Unsupervised model based image segmentation using domain knowledge based fuzzy logic and edge enhancement

Authors:
N. D. Nanayakkara;J. Samarabandu
Affiliations:
Dept. of Electr. & Comput. Eng., Univ. of Western Ontario, London, Ont., Canada;Dept. of Electr. & Comput. Eng., Univ. of Western Ontario, London, Ont., Canada
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Year:
2003

Citing 3
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge-based image understanding systems: a survey

Computer Vision and Image Understanding
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an automatic model based image segmentation system, which combines a multi-resolution discrete dynamic contour (DDC) model refinement procedure and the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining an open DDC model, followed by a contour growing process generates the closed DDC model, which deforms progressively towards higher resolution images. A combination of knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators is used to enhance the edges of interest and to govern the DDC model deformation. With the above process we were able to greatly reduce the sensitivity to the initial model, thus paving the way for automatic segmentation on noisy images. Domain knowledge of a particular class of images is encapsulated within the FIS such that it can be easily changed for different image classes. We applied this algorithm successfully to detect the organ boundary in ultra-sound images of prostates and examples are shown in order to illustrate the advantages of the proposed method.