Record Details

A high-throughput active contour scheme for segmentation of histopathological imagery

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title A high-throughput active contour scheme for segmentation of histopathological imagery
 
Creator XU, J
JANOWCZYK, A
CHANDRAN, S
MADABHUSHI, A
 
Subject LEVEL SET APPROACH
PROSTATE-CANCER
UNSUPERVISED SEGMENTATION
NORMALIZED CUTS
NUCLEI
MODELS
REGIONS
SNAKES
High-throughput system
Active contour model
Segmentation
Prostate cancer
Digital pathology
 
Description In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies-mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: (1) efficiency in segmenting multiple target structures; (2) the ability to segment multiple objects from very large images; (3) minimal human interaction; (4) accuracy; and (5) reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios-Deriche) and a region-based AC model (Rousson-Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications. (C) 2011 Elsevier B.V. All rights reserved.
 
Publisher ELSEVIER SCIENCE BV
 
Date 2012-06-26T07:38:39Z
2012-06-26T07:38:39Z
2011
 
Type Article
 
Identifier MEDICAL IMAGE ANALYSIS,15(6)851-862
1361-8415
http://dx.doi.org/10.1016/j.media.2011.04.002
http://dspace.library.iitb.ac.in/jspui/handle/100/14156
 
Language English