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.
Author(s) Details
Neha N. Ganvir Author(s) Details
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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