Automatic ROI detection and classification of the Achilles tendon ultrasound images

Ultrasound (US) imaging plays an important role in medical imaging technologies. It is widely used because of its ease of use and low cost compared to other imaging techniques. Specifically, ultrasound imaging is used in the detection of the Achilles Tendon (AT) pathologies as it detects important details. For example, US imaging is used for AT rupture that affects about 1 in 5,000 people worldwide. Decision support systems are important in medical imaging, as they assist radiologist in detecting probable diagnoses and lesions. The work presented in this paper concerns the development of a software application to detect changes in the AT ultrasound images and subsequently classify them into normal or abnormal. We propose an approach that fully automates the detection for the Region of Interest (ROI) in ultrasound AT images. The original image is divided into six blocks with 1 cm size in each direction. The blocks lie inside the vulnerable area considered as our ROI. The proposed system achieved an accuracy of 97.21%