Tissue-characterization of the prostate using radio frequency ultrasonic signals

In this paper, we will present a complete method and system for the detection of prostatic carcinoma, providing color-coded images of the estimated probability of malignancy by processing radio-frequency ultrasonic echo signals. For this, a hardware setup based on a conventional diagnostic sonograph was realized. The image-processing software works on ultrasound images automatically segmented into regions of about 3/spl times/3.5 mm. System-dependent effects, as well as tissue attenuation, were measured and compensated for. Tissue-characterisation parameters, which have been used successfully by other authors, were calculated for each segment. To demonstrate the methods of selection of relevant parameters and comparison of different classifiers, a first clinical study using data of 33 patients with local prostatic carcinoma was performed. For these patients, location and extent of the carcinoma were known from histological findings after radical prostatectomy. Classifiers investigated during the study were: the linear and quadratic Bayes classifier, a nearest neighbor classifier, and several classifiers based on Kohonen-maps. The best classifier was used to calculate color-coded result images. Applying a threshold of 50% to the estimated probability of malignancy, produced the encouraging results of 82 and 88% for sensitivity and specificity, respectively.