NDLI: Recovery of the 3-D shape of the left ventricle from echocardiographic images

Content Provider	IEEE Xplore Digital Library
Author	Coppini, G. Poli, R. Valli, G.
Copyright Year	1982
Abstract	A computational method is reported which allows the fully automated recovery of the three-dimensional shape of the cardiac left ventricle from a reduced set of apical echo views. Two typically ill-posed problems have been faced: 1) the detection of the left ventricle contours in each view, and 2) the integration of the detected contour points (which form a sparse and partially inconsistent data set) into a single surface representation. The authors' solution to these problems is based on a careful integration of standard computer vision algorithms with neural networks. Boundary detection comprises three steps: edge detection, edge grouping, and edge classification. The first and second steps (which are typical early-vision tasks not involving specific domain-knowledge) have been performed through fast, well-established algorithms of computer vision. The higher level task of left ventricle-edge discrimination, which involves the exploitation of specific knowledge about the left ventricle silhouette, has been performed by feedforward neural networks. Following the most recent results in the field of computer vision, the first step in solving the problem of recovering the ventricle surface has been the adoption of a physically inspired model of it. Basically, the authors have modeled the left ventricle surface as a closed, thin, elastic surface and the data as a set of radial springs acting on it. The recovery process is equivalent to the settling of the surface-plus-springs system into a stable configuration of minimum potential energy. The finite element discretization of this model leads directly to an analog neural-network implementation. The efficiency of such an implementation has been remarkably enhanced through a learning algorithm which embeds specific knowledge about the shape of the left ventricle in the network. Experiments using clinical echographic sequences are described. Four apical views (each with a different rotation of the probe) have been acquired during a heartbeat from a set of seven normal subjects. These images have been utilized to set the various processing modules and test their capabilities.<>
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Nuclear and Plasma Sciences Society IEEE Signal Processing Society IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
Starting Page	301
Ending Page	317
Page Count	17
File Size	1870962
File Format	PDF
ISSN	02780062
Volume Number	14
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Face detection Computer vision Neural networks Image edge detection Feedforward neural networks Springs Potential energy Finite element methods Lead
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Radiological and Ultrasound Technology Software

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