Region detection by minimizing intraclass variance with geometric constraints, global optimality, and efficient approximation

Xiaodong Wu; Xin Dou; Andreas Wahle; Milan Sonka

doi:10.1109/TMI.2010.2095870

Back

Region detection by minimizing intraclass variance with geometric constraints, global optimality, and efficient approximation

Journal article

Open access

Peer reviewed

Region detection by minimizing intraclass variance with geometric constraints, global optimality, and efficient approximation

Xiaodong Wu, Xin Dou, Andreas Wahle and Milan Sonka

IEEE transactions on medical imaging, Vol.30(3), pp.814-827

03/2011

DOI: 10.1109/TMI.2010.2095870

PMCID: PMC3131164

PMID: 21118766

Files and links (1)

url

https://www.ncbi.nlm.nih.gov/pmc/articles/3131164View

Open Access

Abstract

Efficient segmentation of globally optimal surfaces in volumetric images is a central problem in many medical image analysis applications. Intraclass variance has been successfully utilized for object segmentation, for instance, in the Chan-Vese model, especially for images without prominent edges. In this paper, we study the optimization problem of detecting a region (volume) between two coupled smooth surfaces by minimizing the intraclass variance using an efficient polynomial-time algorithm. Our algorithm is based on the shape probing technique in computational geometry and computes a sequence of minimum-cost closed sets in a derived parametric graph. The method has been validated on computer-synthetic volumetric images and in X-ray CT-scanned datasets of plexiglas tubes of known sizes. Its applicability to clinical data sets was also demonstrated. In all cases, the approach yielded highly accurate results. We believe that the developed technique is of interest on its own. We expect that it can shed some light on solving other important optimization problems arising in medical imaging. Furthermore, we report an approximation algorithm which runs much faster than the exact algorithm while yielding highly comparable segmentation accuracy.

Reproducibility of Results

Algorithms

Radiographic Image Interpretation, Computer-Assisted - methods

Humans

Sensitivity and Specificity

Radiographic Image Enhancement - methods

Tomography, X-Ray Computed - methods

Pattern Recognition, Automated - methods

Details

Title: Subtitle: Region detection by minimizing intraclass variance with geometric constraints, global optimality, and efficient approximation
Creators: Xiaodong Wu - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA. xiaodong-wu@uiowa.edu
Xin Dou
Andreas Wahle
Milan Sonka
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.30(3), pp.814-827
DOI: 10.1109/TMI.2010.2095870
PMID: 21118766
PMCID: PMC3131164
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers; United States
Grant note: R01 HL071809 / NHLBI NIH HHS R01-EB004640 / NIBIB NIH HHS R01 EB004640 / NIBIB NIH HHS K25 CA123112 / NCI NIH HHS K25-CA123112 / NCI NIH HHS R01 HL071809-04 / NHLBI NIH HHS R01-HL64368 / NHLBI NIH HHS K25 CA123112-01A1 / NCI NIH HHS R01 HL064368-10 / NHLBI NIH HHS R01 EB004640-06 / NIBIB NIH HHS R01-HL071809 / NHLBI NIH HHS R01 HL064368 / NHLBI NIH HHS
Language: English
Date published: 03/2011
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984047681402771

Metrics

26 Record Views

8 Times Cited - Web of Science