Automatic detection of anatomical landmarks of the aorta in CTA images.

Computed tomography angiography (CTA) is one of the most common vascular imaging modalities. However, for clinical use, it still requires laborious manual analysis. This study demonstrates the feasibility of a fully automated technology for the accurate detection and identification of several anatomical reference points (landmarks), commonly used in intravascular imaging.

Automatic estimation of the aortic lumen geometry by ellipse tracking.

Purpose: The shape and size of the aortic lumen can be associated with several aortic diseases. Automated computer segmentation can provide a mechanism for extracting the main features of the aorta that may be used as a diagnostic aid for physicians. This article presents a new fully automated algorithm to extract the aorta geometry for either normal (with and without contrast) or abnormal computed tomography (CT) cases.

Computer vision techniques for breast tumor ultrasound analysis.

In this paper, we present a new approach to the segmentation and analysis of solid breast nodules in ultrasonography. We have applied computer vision techniques to segment the nodules and analyze a series of diagnostic criteria which can help discriminate malignant and benignant tumors. The segmentation is carried out in a semiautomatic way, whereas the analysis of the diagnostic criteria involves several computational methods.