has been proven to be effective for image segmentation. Several selective types of variational level set models have recently been formulated to accurately segment a specific object on images. However, these models are incapable of handling complex intensity inhomogeneity images, and the segmenta

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Swati Bula Patil. "Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm." Panamerican Mathematical Journal 34, no. 4 (2024): 121–40. http://dx.doi.org/10.52783/pmj.v34.i4.1874.

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The precise segmentation of collagen fibres in breast tissue is essential for an efficient diagnostic and treatment strategy because breast cancer is still a major worldwide health concern. By better separating fibers from the background, the Swim Transformation (SWT) algorithm has demonstrated potential to improve collagen fiber segmentation. Unfortunately, the resilience and accuracy of the original SWT method are limited because to its susceptibility to noise and distortions. This work suggests an updated version of the SWT algorithm that includes multiple improvements to maximize collagen

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Fan, Tongle, Guanglei Wang, Yan Li, Zhongyang Wang, and Hongrui Wang. "A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation." Journal of Medical Imaging and Health Informatics 11, no. 8 (2021): 2124–32. http://dx.doi.org/10.1166/jmihi.2021.3635.

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Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram.

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Tapia Negrete, Nataly, Ruquaiyah Takhtawala, Madeleine Shaver, et al. "Automated breast cancer lesion detection on breast MRI using artificial intelligence." Journal of Clinical Oncology 37, no. 15_suppl (2019): e14612-e14612. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14612.

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e14612 Background: Over 40,000 women in the US will die from breast cancer. Early detection of cancer is crucial and is a potential avenue to improve survival. The objective of this research study is to develop a convoluted neural network (CNN), a subset of artificial intelligence, in order to enhance computerized detection of breast lesions on MRIs. Methods: This is an institutional review board approved retrospective study with post contrast MRI data from 238 patients. Breast tumor segmentation was automated with a hybrid 3D/2D CNN designed adapted from U-net, a popular neural network archit

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Li, Yuan-Zhe, Yin-Hui Huang, Xian-yan Su, et al. "Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning." Computational and Mathematical Methods in Medicine 2022 (October 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/1770531.

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Background and Objective. Breast cancer is a common malignant tumor that seriously threatens the health of women in my country and even around the world. The proliferation marker Ki-67 has been utilized to distinguish luminal B from luminal A tumors and is a reliable indicator of more aggressive breast cancer growth. If a reliable prediction method for breast cancer patients to avoid invasive damage can be found to predict Ki-67 before pathological examination, it will be very beneficial for doctors to formulate later treatment plans and provide more useful treatment options. Methodology. This

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Zhao, Tianyu, and Hang Dai. "Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm." Journal of Healthcare Engineering 2021 (October 20, 2021): 1–6. http://dx.doi.org/10.1155/2021/1141619.

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Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN met

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Preethi Veerlapalli. "ROISegNet: A Region-Focused Deep Learning Framework for Precision-Driven Medical Image Segmentation." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 243–61. https://doi.org/10.52783/jisem.v10i21s.3311.

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Breast cancer is still one of the leading causes of death in women globally. Breast thermography is a non-invasive imaging procedure that can be used as an early screening device to identify temperature irregularities that suggest breast malignancies can develop. Early diagnosis is key to achieving better treatment and survival outcomes. Automated methods for breast cancer detection have drawn much attention due to the proliferation of artificial intelligence (AI) and deep learning (DL). This paper presents a new deep learning model, ROISegNet, that enables accurate segmentation of the region

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Takhtawala, Ruquaiyah, Nataly Tapia Negrete, Madeleine Shaver, et al. "Automated artificial intelligence quantification of fibroglandular tissue on breast MRI." Journal of Clinical Oncology 37, no. 15_suppl (2019): e12071-e12071. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e12071.

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e12071 Background: The objective of this study is to examine if a convolutional neural network can be utilized to automate breast fibroglandular tissue segmentation, a risk factor for breast cancer, on MRIs. Methods: This institutional review board approved study assessed retrospectively acquired MRI T1 pre-contrast image data for 238 patients. Ground truth parameters were derived through manual segmentation. A hybrid 3D/2D U-Net architecture was developed for fibroglandular tissue segmentation. The network was trained with T1 pre-contrast MRI data and their corresponding ground-truth labels.

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