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 apply 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 choose parameters of the SVM algorithm. This approach is more efficient in terms of the required number of SVM training cycles than the commonly used method of finding the optimal parameters which is based on sampling the parameter space and choosing the
parameter combination which produces the lowest test error. We assess the
applicability of optimization of the radius/margin bound to tuning SVM
parameters for the problem of segmentation of lung pattern textures in HRCT images. Results of experiments indicate that this method chooses parameters which are comparable to the parameters obtained using test error in terms of classification accuracy, employing fewer training cycles.