Determining And Classifying The Region Of Interest In Ultrasonic Images Of The Breast Using Neural Networks

Abstract

This paper describes in detail how ultrasonic images of the female breast have been processed and neural nets used to aid the identification of malignant and benign areas in them. The images are windowed, filtered and pre-processed using a `spider-web` extraction algorithm into suitable patterns for processing by a neural net. Two networks are trained and used: one for malignant cases and the other for benign cases. These are used to make predictions of regions of interest for unknown cases. The predictions are presented as circles overlayed on the image. The relative sizes of the circles give an indication of the diagnosis category and the position of the circles show where the centre of gravity of the regions of interest lay. The system has been prototyped and tested with 25 test cases (13 malignant, 9 benign and 3 with no lesions) which are intended to show the strengths and weaknesses of the approach. Experts agreed well with the classification and localisation in 17 cases. The system is usually weak when the evidence on the image is considered weak by the expert. It is concluded that the system is promising and should be developed further by providing more training to the network. Introduction According to Boone et al (1990a), radiologists first read the radiographic images and compose a mental list of abnormal findings which may serve as clues in the diagnosis. They state that this first stage process of diagnosis is clearly one of pattern recognition. However, it could be argued that this is a gross oversimplification of what is a much more complicated cognitive function especially where there is a less direct correspondence between the image and its physical counterpart as in the ultrasound image. In these cases, a more complicated model might include, for examp..