Paper Info
Title
TRUS image segmentation driven by narrow band contrast pattern using shape space embedded level sets
Abstract
Prostate segmentation in transrectal ultrasound (TRUS) images is highly desired in many clinical applications. However, manual segmentation is difficult, time consuming and irreproducible. In this paper, we present a novel automatic approach using narrow band contrast pattern to segment prostates in TRUS images. Implicit representation of the segmenting level sets curve is firstly trained via principal component analysis, which also constraints the shape of prostate into a linear subspace. Then the model evolves to segment the prostate by maximizing the contrast in a narrow band near the segmenting curve. Many experimental results demonstrate the performance of the proposed algorithm, whose favorableness is validated by comparing to the state-of-the-art algorithms. Especially, the shape of prostate segmented by our algorithm is close to the one manually obtained by expert, and the mean absolute distance is only 1.07±0.77mm, which is quite promising.
Year
DOI
Venue
2012
10.1007/978-3-642-36669-7_42
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
narrow band,state-of-the-art algorithm,prostate segmentation,manual segmentation,trus image segmentation,narrow band contrast pattern,segment prostate,shape space embedded level,clinical application,proposed algorithm,trus image
Shape space,Computer vision,Pattern recognition,Segmentation,Prostate segmentation,Level set,Linear subspace,Image segmentation,Artificial intelligence,Narrow band,Principal component analysis,Mathematics
Conference
Volume
Issue
ISSN
7751 LNCS
null
16113349
Citations 
PageRank 
References 
1
0.35
11
Authors
4
Name
Order
Citations
PageRank
Pengfei Wu1256.14
Yiguang Liu233837.15
Yongzhong Li3242.10
Liping Cao4716.47