Abstract
Analyzing mammogram images is the most challenging task for radiologists in detecting breast cancer. Computer Aided Detection (CAD) plays major role in detecting such disease. Preprocessing, segmentation and detection are the processes involved in CAD. In this study, we have designed a CAD by improving the processes for the effective detection for breast cancer. Selective median filter has been used for noise reduction, Modified Local Range Modification (MLRM) is used for the enhancement, Cloud Model Based Region Growing Segmentation (CMBRGS) is used for effective segmentation of suspected area and rank based method is used for detection of cancer. This CAD method has been tested for over 40 mammogram images and found the detection accuracy of 98.8%.
Highlights
Breast cancer is a major public health problem in the world and the most common form of cancer among women worldwide
Selective median filter has been used for noise reduction, Modified Local Range Modification (MLRM) is used for the enhancement, Cloud Model Based Region Growing Segmentation (CMBRGS) is used for effective segmentation of suspected area and rank based method is used for detection of cancer
Computer Aided Detection (CAD) tool is the aid for the radiologists in analyzing such images for the effective detection and diagnosis of the disease
Summary
Breast cancer is a major public health problem in the world and the most common form of cancer among women worldwide. Computer Aided Detection (CAD) tool is the aid for the radiologists in analyzing such images for the effective detection and diagnosis of the disease Such a CAD tool consists of Preprocessing, Segmentation and detection processes (Papadopoulos et al, 2008). Mammographic feature enhancement (cluster detection and enhancement) will be essential and critical for automated mammogram analysis. It is performed by emphasizing image features and suppressing noises so that the image quality can be greatly improved and be useful for breast cancer diagnosis. In this study we have discussed about the MLRM for noise removal and contrast enhancement and CMBRGS for cluster detection and segmentation
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