Paper
3 July 2001 Extraction of the contours of left ventricular cavity according with those traced by medical doctors from left ventriculograms using a neural edge detector
Kenji Suzuki, Isao Horiba, Noboru Sugie, Michio Nanki
Author Affiliations +
Abstract
In this paper, we have proposed a novel edge detector using a multilayer neural network, called the neural edge detector (NED), and a new contour-extraction method using the NED to extract the contours according with those traced by medical doctors. The NED is a supervised edge detector: through training the NED with a set of input images and desired edges, it acquires the function of a desired edge detector. The proposed contour-extraction method consists of (a) edge detection using the NED, (b) extraction of rough contours based on band-pass filtering, and (c) contour tracking based on the candidates for the contours synthesized from the edges detected by the NED and the rough contours. The experiments to extract the contours of left ventricular cavity from left ventriculograms were performed. By comparative evaluation with the conventional edge detectors, it has been shown that the NED has the highest performance. Through the experiments to evaluate the performance of contour extraction, the following has been demonstrated: The proposed method can extract the contours according with those traced by medical specialists; The performance of the proposed method is higher than that of the conventional method; The proposed method has the about same ability of medical specialists.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenji Suzuki, Isao Horiba, Noboru Sugie, and Michio Nanki "Extraction of the contours of left ventricular cavity according with those traced by medical doctors from left ventriculograms using a neural edge detector", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431006
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Cited by 1 scholarly publication and 1 patent.
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KEYWORDS
Sensors

Field emission displays

Edge detection

Edge roughness

Neural networks

Image enhancement

Medical imaging

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