Novel stochastic framework for accurate segmentation of prostate in dynamic contrast enhanced MRI

Ahmad Firjany, Ahmed Elnakib, Ayman El-Baz, Georgy Gimel'farb, Mohamed Abo El-Ghar, Adel Elmagharby

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Dynamic Contrast Enhancement Magnetic Resonance Images (DCE-MRI). In this paper we propose a novel approach for segmenting the prostate region from DCE-MRI based on using a graph cut framework to optimize a new energy function consists of three descriptors: (i) 1st -order visual appearance descriptors of the DCE-MRI; (ii) a spatially invariant 2nd -order homogeneity descriptor, and (iii) a prostate shape descriptor. The shape prior is learned from a subset of co-aligned training images. The visual appearances are described with marginal gray level distributions obtained by separating their mixture over the image. The spatial interactions between the prostate pixels are modeled by a 2nd -order translation and rotation invariant Markov-Gibbs random field of object / background labels with analytically estimated potentials. Experiments with prostate DCE-MR images confirm robustness and accuracy of the proposed approach.

Original languageEnglish (US)
Title of host publicationProstate Cancer Imaging
Subtitle of host publicationComputer-Aided Diagnosis, Prognosis, and Intervention - International Workshop Held in Conjunction with MICCAI 2010, Proceedings
Pages121-130
Number of pages10
DOIs
StatePublished - 2010
EventInternational Workshop on Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention Held in Conjunction with MICCAI 2010 - Beijing, China
Duration: Sep 24 2010Sep 24 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention Held in Conjunction with MICCAI 2010
Country/TerritoryChina
CityBeijing
Period9/24/109/24/10

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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