Edge detection in echocardiographic images based on differential tissue attenuation rates

Hammoude, Andrew

doi:10.1109/cic.1997.647908

Cited by 1 publication

(2 citation statements)

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“…For example, the 2-dimensional automated border detection (ABD) system used in the Hewlett Packard SONOS 2500 provides a detection of the LV endocardial border from the ultrasound scan plane which is highly operator-dependent 19 . In fact, the process of manual outlining and gain setting necessary in most ABD systems is time consuming, tedious, and very subjective 5,12,15 , as is indicated by the general lack of reproducibility between inter-and intra-observers. The objective of the work reported in this paper is to fully automate the procedure of extracting the LV boundary in 2-D echo sequences.…”

Section: Introductionmentioning

confidence: 99%

“…This in itself is a challenging problem because of the inherently poor quality of 2-D ultrasound images. Obstacles to accurate contour extraction are poor contrast, high levels of speckle noise, and the appearance of structural variations in the heart muscle as false edges [5][6][7] . To overcome these obstacles, the determination of LV contour is often manual or semi-automatic.…”

Section: Introductionmentioning

confidence: 99%

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<title>Template and active contour matching for automated left ventricular boundary detection</title>

et al. 2001

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This paper presents an algorithm that extracts accurate left ventricular (LV) boundaries from a two-dimensional (2-D) echocardiographic (echo) sequence covering a cardiac cycle. Unlike user-dependent, manual or semi-automatic techniques, the key feature of this algorithm is its truly automated processing for estimation. First, the algorithm performs smoothing of the image in the LV target area, followed by enhancement of intensity differences and edge detection. In order to best localize the position of the LV boundary, the algorithm uses a deformable template model derived from prior knowledge of LV shape and an edge map obtained from boundary estimation. The deformable template model is matched to the target by minimizing an energy function induced by the difference between edge locations and tangents of the template and those of the current frame edge map. Since the shape of the endocardial boundary will vary between temporally distinct frames, a controlled continuity spline, a "snake", is then used to implement refined active contour matching to the current frame LV boundary. Frame-to-frame tracking of the LV boundary is incorporated by using the boundary estimate from one frame to initialize and help with the estimation in the subsequent frame, which leads to faster and more accurate LV estimation throughout the image sequence. Test results of this algorithm show that the combination of approximate template matching with smoothness constraints in snakes produces good LV boundary extraction even with significant false and/or missing edge information caused by poor contrast and noise.

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