The aim of the thesis is to explore problems related to ultrasound imaging, in particular to ovarian cancer, and to develop tools to analyze the images using unsupervised learning algorithm. Benign and malignant masses show different features that can be investigated with ultrasound scan, making this imaging technique an important and low cost instrument to gain relevant information about the potential tumor. Unfortunately the efficacy of this imaging technique is very much operator-dependent and the capability of discriminating functional ovarian masses from malignant ovarian tumor depends a lot on the doctor’s experience. Therefore computer-aided examination could help to reduce the gap in performance between doctors having different level of experience.