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Proceedings Paper

Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images
Author(s): Sergei V. Fotin; Yin Yin; Senthil Periaswamy; Justin Kunz; Hrishikesh Haldankar; Naira Muradyan; François Cornud; Baris Turkbey; Peter L. Choyke
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Paper Abstract

Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 ± 0.33 mm and 3.10 ± 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

Paper Details

Date Published: 14 February 2012
PDF: 11 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140V (14 February 2012); doi: 10.1117/12.911620
Show Author Affiliations
Sergei V. Fotin, iCAD, Inc. (United States)
Yin Yin, iCAD, Inc. (United States)
Senthil Periaswamy, iCAD, Inc. (United States)
Justin Kunz, iCAD, Inc. (United States)
Hrishikesh Haldankar, iCAD, Inc. (United States)
Naira Muradyan, iCAD, Inc. (United States)
François Cornud, Ctr. d'Imagerie Medicale Tourville (France)
Baris Turkbey, National Cancer Institute (United States)
Peter L. Choyke, National Cancer Institute (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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