Antonio Fernando Catelli Infantosi

TL;DR: It was observed that averaging texture descriptors of a same distance impacts negatively the classification performance, while regarding the single texture features, the quantization level does not impact the discrimination power, since AUC=0.87 was obtained for the six quantization levels.

TL;DR: The findings suggest that the texture parameters can be useful to help radiologist in distinguishing between benign or malign breast tumors on ultrasound images.

TL;DR: The segmentation method proposed was capable of delineating the lesion contours with high accuracy in comparison to both the radiologists' delineations and the true delineations of simulated images and was also found to be robust to human-dependent parameters variations.

TL;DR: Different morphological and texture features widely used in computer-aided diagnosis systems for BUS images are compiled and evaluated for classifying breast lesions on ultrasound, revealing the best classification performance is obtained by a morphological set with five features.

TL;DR: The phase-based techniques appear promising for the automated detection and monitoring of somatosensory evoked potentials.