Early detection of cardiac affections is fundamental to address a correct treatment that allows preserving the patient’s life. Since heart disease is one of the main causes of death in most countries, analysis of cardiac images is of great value for cardiac assessment. Cardiac MR has become essential for heart evaluation. In this work we present a segmentation framework for shape analysis in cardiac magnetic resonance (MR) images. The method consists of an active contour model which is guided by the spectral coefficients obtained from the Hermite transform (HT) of the data. The HT is used as model to code image features of the analyzed images. Region and boundary based energies are coded using the zero and first order coefficients. An additional shape constraint based on an elliptical function is used for controlling the active contour deformations. The proposed framework is applied to the segmentation of the endocardial and epicardial boundaries of the left ventricle using MR images with short axis view. The segmentation is sequential for both regions: the endocardium is segmented followed by the epicardium. The algorithm is evaluated with several MR images at different phases of the cardiac cycle demonstrating the effectiveness of the proposed method. Several metrics are used for performance evaluation.
Variational approaches based on level set representation have become some of the most important methodologies used to handle the segmentation tasks of biological structures in medical images. Because the segmentation is one of the most challenging processes in medical applications, all the methods fail to achieve perfect results. The major problems are due to noise, poor contrast and high variation of the structure shapes. In this paper, we review the principal level set – based methods that have been designed for image segmentation applications. These approaches include: Geodesic Active Contour, Chan-Vese Functional and Geodesic Active Regions. We also shortly analyze the first method proposed for shape extraction in images by using level set representation. We make a comparative study of the performance obtained for each method applied on cardiac CT images which present strong and very marked differences about the contrast and shape variation. Left ventricle is selected as structure of analysis. Measures of similarity are used to evaluate the performance of the methods.
Considering the importance of studying the movement of certain cardiac structures such as left ventricle and myocardial wall for better medical diagnosis, we propose a method for motion estimation and image segmentation in sequential Computed Tomography images. Two main tasks are tackled. The first one consists of a method to estimate the heart's motion based on a bio-inspired image representation model. Our proposal for optical flow estimation incorporates image structure information extracted from the steered Hermite transform coefficients that is later used as local motion constraints in a differential estimation approach. The second task deals with cardiac structure segmentation in time series of cardiac images based on deformable models. The goal is to extend active shape models (ASM) of 2D objects to the problem of 3D (2D + time) cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that encodes the spatio-temporal variability of a training set. Combination of both motion estimation and image segmentation allows isolating motion in cardiac structures of medical interest such as ventricle walls.
This paper describes a segmentation method for time series of 3D cardiac images based on deformable models. The goal
of this work is to extend active shape models (ASM) of
tree-dimensional objects to the problem of 4D (3D + time)
cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that
encodes the spatio-temporal variability of a training set, i.e., the principal modes of variation of the temporal shapes are
computed using some statistical parameters. An active search is used in the segmentation process where an initial
approximation of the spatio-temporal shape is given and the gray level information in the neighborhood of the landmarks
is analyzed. The starting shape is able to deform so as to better fit the data, but in the range allowed by the point
distribution model. Several time series consisting of eleven 3D images of cardiac CT are employed for the method
validation. Results are compared with manual segmentation made by an expert. The proposed application can be used
for clinical evaluation of the left ventricle mechanical function. Likewise, the results can be taken as the first step of
processing for optic flow estimation algorithms.