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Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness.

The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.

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The segmentation of the heart is usually demanded in the clinical practice for computing functional parameters in patients, such as ejection fraction, cardiac output, peak ejection rate, or filling rate. Because of the time required, the manual delineation is typically limited to the left ventricle at the end-diastolic and end-systolic phases, which is insufficient for computing some of these parameters (e.g., peak ejection rate or filling rate). Common computer-aided (semi-)automated approaches for the segmentation task are computationally demanding, and an initialization step is frequently needed. This work is intended to address the aforementioned problems by providing an image-driven method for the accurate segmentation of the heart from computed tomography scans. The resulting algorithm is fast and fully automatic (even the region of interest is delimited without human intervention). The proposed methodology relies on image processing and analysis techniques (such as multi-thresholding based on statistical local and global parameters, mathematical morphology, and image filtering) and also on prior knowledge about the cardiac structures involved. Segmentation results are validated through the comparison with manually delineated ground truth, both qualitatively (no noticeable errors found after visual inspection) and quantitatively (mass overlapping over 90%).