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.
The work in this paper aims for analyzing texture features of the prostate using Trans-Rectal Ultra-Sound images (TRUS) images for tissue characterization. This research is expected to assist beginner radiologists with the decision making. Moreover it will also assist in determining the biopsy locations. Texture feature analysis is composed of four stages. The first stage is automatically identifying Regions Of Interest (ROI), a step that was usually done either by an expert radiologist or by dividing the whole image into smaller squares that represent regions of interest. The second stage is extracting the statistical features from the identified ROIs. Two different statistical feature sets were used in this study; the first is Grey Level Dependence Matrix features. The second feature set is Grey level difference vector features. These constructed features are then ranked using Mutual Information (MI) feature selection algorithm that maximizes MI between feature and class. The obtained feature sets, the combined feature set as well as the reduced feature subset were examined using Support Vector Machine (SVM) classifier, a well established classifier that is suitable for noisy data such as those obtained from the ultrasound images. The obtained sensitivity is 83.3%, specificity ranges from 90% to 100% and accuracy ranges from 87.5% to 93.75%.