Gabor wavelet based features for medical image analysis and classification

Identifying relevant, representative and more important, discriminant image features for analysis and proper image classification purpose is one of the main tasks in image processing and pattern recognition field. In this paper, Gabor wavelets based features are extracted from medical mammogram images representing normal tissues, or benign and malign tumors. Once features are detected, Principal Component Analysis (PCA) is further employed to reduce data dimensionality. To an end, directional properties and frequency spectrum of those features are analyzed with respect to the classification performance by employing multiclass support vector machines as classifier. For comparison, as baseline, PCA was also applied directly to the data set with no feature filtering. The experimental results indicate that directional properties of Gabor wavelets provided by their orientation are important issues to accurately discriminate mammogram tumor types.