Abstract: Interstitial Lung Disease (ILD) is a group of lung diseases affecting lung parenchyma. Since the lesions can be clearly identified in the CT scan of the lung, CT analysis is best among the ways for the identification of ILD by the pathologists. The automated detection of any disease from images uses the same fact that they are diagnosed by the medical professionals by exploiting the appearance features of the image of organ under consideration. Here we are applying the same to detect Lung Diseases from CT images. Segmenting the arteries and veins from the image is one of the major steps in analyzing the medical image. We need an edge enhanced lung image to do the same, so that the vascular tree can be clearly identified. In this implementation, discrete wavelet transform is applied for edge enhancement followed by the dynamic range compression. Wavelet edge enhancement and vessel enhancement filtering comprises the first stage of the algorithm. Vessel enhancement filtering uses the Eigen values of the Hessian of the image. The second stage of the algorithm corresponds to the feature extraction and classification. Feature extraction is done from the co-occurrence matrix of the resulting vessel segmented image. The co-occurrence features of the image forms the input feature vector for fuzzy SVM classifier. The performance of the proposed scheme is evaluated for accuracy.
Keywords – CAD, Wavelet edge enhancement, DWT, Fuzzy SVM, ILD diagnosis, PSO thresholding, Image classifcication, Dynamic Range Compression.
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