Relevant bibliographies by topics / Breast Segmentation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Breast Segmentation'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Breast Segmentation"

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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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Dissertations / Theses on the topic "Breast Segmentation"

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Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.

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This thesis deals with the detection, segmentation and classification of lesions on sonography. The contribution of the thesis is the development of a new Computer-Aided Diagnosis (CAD) framework capable of detecting, segmenting, and classifying breast abnormalities on sonography automatically. Firstly, an adaption of a generic object detection method, Deformable Part Models (DPM), to detect lesions in sonography is proposed. The method uses a machine learning technique to learn a model based on Histogram of Oriented Gradients (HOG). This method is also used to detect cancer lesions directly,
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Shan, Juan. "A Fully Automatic Segmentation Method for Breast Ultrasound Images." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/905.

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Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a cha
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Al-Roubaie, Zahra. "Human breast images : segmentation, analysis and conversion to electrical parameter profiles for Semcad-X electromagnetic simulator." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=116044.

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Electrical parameter profiles of human breast images can be used to simulate and analyze the anticipated effects on tissue from its interaction with electromagnetic fields involved in the cancer treatment exposure. In part, the success of this approach depends on the accuracy and precision in identifying the different tissue types. In this work, we propose two methods of segmenting human breast images with malignant tumors. The first method of algorithmic partitioning of the image involves manual color-edge contouring of the tissues using a cursor and subsequent identification of the tissue ty
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von, Lavante Etienne. "Segmentation and sizing of breast cancer masses with ultrasound elasticity imaging." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81225f61-6b83-405b-aed5-17b316ed586a.

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Uncertainty in the sizing of breast cancer masses is a major issue in breast screening programs, as there is a tendency to severely underestimate the sizing of malignant masses, especially with ultrasound imaging as part of the standard triple assessment. Due to this issue about 20% of all surgically treated women have to undergo a second resection, therefore the aim of this thesis is to address this issue by developing novel image analysis methods. Ultrasound elasticity imaging has been proven to have a better ability to differentiate soft tissues compared to standard B-mode. Thus a novel seg
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Rojas, Dominguez Alfonso. "Automated detection, segmentation and classification of breast masses in digitised mammograms." Thesis, University of Liverpool, 2007. http://livrepository.liverpool.ac.uk/375/.

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A method for automatic detection of mammographic masses is presented. As part of this method, an enhancement algorithm is proposed that improves image contrast based on local statistical measures of the mammograms. After enhancement, regions are segmented via thresholding at multiple levels, and a set of features is computed from each of the segmented regions. A region-ranking system is also presented that identifies the regions most likely to represent abnormalities based on the features computed. The method was tested on 57 mammographic images of masses and achieved a sensitivity of 80% at 2
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Kwok, Sze Man Simon. "Attribute-driven segmentation and analysis of mammograms." University of Western Australia. Centre for Intelligent Information Processing Systems, 2005. http://theses.library.uwa.edu.au/adt-WU2005.0010.

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[Truncated abstract] In this thesis, we introduce a mammogram analysis system developed for the automatic segmentation and analysis of mammograms. This original system has been designed to aid radiologists to detect breast cancer on mammograms. The system embodies attribute-driven segmentation in which the attributes of an image are extracted progressively in a step-by-step, hierarchical fashion. Global, low-level attributes obtained in the early stages are used to derive local, high-level attributes in later stages, leading to increasing refinement and accuracy in image segmentation and anal
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Masek, Martin. "Hierarchical segmentation of mammograms based on pixel intensity." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2004. http://theses.library.uwa.edu.au/adt-WU2003.0033.

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Mammography is currently used to screen women in targeted risk classes for breast cancer. Computer assisted diagnosis of mammograms attempts to lower the workload on radiologists by either automating some of their tasks or acting as a second reader. The task of mammogram segmentation based on pixel intensity is addressed in this thesis. The mammographic process leads to images where intensity in the image is related to the composition of tissue in the breast; it is therefore possible to segment a mammogram into several regions using a combination of global thresholds, local thresholds and high
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Ball, John E. "Three stage level set segmentation of mass core, periphery, and spiculations for automated image analysis of digital mammograms." Diss., Mississippi State : Mississippi State University, 2007. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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Massich, Joan. "Segmentation d'objets déformables en imagerie ultrasonore." Thesis, Dijon, 2013. http://www.theses.fr/2013DIJOS090/document.

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Le cancer du sein est le type de cancer le plus répandu, il est la cause principale de mortalité chez les femmes aussi bien dans les pays occidentaux que dans les pays en voie de développement. L'imagerie médicale joue un rôle clef dans la réduction de la mortalité du cancer du sein, en facilitant sa première détection par le dépistage, le diagnostic et la biopsie guidée. Bien que la Mammographie Numérique (DM) reste la référence pour les méthodes d'examen existantes, les échographies ont prouvé leur place en tant que modalité complémentaire. Les images de cette dernière fournissent des inform
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Tardy, Mickael. "Deep learning for computer-aided early diagnosis of breast cancer." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0035.

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Le cancer du sein est un des plus répandus chez la femme. Le dépistage systématique permet de baisser le taux de mortalité mais crée une charge de travail importante pour les professionnels de santé. Des outils d’aide au diagnostic sont conçus pour réduire ladite charge, mais un niveau de performance élevé est attendu. Les techniques d’apprentissage profond peuvent palier les limitations des algorithmes de traitement d’image traditionnel et apporter une véritable aide à la décision. Néanmoins, plusieurs verrous technologiques sont associés à l’apprentissage profond appliqué à l’imagerie du sei
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Books on the topic "Breast Segmentation"

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Parker, Philip M. The 2007 Report on Frozen Rye and Pumpernickel Breads: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007 Report on Retail Bakery Breads and Rolls Excluding Bagels: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007 Report on Frozen Raisin, Potato, Self-Rising, Salt-Free, and Canned Breads: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007 Report on Whole Wheat, Cracked Wheat, Multigrain, and Other Dark Wheat Breads: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007 Report on Frozen Whole Wheat, Cracked Wheat, Multigrain, and Other Dark Wheat Breads: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007 Report on Raisin, Potato, Self-Rising, Salt-Free, and Canned Breads Excluding Frozen Bread: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007 Report on Whole Wheat, Cracked Wheat, Multigrain, and Other Dark Wheat Breads Excluding Frozen Bread: World Market Segmentation by City. ICON Group International, Inc., 2006.

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Book chapters on the topic "Breast Segmentation"

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Wu, Shandong, Susan Weinstein, Brad M. Keller, Emily F. Conant, and Despina Kontos. "Fully-Automated Fibroglandular Tissue Segmentation in Breast MRI." In Breast Imaging. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_32.

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He, Wenda, Sam Harvey, Arne Juette, Erika R. E. Denton, and Reyer Zwiggelaar. "Mammographic Segmentation and Density Classification: A Fractal Inspired Approach." In Breast Imaging. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41546-8_45.

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Massich, Joan, Fabrice Meriaudeau, Melcior Sentís, et al. "Automatic Seed Placement for Breast Lesion Segmentation on US Images." In Breast Imaging. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_40.

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Petersen, Kersten, Mads Nielsen, Pengfei Diao, Nico Karssemeijer, and Martin Lillholm. "Breast Tissue Segmentation and Mammographic Risk Scoring Using Deep Learning." In Breast Imaging. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07887-8_13.

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Massich, Joan, Fabrice Meriaudeau, Elsa Pérez, Robert Martí, Arnau Oliver, and Joan Martí. "Lesion Segmentation in Breast Sonography." In Digital Mammography. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13666-5_6.

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Petroudi, Styliani, and Michael Brady. "Breast Density Segmentation Using Texture." In Digital Mammography. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783237_82.

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Zolfagharnasab, Hooshiar, João P. Monteiro, João F. Teixeira, Filipa Borlinhas, and Hélder P. Oliveira. "Multi-modal Complete Breast Segmentation." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_57.

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He, Wenda, Erika R. E. Denton, and Reyer Zwiggelaar. "Mammographic Segmentation and Risk Classification Using a Novel Binary Model Based Bayes Classifier." In Breast Imaging. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_6.

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Kuo, Hsien-Chi, Maryellen L. Giger, Ingrid Reiser, et al. "Level Set Breast Mass Segmentation in Contrast-Enhanced and Non-Contrast-Enhanced Breast CT." In Breast Imaging. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31271-7_90.

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Pramanik, Sourav, Mrinal Kanti Bhowmik, Debotosh Bhattacharjee, and Mita Nasipuri. "Hybrid Intelligent Techniques for Segmentation of Breast Thermograms." In Hybrid Soft Computing for Image Segmentation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47223-2_11.

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Conference papers on the topic "Breast Segmentation"

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Hossain, Md Belayat, Tamerlan Mustafaev, Robert M. Nishikawa, and Juhun Lee. "Developing breast dense tissue segmentation algorithm in digital breast tomosynthesis." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3047307.

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Rehman, Engr Arif Ali, Atif Ahmed Siddiqui, Enrique Nava, Pablo Otero, Syed Ashad Mustufa, and Nabil Shaukat. "Breast Tumor Segmentation Using Active Contours." In 2024 Global Conference on Wireless and Optical Technologies (GCWOT). IEEE, 2024. https://doi.org/10.1109/gcwot63882.2024.10805598.

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K, Majina. "U-Net for Breast Tumor Segmentation." In 2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2024. https://doi.org/10.1109/hipcw63042.2024.00079.

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Lin, Ke, Xiangfeng Wen, Zhisong Qin, and Wenying Chen. "Pharmacokinetics-guided breast tumor segmentation method." In 2025 5th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2025. https://doi.org/10.1109/isctis65944.2025.11065954.

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Rana, Khadija, Eman Dar, Muhammad Talha Bilal, et al. "Breast Cancer Segmentation Using U-NET Architecture." In 2024 2nd International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC). IEEE, 2024. https://doi.org/10.1109/icmacc62921.2024.10893910.

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Luo, Meiyuan, Chao Zhang, and Yu Liu. "An Efficient Lightweight Network for Breast Cancer Segmentation." In 2024 2nd International Conference on Machine Vision, Image Processing & Imaging Technology (MVIPIT). IEEE, 2024. https://doi.org/10.1109/mvipit65697.2024.00035.

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Andini, Silfia, Sumijan, and Iskandar Fitri. "Identification of Lesions on Breast Based on Breast Ultrasonography Images Using Segmentation Method." In 2024 12th International Conference on Information and Communication Technology (ICoICT). IEEE, 2024. http://dx.doi.org/10.1109/icoict61617.2024.10698665.

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Correa, Thays, Fabíola De Oliveira, Matheus Baffa, and Lucas Lattari. "Unsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13477.

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Breast cancer is the second most common type of cancer in the world. It is estimated that 29.7% of new cases diagnosed in Brazil occur in any structures of the breasts. However, the disease has a good prognosis if detected early. Thus, the development of new technologies to help doctors to provide an accurate diagnosis is indispensable. The goal of this work is to develop a new method to automate parts of computer-aided diagnosis systems, performing the unsupervised segmentation of the Region of Interest (ROI) of infrared breast images acquired in lateral view. The segmentation proposed in thi
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Roy, Kyamelia, Shrishti Ghosh, Anupurba Mukherjee, et al. "Breast Tumor Segmentation using Image Segmentation Algorithms." In 2019 International Conference on Opto-Electronics and Applied Optics (Optronix). IEEE, 2019. http://dx.doi.org/10.1109/optronix.2019.8862339.

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Wang, Lei, Bram Platel, Tatyana Ivanovskaya, Markus Harz, and Horst K. Hahn. "Fully automatic breast segmentation in 3D breast MRI." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235732.

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Reports on the topic "Breast Segmentation"

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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose t
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