Color Image Segmentation for Objects of Interest with Modified Geodesic Active Contour Method

Authors:
Ling Pi;Jinsong Fan;Chaomin Shen
Affiliations:
Department of Mathematics, Shanghai Jiao Tong University, Shanghai, China;Aff2 Aff3;Joint Laboratory for Imaging Science & Technology/ Department of Computer Science, East China Normal University, Shanghai, China
Venue:
Journal of Mathematical Imaging and Vision
Year:
2007

Citing 11
Cited 4

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Image selective smoothing and edge detection by nonlinear diffusion. II

SIAM Journal on Numerical Analysis
Active shape models—their training and application

Computer Vision and Image Understanding
Geodesic Active Contours

International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
Fast Implicit Active Contour Models

Proceedings of the 24th DAGM Symposium on Pattern Recognition
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Anisotropic diffusion of multivalued images with applications to color filtering

IEEE Transactions on Image Processing
A general framework for low level vision

IEEE Transactions on Image Processing
Active contours without edges

IEEE Transactions on Image Processing
Fast geodesic active contours

IEEE Transactions on Image Processing

Fast edge integration based active contours for color images

Computers and Electrical Engineering
Geometric active contours without re-initialization for image segmentation

Pattern Recognition
A geometric active contour model without re-initialization for color images

Image and Vision Computing
A new automatic image analysis method for assessing estrogen receptors' status in breast tissue specimens

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a novel variational method for color image segmentation using modified geodesic active contour method. Our goal is to detect Object(s) of Interest (OOI) from a given color image, regardless of other objects. The main novelty of our method is that we modify the stopping function in the functional of usual geodesic active contour method so that the new stopping function is coupled by a discrimination function of OOI. By minimizing the functional, the OOI is segmented. Firstly, we study the pixel properties of the OOI by sample pixels visually chosen from OOI. From these sample pixels, by the principal component analysis and interval estimation, the discrimination function of whether a pixel is in the OOI is obtained probabilistically. Then we propose the energy functional for the segmentation of OOI with new stopping function. Unlike usual stopping functions defined by the image gradient, our improved stopping function depends on not only the image gradient but also the discrimination function derived from the color information of OOI. As a result, better than usual active contour methods which detect all objects in the image, our modified active contour method can detect OOI but without unwanted objects. Experiments are conducted in both synthetic and natural images. The result shows that our algorithm is very efficient for detecting OOI even the background is complicated.