AIS TLS-ESPRIT feature selection for prostate tissue characterization

S. S. Mohamed; A. M. Youssef; E. F. El-Saadany; M. M. A. Salama

doi:10.1117/12.649274

15 March 2006 AIS TLS-ESPRIT feature selection for prostate tissue characterization

S. S. Mohamed, A. M. Youssef, E. F. El-Saadany, M. M. A. Salama

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 614451 (2006) https://doi.org/10.1117/12.649274
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

The work in this paper aims for analyzing spectral features of the prostate using Trans-Rectal Ultra-Sound images (TRUS) for tissue classification. This research is expected to augment beginner radiologists' decision with the experience of more experienced radiologists. Moreover, Since, in some situations the biopsy results in false negatives due to inaccurate biopsy locations, therefore this research also aims to assist in determining the biopsy locations to decrease the false negative results. In this paper, a new technique for prostate tissue characterization is developed. The proposed system is composed of four stages. The first stage is automatically identifying Regions Of Interest (ROIs). This is achieved using the Gabor multiresolution analysis method, where preliminary regions are identified using the frequency response of the pixels, pixels that have the same response to the same filter are assigned to the same cluster. Next, the radiologist knowledge is integrated to the system to select the most suspicious ROIs among the prelimianry identified regions. The second stage is constructing the spectral features from the identified ROIs. The proposed technique is based on a novel spectral feature set for the TRUS images using the Total Least Square Estimation of Signal Parameters via Rotational Invariance Techniques (TLS-ESPRIT). Classifier based feature selection is then performed to select the most salient features using the recently proposed Artificial Immune System (AIS) optimization technique. Finally, Support Vector Machine (SVM) classifier is used as an accuracy measure, our proposed system obtains a classification accuracy of 94.4%, with 100% sensitivity and 83.3% sensetivity.

Citation Download Citation

S. S. Mohamed, A. M. Youssef, E. F. El-Saadany, and M. M. A. Salama "AIS TLS-ESPRIT feature selection for prostate tissue characterization", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614451 (15 March 2006); https://doi.org/10.1117/12.649274

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