Left ventricle analysis in echocardiographic images using transfer learning.

The segmentation of cardiac boundaries, specifically Left Ventricle (LV) segmentation in 2D echocardiographic images, is a critical step in LV segmentation and cardiac function assessment. These images are generally of poor quality and present low contrast, making daily clinical delineation difficult, time-consuming, and often inaccurate. Thus, it is necessary to design an intelligent automatic endocardium segmentation system.

Classification of Mammographic ROI for Microcalcification Detection Using Multifractal Approach.

Microcalcifications (MCs) are the main signs of precancerous cells. The development of aided-system for their detection has become a challenge for researchers in this field. In this paper, we propose a system for MCs detection based on the multifractal approach that classifies mammographic ROIs into normal (healthy) or abnormal ROIs containing MCs.

Development and clinical validation of a hybrid method for semiautomated left ventricle endocardial and epicardial boundary extraction on cine-magnetic resonance images.

We describe a hybrid method for left ventricle (LV) endocardial and epicardial segmentation on cardiac magnetic resonance (CMR) images requiring minimal operator intervention. Endocardium extraction results from the union of three independent estimations based on adaptive thresholding, region growing, and active contour with Chan-Vese energy function. Epicardium segmentation relies on conditional morphological dilation of the endocardial mask followed by active contour optimization.