Pectoral Muscle Segmentation for Digital Mammograms Based on Otsu Thresholding

2011 ◽  
Vol 121-126 ◽  
pp. 4537-4541
Author(s):  
Chen Chung Liu ◽  
Shyr Shen Yu ◽  
Chung Yen Tsai ◽  
Ta Shan Tsui
Keyword(s):  
Cancer Detection ◽  
Pectoral Muscle ◽  
Morphological Processing ◽  
Breast Cancer Detection ◽  
Computer Aided Detection ◽  
Otsu Thresholding ◽  
Expert Radiologist ◽  
Analysis Society ◽  
Mammogram Image ◽  
Muscle Segmentation

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 morphological processing to find the rough border of the pectoral muscle. The multiple regression analysis is then employed on the rough border to obtain the accurate segmentation of the pectoral muscle. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the pectoral muscle extracted by the presented algorithm approximately follows that extracted by an expert radiologist.

scholarly journals Assessment of Breast Cancer Detection and Implementation

2019 ◽  
Vol 8 (11) ◽  
pp. 323-330
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Median Filter ◽  
Breast Cancer Detection ◽  
Statistical Parameters ◽  
Cause Of Deaths ◽  
Life Threatening ◽  
Mammogram Image ◽  
Mean Variance ◽  
Progressive Algorithms

Breast Cancer is the most dangerous and life threatening disease. Of all kinds of cancers, Breast cancer is the second major cause of deaths and especially the first major cause of deaths in women. In this project, images are taken from medical representativess in order to implement a real time project. This methodology aims at diagnosing Breast Cancer at an earlier stage by considering progressive algorithms. In this methodology, a mammogram image is considered. To this image sample, image segmentation technique is applied which separates fore-ground regions from the background regions. Later, Binarization technique is used to enrich the contrast of the image in order to make it more desirable for finding the tumour cell location within the affected area. Median filter is used for removing noise within the image. To the noise free images, some statistical parameters viz., mean, variance, Standard deviation, Mean Square error and entropy are calculated to analyze the performance

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.

Evaluation of Binarization Algorithms in Preprocessing of Digital Mammographies

2016 ◽  
Vol 0 (0) ◽  
Author(s):  
B. Tene ◽  
D.C. Puchianu ◽  
Nicoleta Angelescu
Keyword(s):  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Digital Mammogram ◽  
Mammographic Images ◽  
Computer Aided ◽  
Negative Effect ◽  
Mammogram Image ◽  
Microcalcification Detection ◽  
Aided Diagnosis

AbstractDigital mammograms are a useful tool for breast cancer detection. The quality of digital mammogram image can have a negative effect on the computer-aided diagnosis system. We investigate the use of automatic image thresholding methods for microcalcification detection in mammographic images. Experimental results on the BI-RADS 4 images dataset confirm the proposed approach.

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