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Zonal segmentation of prostate using multispectral magnetic resonance images
Author(s) -
Makni N.,
Iancu A.,
Colot O.,
Puech P.,
Mordon S.,
Betrouni N.
Publication year - 2011
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3651610
Subject(s) - magnetic resonance imaging , segmentation , context (archaeology) , multispectral image , computer science , pattern recognition (psychology) , artificial intelligence , similarity (geometry) , sørensen–dice coefficient , image segmentation , cluster analysis , medical imaging , prostate , a priori and a posteriori , computer vision , medicine , radiology , image (mathematics) , geology , paleontology , philosophy , epistemology , cancer
Purpose: To investigate the performance of a new method of automatic segmentation of prostatic multispectral magnetic resonance images into two zones: the peripheral zone and the central gland.Methods: The proposed method is based on a modified version of the evidential C‐means clustering algorithm. The evidential C‐means optimization process was modified to introduce spatial neighborhood information. A priori knowledge of the prostate's zonal morphology was modeled as a geometric criterion and used as an additional data source to enhance the differentiation of the two zones.Results: Thirty‐one clinical magnetic resonance imaging series were used to validate the method, and interobserver variability was taken into account in assessing its accuracy. The mean Dice Similarity Coefficient was 89% for the central gland and 80% for the peripheral zone, as validated by a consensus from expert radiologist segmentation.Conclusions: The method was statistically insensitive to variations in patient age, prostate volume and the presence of tumors, which increases its feasibility in a clinical context.