Log in

Relevant bibliographies by topics / Pectoral Segmentation

Contents

Journal articles
Book chapters
Conference papers

Academic literature on the topic 'Pectoral Segmentation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Pectoral Segmentation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Pectoral Segmentation"

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

Yoon, Woong Bae, Ji Eun Oh, Eun Young Chae, Hak Hee Kim, Soo Yeul Lee, and Kwang Gi Kim. "Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms." BioMed Research International 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/5967580.

Full text

Abstract:

The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic de

APA, Harvard, Vancouver, ISO, and other styles

3

Aliniya, Parvaneh, Mircea Nicolescu, Monica Nicolescu, and George Bebis. "Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images." Journal of Imaging 10, no. 12 (2024): 331. https://doi.org/10.3390/jimaging10120331.

Full text

Abstract:

Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the developm

APA, Harvard, Vancouver, ISO, and other styles

4

Liu, Chen Chung, Shyr Shen Yu, Chung Yen Tsai, and Ta Shan Tsui. "Pectoral Muscle Segmentation for Digital Mammograms Based on Otsu Thresholding." Applied Mechanics and Materials 121-126 (October 2011): 4537–41. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4537.

Full text

Abstract:

The appearance of pectoral muscle in medio-lateral oblique (MLO) views of mammograms can increase the false positive in computer aided detection (CAD) of breast cancer detection. Pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm bases on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphologic

APA, Harvard, Vancouver, ISO, and other styles

5

Divyashree B. V., Amarnath R., Naveen M., and Hemantha Kumar G. "Segmentation of Pectoral Muscle in Mammograms Using Granular Computing." Journal of Information Technology Research 15, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/jitr.2022010106.

Full text

Abstract:

In this paper, pectoral muscle segmentation was performed to study the presence of malignancy in the pectoral muscle region in mammograms. A combined approach involving granular computing and layering was employed to locate the pectoral muscle in mammograms. In most cases, the pectoral muscle is found to be triangular in shape and hence, the ant colony optimization algorithm is employed to accurately estimate the pectoral muscle boundary. The proposed method works with the left mediolateral oblique (MLO) view of mammograms to avoid artifacts. For the right MLO view, the method automatically mi

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

Suganthi, G. Vaira, J. Sutha, M. Parvathy, and C. Durga Devi. "Pectoral Muscle Segmentation in Mammograms." Biomedical and Pharmacology Journal 13, no. 03 (2020): 1357–65. http://dx.doi.org/10.13005/bpj/2005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Ganesan, Karthikeyan, U. Rajendra Acharya, Kuang Chua Chua, Lim Choo Min, and K. Thomas Abraham. "Pectoral muscle segmentation: A review." Computer Methods and Programs in Biomedicine 110, no. 1 (2013): 48–57. http://dx.doi.org/10.1016/j.cmpb.2012.10.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

Sivakumar, V., and P. Shanmugavadivu. "Mammogram Mass Segmentation Using Fractals." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3437–42. http://dx.doi.org/10.1166/jctn.2019.8305.

Full text

Abstract:

A new fractal-based technique for the segmentation of masses from digital mammograms is shown in this paper. This proposed fractal-based approach aims to detect and segment the masses from mammograms after successful suppression of pectoral muscle. This paper identifies and suppresses the pectoral muscle efficiently from the input mammogram and further segments the masses precisely with the help of fractal dimensions and fractal hurst based thresholding oriented computation. This approach is proved to be efficient in getting accurate segmentation results that guarantees the virtue of the proje

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Pectoral Segmentation"

1

Ma, Fei, Mariusz Bajger, John P. Slavotinek, and Murk J. Bottema. "Validation of Graph Theoretic Segmentation of the Pectoral Muscle." In Digital Mammography. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11783237_86.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Djaroudib, Khamsa, Pascal Lorenz, Abdelmalik Taleb Ahmed, and Abdelmadjid Zidani. "Pectoral and Breast Segmentation Technique Based on Texture Information." In Lecture Notes in Computational Vision and Biomechanics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15799-3_16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Raba, David, Arnau Oliver, Joan Martí, Marta Peracaula, and Joan Espunya. "Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_58.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Galdran, Adrian, Artzai Picón, Estibaliz Garrote, and David Pardo. "Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition." In Pattern Recognition and Image Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_66.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Zafari, Sahar, Mazen Diab, Tuomas Eerola, et al. "Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images." In Advances in Visual Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Khoulqi, Ichrak, and Najlae Idrissi. "Pectoral Muscle Segmentation Using Mammogram Images in Medio Lateral Oblique View." In Artificial Intelligence and Green Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46584-0_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Gubern-Mérida, Albert, Michiel Kallenberg, Robert Martí, and Nico Karssemeijer. "Segmentation of the Pectoral Muscle in Breast MRI Using Atlas-Based Approaches." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33418-4_46.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Wang, Kaier, Nabeel Khan, Ariane Chan, Jonathan Dunne, and Ralph Highnam. "Deep Learning for Breast Region and Pectoral Muscle Segmentation in Digital Mammography." In Image and Video Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34879-3_7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Silva, Santiago V., Cesar A. Sierra-Franco, Jan Hurtado, et al. "A Data-Centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images." In Advances in Visual Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47969-4_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Dabass, Jyoti. "Pectoral Muscle and Breast Density Segmentation Using Modified Region Growing and K-Means Clustering Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0132-6_24.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Pectoral Segmentation"

1

Huemmer, Christian, Ramyar Biniazan, Manasi Datar, Martin Kraus, Andreas Fieselmann, and Steffen Kappler. "Improved Pectoral Muscle Segmentation in Mammograms through Regression-Based Deep Learning and Knowledge Distillation." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635448.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Santhana Lakshmi, N., M. Chitra Evangelin Christina, and N. Persis Saro Bell. "Pectoral Muscle Segmentation in the Identification of Breast Cancer." In 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2018. http://dx.doi.org/10.1109/icssit.2018.8748754.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Rodriguez-Ruiz, Alejandro, Jonas Teuwen, Kaman Chung, et al. "Pectoral muscle segmentation in breast tomosynthesis with deep learning." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2292920.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Ture, Hayati, and Temel Kayikcioglu. "Segmentation of pectoral muscle by optimum contour selection approach." In 2017 Medical Technologies National Congress (TIPTEKNO). IEEE, 2017. http://dx.doi.org/10.1109/tiptekno.2017.8238131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Shanmugavadivu, P., and V. Sivakumar. "Segmentation of pectoral muscle in mammograms using fractal method." In 2013 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2013. http://dx.doi.org/10.1109/iccci.2013.6466299.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Mirzaalian, H., M. R. Ahmadzadeh, and S. Sadri. "Pectoral Muscle Segmentation on Digital Mammograms by Nonlinear Diffusion Filtering." In 2007 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/pacrim.2007.4313303.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Chatzistergos, Sevastianos, Ioannis Andreadis, and Konstantina S. Nikita. "Pectoral muscle segmentation on mammographic images based on radial lengths." In 2016 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2016. http://dx.doi.org/10.1109/ist.2016.7738278.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Lee, Myungeun, Yanjuan Chen, Soohyung Kim, and Jonghyo Kim. "Segmentation of the Pectoral Muscle Boundary in Breast MR Images." In Bioengineering (BIBE). IEEE, 2011. http://dx.doi.org/10.1109/bibe.2011.60.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Molinara, M., C. Marrocco, and F. Tortorella. "Automatic segmentation of the pectoral muscle in mediolateral oblique mammograms." In 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2013. http://dx.doi.org/10.1109/cbms.2013.6627852.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Mahaveera, Dhanush Jain, Shubham Arun Gujar, Steven Yong Cen, Xiaomeng Lei, Darryl H. Hwang, and Bino A. Varghese. "Pectoral Muscle Segmentation from Digital Mammograms Using a Transformative Approach." In 2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM). IEEE, 2023. http://dx.doi.org/10.1109/sipaim56729.2023.10373450.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!