The anisotropic Gaussian kernel for SVM classification of HRCT images of the lung

High-resolution computed tomography (HRCT) produces lung images with a high level of detail which makes it suitable for diagnosis of diffuse lung diseases. Segmentation of abnormal lung patterns is a necessary stage in the construction of a computer-aided diagnosis system. We interpret lung patterns as textures and develop a texture classification technique for segmentation of lung patterns. The wavelet transform is used to extract texture features and then the support vector machines (SVM) machine learning algorithm is applied to texture classification. The parameters of the SVM play a crucial role in the performance of the algorithm. We apply gradient-based optimization of the radius/margin bound of a generalization error to tune parameters of the SVM algorithm. We compare the performance of isotropic and anisotropic Gaussian kernels and study the applicability of the radius/margin bound to tuning parameters of the SVM algorithm on the problem of lung pattern classification.