American College of Radiology introduces a standard in classification, the breast imaging reporting and data system
(BIRADS), standardize the reporting of ultrasound findings, clarify its interpretation, and facilitate communication
between clinicians. The effective use of new technologies to support healthcare initiatives is important and current
research is moving towards implementing computer tools in the diagnostics process. Initially a detailed study was carried
out to evaluate the performance of two commonly used appearance based classification algorithms, based on the use of
Principal Component Analysis (PCA), and two dimensional linear discriminant analysis (2D-LDA). The study showed
that these two appearance based classification approaches are not capable of handling the classification of ultrasound
breast image lesions. Therefore further investigations in the use of a popular feature based classifier – Support Vector
Machine (SVM) was conducted. A pre-processing step before feature based classification is feature extraction, which
involve shape, texture and edge descriptors for the Region of Interest (ROI). The input dataset to SVM classification is
from a fully automated ROI detection. We achieve the success rate of 0.550 in PCA, 0.500 in LDA, and 0.931 in SVM.
The best combination of features in SVM classification is to combine the shape, texture and edge descriptors, with
sensitivity 0.840 and specificity 0.968. This paper briefly reviews the background to the project and then details the
ongoing research. In conclusion, we discuss the contributions, limitations, and future plans of our work
