An efficient method for deformable segmentation of 3D us prostate images

Yiqiang Zhan, Dinggang Shen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We previously proposed a deformable model for automatic and accurate segmentation of prostate boundary from 3D ultrasound (US) images by matching both prostate shapes and tissue textures in US images[6]. Textures were characterized by a Gabor filter bank and further classified by support vector machines (SVM), in order to discriminate the prostate boundary from the US images. However, the step of tissue texture characterization and classification is very slow, which impedes the future applications of the proposed approach in clinic applications. To overcome this limitation, we firstly implement it in a 3-level multi-resolution framework, and then replace the step of SVMbased tissue classification and boundary identification by a Zemike momentbased edge detector in both low and middle resolutions, for fast capturing boundary information. In the high resolution, the step of SVM-based tissue classification and boundary identification is still kept for more accurate segmentation. However, SVM is extremely slow for tissue classification as it usually needs a large number of support vectors to construct a complicated separation hypersurface, due to the high overlay of texture features of prostate and non-prostate tissues in US images. To increase the efficiency of SVM, a new SVM training method is designed by effectively reducing the number of support vectors. Experimental results show that the proposed method is 10 times faster than the previous one, yet without losing any segmentation accuracy.

Original languageEnglish
Pages (from-to)103-112
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3150
Publication statusPublished - 2004 Dec 1
Externally publishedYes

Fingerprint

Ultrasound Image
Prostate
Support Vector Machine
Segmentation
Support vector machines
Tissue
Texture
Textures
Ultrasonics
Support Vector
Deformable Models
Gabor Filter
Filter Banks
Texture Feature
3D Image
Gabor filters
Multiresolution
Overlay
Filter banks
Hypersurface

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

@article{93bb291064f041fe9ee7ae9972e85cc1,
title = "An efficient method for deformable segmentation of 3D us prostate images",
abstract = "We previously proposed a deformable model for automatic and accurate segmentation of prostate boundary from 3D ultrasound (US) images by matching both prostate shapes and tissue textures in US images[6]. Textures were characterized by a Gabor filter bank and further classified by support vector machines (SVM), in order to discriminate the prostate boundary from the US images. However, the step of tissue texture characterization and classification is very slow, which impedes the future applications of the proposed approach in clinic applications. To overcome this limitation, we firstly implement it in a 3-level multi-resolution framework, and then replace the step of SVMbased tissue classification and boundary identification by a Zemike momentbased edge detector in both low and middle resolutions, for fast capturing boundary information. In the high resolution, the step of SVM-based tissue classification and boundary identification is still kept for more accurate segmentation. However, SVM is extremely slow for tissue classification as it usually needs a large number of support vectors to construct a complicated separation hypersurface, due to the high overlay of texture features of prostate and non-prostate tissues in US images. To increase the efficiency of SVM, a new SVM training method is designed by effectively reducing the number of support vectors. Experimental results show that the proposed method is 10 times faster than the previous one, yet without losing any segmentation accuracy.",
author = "Yiqiang Zhan and Dinggang Shen",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "3150",
pages = "103--112",
journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - An efficient method for deformable segmentation of 3D us prostate images

AU - Zhan, Yiqiang

AU - Shen, Dinggang

PY - 2004/12/1

Y1 - 2004/12/1

N2 - We previously proposed a deformable model for automatic and accurate segmentation of prostate boundary from 3D ultrasound (US) images by matching both prostate shapes and tissue textures in US images[6]. Textures were characterized by a Gabor filter bank and further classified by support vector machines (SVM), in order to discriminate the prostate boundary from the US images. However, the step of tissue texture characterization and classification is very slow, which impedes the future applications of the proposed approach in clinic applications. To overcome this limitation, we firstly implement it in a 3-level multi-resolution framework, and then replace the step of SVMbased tissue classification and boundary identification by a Zemike momentbased edge detector in both low and middle resolutions, for fast capturing boundary information. In the high resolution, the step of SVM-based tissue classification and boundary identification is still kept for more accurate segmentation. However, SVM is extremely slow for tissue classification as it usually needs a large number of support vectors to construct a complicated separation hypersurface, due to the high overlay of texture features of prostate and non-prostate tissues in US images. To increase the efficiency of SVM, a new SVM training method is designed by effectively reducing the number of support vectors. Experimental results show that the proposed method is 10 times faster than the previous one, yet without losing any segmentation accuracy.

AB - We previously proposed a deformable model for automatic and accurate segmentation of prostate boundary from 3D ultrasound (US) images by matching both prostate shapes and tissue textures in US images[6]. Textures were characterized by a Gabor filter bank and further classified by support vector machines (SVM), in order to discriminate the prostate boundary from the US images. However, the step of tissue texture characterization and classification is very slow, which impedes the future applications of the proposed approach in clinic applications. To overcome this limitation, we firstly implement it in a 3-level multi-resolution framework, and then replace the step of SVMbased tissue classification and boundary identification by a Zemike momentbased edge detector in both low and middle resolutions, for fast capturing boundary information. In the high resolution, the step of SVM-based tissue classification and boundary identification is still kept for more accurate segmentation. However, SVM is extremely slow for tissue classification as it usually needs a large number of support vectors to construct a complicated separation hypersurface, due to the high overlay of texture features of prostate and non-prostate tissues in US images. To increase the efficiency of SVM, a new SVM training method is designed by effectively reducing the number of support vectors. Experimental results show that the proposed method is 10 times faster than the previous one, yet without losing any segmentation accuracy.

UR - http://www.scopus.com/inward/record.url?scp=35048812740&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048812740&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048812740

VL - 3150

SP - 103

EP - 112

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SN - 0302-9743

ER -