Paper Info
Title
Regulated morphology approach to fuzzy shape analysis with application to blood vessel extraction in thoracic CT scans
Abstract
Blood vessel segmentation in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automatic lung nodule detection in thoracic CT scans, segmented blood vessels can be used in order to resolve local ambiguities based on global considerations and so improve the performance of automated lung nodule detection algorithms. In this paper, a novel regulated morphology approach to fuzzy shape analysis is described in the context of blood vessel extraction in thoracic CT scans. Such a representation is necessary due to noise present in the data and due to the discrete nature of the volumetric data produced by CT scans, particularly the interslice spacing. Regulated morphological operations are a generalization of ordinary morphological operations which relax the extreme strictness inherent in ordinary morphological operations. Based on constraints of collinearity, size, and global direction, a tracking algorithm produces a set of connected trees representing blood vessels and nodules in the volume. The produced tree structures are composed of fuzzy spheres in which the degree of object membership is proportional to the ratio between the occupied volume and the volume of the discrete sphere encompassing it. The proposed algorithm is capable of handling bifurcations and discontinuities of blood vessels. The performance of the blood vessel extraction algorithm described in the paper is evaluated based on a distance measure between a known blood vessel structure and a reconstructed one. As the generation of synthetic data for which the true vessel network is known may not be sufficiently realistic, our evaluation is based on different versions of actual clinical data corrupted by multiplicative Gaussian noise. Preliminary results show that by using the extracted blood vessels it is possible to eliminate approximately 30% of the false positives obtained in previous work.(1)
Year
DOI
Venue
2004
10.1117/12.533175
Proceedings of SPIE
Keywords
Field
DocType
medical imaging,computer vision,image processing,mathematical morphology,regulated morphological operation,fuzzy shape representation,lung nodule detection
Computer vision,Mathematical morphology,Medical imaging,Fuzzy logic,Image processing,Synthetic data,Tree structure,Artificial intelligence,Gaussian noise,Mathematics,Shape analysis (digital geometry)
Conference
Volume
ISSN
Citations 
5370
0277-786X
12
PageRank 
References 
Authors
1.28
13
4
Name
Order
Citations
PageRank
Changhua Wu118916.89
Gady Agam239143.99
Arunabha S. Roy3211.95
Samuel G. Armato III4869.72