Auto-Identification of Pectoral Muscle Region in Digital Mammogram Images

2014 ◽  
Author(s):  
Farag Alhsnony
Keyword(s):  
Pectoral Muscle ◽  
Digital Mammogram ◽  
Muscle Region ◽  
Auto Identification ◽  
Mammogram Images

REGION-BASED CONTRAST ENHANCEMENT OF DIGITAL MAMMOGRAMS USING AN IMPROVED WATERSHED SEGMENTATION

2013 ◽  
Vol 13 (01) ◽  
pp. 1350007 ◽  
Author(s):  
ABUBACKER KAJA MOHIDEEN ◽  
KUTTIANNAN THANGAVEL
Keyword(s):  
Contrast Enhancement ◽  
Absolute Error ◽  
Pectoral Muscle ◽  
Histogram Equalization ◽  
Classification Performance ◽  
Performance Measure ◽  
Watershed Segmentation ◽  
Muscle Region ◽  
Edge Based ◽  
Mammogram Images

A simple edge-based preprocessing scheme is proposed in this paper for contrast enhancement of digital mammogram images while preserving the edges more accurately. This proposed method has three steps: (i) initially the breast region is segmented from the mammogram images by removing the film artifacts, (ii) the pectoral muscle region is identified and excluded from the breast region using a novel adaptive thresholding method, and (iii) an Improved Watershed Segmentation (IWS) is applied to segment the breast profile, and each region is enhanced with simple histogram equalization. The segmentation is performed in order to achieve adaptive contrast enhancement. The performance of this proposed pectoral removal method is analyzed with two measures: Hausdorff Distance (HD) and Mean of Absolute Error Distance (MAED), and the proposed contrast enhancement approach is been analyzed with the five diverse parameters along with the classification accuracy. The experiments and results show the potential performance of our proposed algorithm over the existing approaches with optimum results on all the performance measure and the classification performance is been evaluated with a hybrid neural network, our proposed method proves the better performance with the achievement of 92% accuracy.

Removal of Pectoral Muscle Region in Digital Mammograms using Binary Thresholding

2012 ◽  
Vol 2 (3) ◽  
pp. 21-29 ◽  
Author(s):  
A. Kaja Mohideen ◽  
K. Thangavel
Keyword(s):  
Binary Operation ◽  
Distance Measure ◽  
Absolute Error ◽  
Pectoral Muscle ◽  
Density Region ◽  
Muscle Region ◽  
Pectoral Region ◽  
The Mean ◽  
Error Distance ◽  
Mammogram Images

The pectoral muscle represents a predominant density region in Medio-Lateral Oblique (MLO) views of mammograms, which appears at approximately the same density as the dense tissues of interest in the image and can affect the results of image analysis methods. Therefore, segmentation of pectoral muscle is important in order to limit the search for the breast abnormalities only to the breast region. In this paper, a simple and effective approach is proposed to exclude the pectoral muscle based on binary operation. The performance is analyzed by the Hausdorff Distance Measure (HDM) and also the Mean of Absolute Error Distance Measure (MAEDM) based on differences between the results received from the radiologists and by the proposed method. The digital mammogram images are taken from MIAS dataset which contains 322 images in total, out of which the proposed algorithm able to detect and remove the pectoral region from 291 images successfully.

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550001 ◽  
Author(s):  
A. Suruliandi ◽  
G. Murugeswari ◽  
P. Arockia Jansi Rani
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Rate Sensitivity ◽  
Digital Mammogram ◽  
Abnormality Detection ◽  
Texture Descriptor ◽  
Texture Pattern ◽  
Local Texture ◽  
Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

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.

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