Epicardial boundary detection using fuzzy reasoning

Abstract

A fully automated system for detecting the endocardial and epicardial boundaries in a two-dimensional echocardiography by using fuzzy reasoning techniques is proposed. The image is first enhanced by applying the Laplacian-of-Gaussian edge detector. Second, the center of the left ventricle is determined automatically by analyzing the original image. Next, a search process radiated from the estimated center is performed to locate the endocardial boundary by using the zero-crossing points. After this step, the estimation of the range of radius of a possible epicardial boundary is carried out by comparing the high-level knowledge of intensity changes along all directions with the actual image intensity changes. The high-level knowledge of global intensity change in the image is acquired from experts in advance, and is represented in the form of fuzzy linguistic descriptions and relations. Knowledge of local intensity change can therefore be deduced from the knowledge of global intensity change through fuzzy reasoning.

Maximum likelihood segmentation of ultrasound images with Rayleigh distribution

“…Several approaches have been reported in the literature for automated or semiautomated border detection from ultrasound data, based on different methods: statistical Markov random fields [1]- [5], multidimensional spacefrequency methods [6], [7], fuzzy logic [8]- [10], neural networks [11], [12], active contours models (snakes) [13]. The classical active contour models are based on the evolution of a curve attracted by image boundaries in order to detect objects.…”

LV volume quantification via spatiotemporal analysis of real-time 3-D echocardiography

“…We define a snake as the space of admissible deformations that minimize the functional (17) with and weighting parameters that control respectively the elasticity and rigidity of the snake and the potential of external forces, derived from the image edges. Let be a local minimum of , the associated Euler-Lagrange equation verified for is for some (18) The first two terms represent the internal force that impose regularity to the curve, and the last term represents the potential of the external force that attracts the curve to features of interest.…”

“…Several approaches for epicardial and endocardial border 0278-0062/01$10.00 ©2001 IEEE detection from ultrasound data have been reported during the past decade with partial success. Recent studies include methods based on statistical Markov random field models [12]- [15], fuzzy logic [16], [17], neural networks [18], [19], morphological filters [18], [20], active contours, and level sets [21]- [25]. A common motivation for these efforts, which have focused on the development of new methods of volume extraction, is that existing segmentation tools are not adapted to this type of data and do not meet the accuracy requirements of clinical applications.…”

A Fourier-Shannon approach to closed contours modelling

“…The expert interpolates the endocardic contour in those areas where it is indistinguishable, relying on his knowledge of the morphology of the ventricular cavity, especially of its overall shape. Despite recent progress reported in the literature [1][2][3][4][5][6][7][8][9][10][11][12], there is no general method for automatic boundary detection that provides satisfactory results in these cases. Most of the detection methods use specific a priori information about the characteristics of the target contours to solve the detection problem.…”