Psychovisual issues in the display of medical images
Proceedings of the NATO Advanced Study Institute (NATO ASI Series) on Pictorial information systems in medicine
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchy in Picture Segmentation: A Stepwise Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new approach to combining region growing and edge detection
Pattern Recognition Letters
An improved seeded region growing algorithm
Pattern Recognition Letters
Digital Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region growing: a new approach
IEEE Transactions on Image Processing
Calculation of melatonin and resveratrol effects on steatosis hepatis using soft computing methods
Computer Methods and Programs in Biomedicine
Teeth segmentation of dental periapical radiographs based on local singularity analysis
Computer Methods and Programs in Biomedicine
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Digital mammogram has emerged as the most popular screening technique for early detection of breast cancer and other abnormalities in human breast tissue. It provides us opportunities to develop algorithms for computer aided detection (CAD). In this paper we have proposed three distinct steps. The initial step involves contrast enhancement by using the contrast limited adaptive histogram equalization (CLAHE) technique. Then define the rectangle to isolate the pectoral muscle from the region of interest (ROI) and finally suppress the pectoral muscle using our proposed modified seeded region growing (SRG) algorithm. The proposed algorithms were extensively applied on all the 322 mammogram images in MIAS database resulting in complete pectoral muscle suppression in most of the images. Our proposed algorithm is compared with other segmentation methods showing superior results in comparison.