Prostate Tissue Texture Feature Extraction for Cancer Recognition in TRUS Images Using Wavelet Decomposition

In this paper, a wavelet based approach is proposed for the detection and diagnosis of prostate cancer in Trans Rectal UltraSound (TRUS) images. A texture feature extraction filter was implemented to extract textural features from TRUS images that characterize malignant and benign tissues. The filter is based on the wavelet decomposition. It is demonstrated that the wavelet decomposition reveals details in the malignant and benign regions in TRUS images of the prostate which correlate with their pathological representations. The wavelet decomposition is applied to enhance the visual distinction between the malignant and benign regions, which could be used by radiologists as a supplementary tool for making manual classification decisions. The proposed filter could be used to extract texture features which linearly separate the malignant and benign regions in the feature domain. The extracted feature could be used as an input to a complex classifier for automated malignancy region classification.

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