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%

Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment

The number density of scatterers in tumor tissue contribute to a heterogeneous ultrasound speckle pattern that can be difficult to discern by visual observation. Such tumor stochastic behavior becomes even more challenging if the tumor texture heterogeneity itself is investigated for changes related to response to chemotherapy treatment. Here we define a new tumor texture heterogeneity model for evaluating response to treatment. The characterization of the speckle patterns is performed via state-of-the-art multi-orientation and multi-scale circular harmonic wavelet (CHW) frames analysis of the envelope of the radio-frequency signal. The lacunarity measure—corresponding to scatterer number density—is then derived from fractal dimension texture maps within the CHW decomposition, leading to a localized quantitative assessment of tumor texture heterogeneity. Results indicate that evaluating tumor heterogeneity in a multidimensional texture analysis approach could potentially impact on designing an early and effective chemotherapy treatment