Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours
Open Access
- 10 August 2020
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 8, 147370-147386
- https://doi.org/10.1109/access.2020.3015286
Abstract
Automatic detection of the pectoral muscle in mammograms is widely used in computer-aided diagnostic (CAD) systems for breast cancer. The pectoral muscle region has some prominent features such as the upper corner position, high density, and triangular shape. But, these features may be distorted due to the masses, artifacts, skin folds, and overlapping tissues, and other reasons. Despite recent developments in CAD technology, accurate detection of distorted pectoral muscle images remains a challenging task. In this study, we proposed an automatic method that uses a divided topographic representation to detect distorted pectoral muscle boundaries. After the preprocessing stage, firstly an isocontour map is generated and then divided into horizontal blocks. The contours of the pectoral muscle boundary in the blocks often reveal specific patterns in terms of location, geometric and topological features. We developed a new segmentation algorithm, rule-based contour detection (RBCD), to detect these specific patterned isocontours. The method applied to two datasets consisting of 84 and 201 mammogram images from MIAS and Inbreast databases respectively. Besides, some distorted pectoral muscle samples selected from these datasets were used to further analyze the performance of the proposed method. The mean False-Positive and the mean False-Negative rates of the proposed method for MIAS and Inbreast datasets were 0.92%, 1.26%, and 2.34%, 1.15%, respectively. The quantitative and qualitative results for the distorted pectoral muscle samples show that the proposed method outperformed the compared methods.This publication has 34 references indexed in Scilit:
- Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviationPattern Recognition, 2013
- INbreast: Toward a Full-field Digital Mammographic DatabaseAcademic Radiology, 2012
- Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based FeatureJournal of Digital Imaging, 2011
- Technique for preprocessing of digital mammogramComputer Methods and Programs in Biomedicine, 2011
- Mammography image quality: Analysis of evaluation criteria using pectoral muscle presentationRadiography, 2008
- Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selectionComputerized Medical Imaging and Graphics, 2008
- Two graph theory based methods for identifying the pectoral muscle in mammogramsPattern Recognition, 2007
- Radon-Domain Detection of the Nipple and the Pectoral Muscle in MammogramsJournal of Digital Imaging, 2007
- Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View MammogramsIEEE Transactions on Medical Imaging, 2004
- Automatic Identification of the Pectoral Muscle in MammogramsIEEE Transactions on Medical Imaging, 2004