A neural network-based stochastic active contour model (NNS-SNAKE) for contour finding of distinct features

Author

Chiou, Greg I. ; Hwang, Jenq-Neng

Author_Institution

Boeing Comput. Services, Seattle, WA, USA

Volume

4

Issue

10

fYear

1995

fDate

10/1/1995 12:00:00 AM

Firstpage

1407

Lastpage

1416

Abstract

Contour finding of distinct features in 2-D/3-D images is essential for image analysis and computer vision. To overcome the potential problems associated with existing contour finding algorithms, we propose a framework, called the neural network-based stochastic active contour model (NNS-SNAKE), which integrates a neural network classifier for systematic knowledge building, an active contour model (also known as the “Snake”) for automated contour finding using energy functions, and the Gibbs sampler to help the snake to find the most probable contour using a stochastic decision mechanism. Successful application of the NNS-SNAKE to extraction of several types of contours on magnetic resonance (MR) images is presented

Keywords

biomedical NMR; computer vision; feature extraction; image sampling; medical image processing; neural nets; stochastic processes; 2-D images; 3-D images; Gibbs sampler; MRI; NNS-SNAKE; Snake; automated contour finding; computer vision; contour finding; contour finding algorithms; distinct features; energy functions; image analysis; magnetic resonance images; neural network classifier; neural network stochastic active contour model; stochastic decision mechanism; systematic knowledge; Active contours; Computer vision; Helium; Image edge detection; Magnetic resonance; Neural networks; Shape; Stochastic processes; Stochastic systems; Tracking;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.465105

Filename

465105