Pectoral muscle segmentation: A review

2013 ◽  
Vol 110 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Karthikeyan Ganesan ◽  
U. Rajendra Acharya ◽  
Kuang Chua Chua ◽  
Lim Choo Min ◽  
K. Thomas Abraham
Keyword(s):  
Pectoral Muscle ◽  
Muscle Segmentation

scholarly journals Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition

2015 ◽  
pp. 587-594 ◽  
Author(s):  
Adrian Galdran ◽  
Artzai Picón ◽  
Estibaliz Garrote ◽  
David Pardo
Keyword(s):  
Pectoral Muscle ◽  
Muscle Segmentation

scholarly journals A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis

2012 ◽  
Vol 64 (5) ◽  
pp. 1100-1107 ◽  
Author(s):  
Chen-Chung Liu ◽  
Chung-Yen Tsai ◽  
Jui Liu ◽  
Chun-Yuan Yu ◽  
Shyr-Shen Yu
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Pectoral Muscle ◽  
Segmentation Algorithm ◽  
Otsu Thresholding ◽  
Muscle Segmentation

scholarly journals A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm

2019 ◽  
Vol 9 (4) ◽  
pp. 481-496 ◽  
Author(s):  
Santhos Kumar Avuti ◽  
Varun Bajaj ◽  
Anil Kumar ◽  
Girish Kumar Singh
Keyword(s):  
Pectoral Muscle ◽  
Muscle Segmentation

scholarly journals Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Woong Bae Yoon ◽  
Ji Eun Oh ◽  
Eun Young Chae ◽  
Hak Hee Kim ◽  
Soo Yeul Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Automatic Segmentation ◽  
Automatic Detection ◽  
Pectoral Muscle ◽  
Accurate Information ◽  
Detection Accuracy ◽  
Pixel Intensity ◽  
Muscle Region ◽  
Computer Aided ◽  
Muscle Segmentation

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 detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.

scholarly journals Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

2019 ◽  
Vol 57 ◽  
pp. 1-17 ◽  
Author(s):  
Andrik Rampun ◽  
Karen López-Linares ◽  
Philip J. Morrow ◽  
Bryan W. Scotney ◽  
Hui Wang ◽  
...  
Keyword(s):  
Edge Detection ◽  
Pectoral Muscle ◽  
Muscle Segmentation

scholarly journals Segmentation of Pectoral Muscle in Mammograms Using Granular Computing

2022 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Divyashree B. V. ◽  
Amarnath R. ◽  
Naveen M. ◽  
Hemantha Kumar G.
Keyword(s):  
Image Analysis ◽  
Processing Time ◽  
Granular Computing ◽  
Pectoral Muscle ◽  
Combined Approach ◽  
Ant Colony Optimization Algorithm ◽  
Muscle Region ◽  
Analysis Society ◽  
Muscle Segmentation ◽  
The Right

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 mirrors the image to the left MLO view. The performance of this method was evaluated using the standard mini MIAS dataset (mammographic image analysis society). The algorithm was tested on 322 images and the overall accuracy of the system was about 97.47 %. The method is robust with respect to the view, shape, size and reduces the processing time. The approach correctly identifies images when the pectoral muscle is completely absent.

Pectoral Muscle Segmentation in Mammograms Based on Anatomic Features

2014 ◽  
Vol 39 (8) ◽  
pp. 1267-1272
Author(s):  
Yan-Feng LI ◽  
Hou-Jin CHEN ◽  
Na YANG ◽  
Sheng-Jun ZHANG
Keyword(s):  
Pectoral Muscle ◽  
Muscle Segmentation

Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation

2013 ◽  
Vol 46 (3) ◽  
pp. 681-691 ◽  
Author(s):  
Yanfeng Li ◽  
Houjin Chen ◽  
Yongyi Yang ◽  
Na Yang
Keyword(s):  
Pectoral Muscle ◽  
Muscle Segmentation

scholarly journals PeMNet for Pectoral Muscle Segmentation

Biology ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiang Yu ◽  
Shui-Hua Wang ◽  
Juan Manuel Górriz ◽  
Xian-Wei Jiang ◽  
David S. Guttery ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Diagnostic Procedures ◽  
Pectoral Muscle ◽  
Global Maximum ◽  
Dice Similarity Coefficient ◽  
Feature Maps ◽  
Cad Systems ◽  
Region Partition ◽  
Muscle Segmentation ◽  
Deep Convolution Network

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 framework, which we code-named PeMNet, for breast pectoral muscle segmentation in mammography images. In the proposed PeMNet, we integrated a novel attention module called the Global Channel Attention Module (GCAM), which can effectively improve the segmentation performance of Deeplabv3+ using minimal parameter overheads. In GCAM, channel attention maps (CAMs) are first extracted by concatenating feature maps after paralleled global average pooling and global maximum pooling operation. CAMs are then refined and scaled up by multi-layer perceptron (MLP) for elementwise multiplication with CAMs in next feature level. By iteratively repeating this procedure, the global CAMs (GCAMs) are then formed and multiplied elementwise with final feature maps to lead to final segmentation. By doing so, CAMs in early stages of a deep convolution network can be effectively passed on to later stages of the network and therefore leads to better information usage. The experiments on a merged dataset derived from two datasets, INbreast and OPTIMAM, showed that PeMNet greatly outperformed state-of-the-art methods by achieving an IoU of 97.46%, global pixel accuracy of 99.48%, Dice similarity coefficient of 96.30%, and Jaccard of 93.33%, respectively.

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