Classification of Prostate Cancerous Tissues by Support Vector Machine Algorithm with Different Kernels from T2-Weighted Magnetic Resonance Images

Prostate cancer is one of the most prevalent cancer types in Iran and worldwide. Prostate cancer imaging had been promoted using magnetic resonance imaging (MRI). The aim of present study was to estimate prostate tumors volume by a computerized approach.

By using a Matlab command, the regions of interest were precisely identified. The Haralick features were applied. In addition, using the principal component analysis algorithm, five important features were selected among 17 features. Then, a support vector machine classifier was applied to classify cancerous and normal tissues. To increase the accuracy of the machine vector classifier, the proposed solutions were applied: 1) a new feature was introduced and extracted, 2) all features were normalized, 3) to optimize mutual validation, k-fold changed from 5 to 10. In addition, the support vector machine classifier was implemented by using the Gaussian kernel, radial basis function, and linear kernel. If the tumor was identified in more than one slice, all identified region of interest (ROIs) in different slices were considered in the feature extractions and tumor volume estimation processes.

Among the Haralick features, contrast, correlation, homogeneity, energy, and entropy were the most powerful features in this study that confirmed the findings of previous studies. The sensitivity of the classifier was obtained 0.9180 using Gaussian kernel, while with radial basis function and linear kernels obtained 0.7097 and 0.8571, respectively. In addition, the specificity of Gaussian, radial basis function, and linear kernels were obtained 0.6500, 0.8305, and 0.7069, respectively. The accuracy with Gaussian and linear kernels was obtained 0.7851 which was greater than with the radial basis function. The feature extraction of the regions of interest, feature reduction, and classification steps took less than one minute which indicated the proposed algorithm was fast. It was also repeatable.

The proposed computerized estimation of prostate tumors volume can increase the accuracy of the diagnosis. It is quick and simply repeatable.