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 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.

scholarly journals Deep Learning Based Pectoral Muscle Segmentation on MIAS Mammograms

2020 ◽  
Author(s):  
Young Jae Kim ◽  
Eun Young Yoo ◽  
Kwang Gi Kim
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Deep Learning ◽  
Cross Validation ◽  
Pectoral Muscle ◽  
Training Data ◽  
Dice Similarity Coefficient ◽  
Detection Accuracy ◽  
Pectoral Muscle Detection ◽  
Fold Cross Validation

Abstract Background: The purpose of this study was to propose a deep learning-based method for automated detection of the pectoral muscle, in order to reduce misdetection in a computer-aided diagnosis (CAD) system for diagnosing breast cancer in mammography. This study also aimed to assess the performance of the deep learning method for pectoral muscle detection by comparing it to an image processing-based method using the random sample consensus (RANSAC) algorithm. Methods: Using the 322 images in the Mammographic Image Analysis Society (MIAS) database, the pectoral muscle detection model was trained with the U-Net architecture. Of the total data, 80% was allocated as training data and 20% was allocated as test data, and the performance of the deep learning model was tested by 5-fold cross validation. Results: The image processing-based method for pectoral muscle detection using RANSAC showed 92% detection accuracy. Using the 5-fold cross validation, the deep learning-based method showed a mean sensitivity of 95.55%, mean specificity of 99.88%, mean accuracy of 99.67%, and mean Dice similarity coefficient (DSC) of 95.88%. Conclusions: The proposed deep learning-based method of pectoral muscle detection performed better than an existing image processing-based method. In the future, by collecting data from various medical institutions and devices to further train the model and improve its reliability, we expect that this model could greatly reduce misdetection rates by CAD systems for breast cancer diagnosis.

Estimación Semi-automática de Fracción Glandular Mamaria en Imágenes de Mastografía

2021 ◽  
Author(s):  
◽  
M. P. R. Zamudio-Arteaga
Keyword(s):  
Breast Cancer ◽  
Spatial Clustering ◽  
Quantitative Estimation ◽  
Automatic Segmentation ◽  
Public Health Problem ◽  
Pectoral Muscle ◽  
Glandular Tissue ◽  
Connective Tissues ◽  
Adipose Tissues ◽  
Breast Tissues

Breast cancer is a priority public health problem due to its global magnitude and importance, that develops mainly in the glandular tissue. On mammography imaging, the presence of a large amount of glandular tissue could conceal lesions. Due to this, the estimation of glandular fraction (FG) is a tool that allows evaluating the risk of developing breast cancer. Having knowledge of the different tissues that constitute the anatomy of the breast (glandular, connective and adipose tissues), on a mammography image there are structures that should not be considered for the estimation of the FG, such as skin or pectoral muscle. In the clinical practice, a proper differentiation between glandular and connective tissues is a challenging task, and a discrimination of extra-mammary structures from glandular tissue is particularly difficult due to an intensity similarity. In this work, a strategy to properly isolate the principal breast tissues from the extra-mammary structures, and to perform a robust semi-automatic segmentation of glandular, connective and adipose tissues by using the K-means algorithm in order to provide a quantitative estimation of the mammary glandular fraction is presented. Additionally, a comparison with the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and an empirical glandular fraction estimated by a clinical expert, to demonstrate the convenience of the strategy is made.

Computer Aided Detection of Clustered Microcalcification: A Survey

2019 ◽  
Vol 15 (2) ◽  
pp. 132-149 ◽  
Author(s):  
M.N. Arun Kumar ◽  
M.N. Anil Kumar ◽  
H.S. Sheshadri
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Pectoral Muscle ◽  
Diagnostic Purpose ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Muscle Segmentation ◽  
Microcalcification Clusters ◽  
Visual Indication

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. </P><P> Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography

2019 ◽  
Vol 53 (3) ◽  
pp. 1873-1918 ◽  
Author(s):  
Mehrdad Moghbel ◽  
Chia Yee Ooi ◽  
Nordinah Ismail ◽  
Yuan Wen Hau ◽  
Nogol Memari
Keyword(s):  
Pectoral Muscle ◽  
Computer Aided Detection ◽  
Segmentation Methods ◽  
Computer Aided ◽  
Muscle Segmentation
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