Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologists, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. In mammogram images, micro-calcifications are one of the imperative signs for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. Early stage detection for any medical image analysis application like brain tumor detection, breast cancer detection is considered as an important step. Micro calcification is small calcium deposits in the breast region and mammogram images are of low contrast. Thus, in this work, different types of filtering techniques used for noise reduction and image enhancement for medical image processing are analyzed on mini-MIAS mammogram image databases. Anisotropic diffusion with wavelet filtering method shows best results for enhancement and noise removal of the image. This filtered image is segmented; region of interest (ROI) is extracted through global Thresholding technique with discrete wavelet transform (DWT). Gray level cooccurrence matrix is used to extract the important features. Here, seven features are extracted for different categories of micro calcified images like normal, benign, malignant. Results show that from extracted features the values of malignant and micro calcified images are same whereas normal and benign are same. This proposed methodology can help to categorize different classes of images.
Neha N. Ganvir
Department of Electronics Engineering, Sinhgad Institute of Technology and Science (SITS), Narhe, Sinhgad Technical Education Society (STES), Pune, India.
Dr. D. M. Yadav
Pune University, India.
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