Breast Tissue Classification Using Gabor Filter, PCA and Support Vector Machine
Volume: 1 - Issue: 04 - Date: 01-04-2012
Approved ISSN: 2278-1412
Published Id: IJAECESTU193 | Page No.: 116-119
Author: Pravin S. Hajare
Co- Author: Vaibhav V. Dixit
Abstract:-Analysis of medical images is done by using image processing as it is one of the prominent tools. It is also
used in the breast cancer detection. This experiment focuses towards the identification of relevant, representative and
more important, discriminate image features for analysis of medical images. The features from mammogram images
representing normal tissues or benign and malign tumors are extracted using Gabor wavelets. These features with large
dimensions (1024x1024) are then applied to Principal Component Analysis (PCA) to reduce data dimensionality and
converted into 140x140 pixel size images. Finally, the extracted features are classified using the proximal support
vector machines as classifier. The features with orientations of 0, π/4, 3π/4, and π/2 and also with all orientations of
Gabor filters are combined with low frequency and high frequency filters and compared for Recognition rate are
calculated. The Gabor filter with low frequency with all orientation gives the highest recognition rate.
Key Words:-Breast cancer, Mammography, Gabor wavelets, PCA, SVM
Area:-Engineering
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