The classification of uterine myoma and uterine adenomyosis based on ultrasound image features

The classification of the uterine myoma and the uterine adenomyosis from ultrasound images mainly depends on doctors experience and lacks objective criterions by now. A novel automatic classification method is proposed to improve the performance. The multiresolution analysis was done for ultrasound images of the uterine myoma and the uterine adenomyosis to obtain their texture parameters under various resolutions. Together with the orientational fractal dimension parameters, a Support Vector Machine SVM was established to classify the uterine myoma and the uterine adenomyosis. The result of the experiments, in which there were 27 normal cases, 45 adenomyosis cases and 74 myoma cases, showed that multiresolution texture parameters and orientational fractal parameters were both sensitive to the uterine myoma and the uterine adenomyosis. The classification accuracy of the myoma and the adenomyosis based on SVM with all these parameters is about 100%.