Multi-scale dyadic filter modulation based enhancement and classification of medical images

For the last many decades, the research is towards the classification of medical images in the early phase of its detection. But, the task becomes challenging due to the absence of the color information, like in natural scene images, and low illumination. In this paper, a multi-scale spectral approach is proposed for the classification of medical images. The proposed approach uses a dyadic filter bank extended to six scales for simultaneous modulation of the frequency and amplitude signal of the medical image. The modulated signal strength is used for enhancing the contrast of the image as a preprocessing step. The 32 bin spectral histogram is used to fetch the features using different modulation components at each scale of the dyadic filter bank. The proposed method has experimented with two medical imaging databases - one is malignant Brain tumor MRI scans collected from SMS medical college Jaipur. The second database is from the TCIA data repository having three datasets of Lung-CT and Brain MRI. These datasets have experimented with SVM using a quadratic kernel function. The experimental results show that the proposed approach fetches better textural information as compared with traditional texture analysis methods. Based on the analysis of the experimentation results, it is recommended that the use of the spectral features gives better early detection of the abnormalities for medical imaging datasets.