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Proceedings Paper

Automatic brain cropping enhancement using active contours initialized by a PCNN
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Paper Abstract

Active contours are a popular medical image segmentation strategy. However in practice, its accuracy is dependent on the initialization of the process. The PCNN (Pulse Coupled Neural Network) algorithm developed by Eckhorn to model the observed synchronization of neural assemblies in small mammals such as cats allows for segmenting regions of similar intensity but it lacks a convergence criterion. In this paper we report a novel PCNN based strategy to initialize the zero level contour for automatic brain cropping of T2 weighted MRI image volumes of Long-Evans rats. Individual 2D anatomy slices of the rat brain volume were processed by means of a PCNN and a surrogate image 'signature' was constructed for each slice. By employing a previously trained artificial neural network (ANN) an approximate PCNN iteration (binary mask) was selected. This mask was then used to initialize a region based active contour model to crop the brain region. We tested this hybrid algorithm on 30 rat brain (256*256*12) volumes and compared the results against manually cropped gold standard. The Dice and Jaccard similarity indices were used for numerical evaluation of the proposed hybrid model. The highly successful system yielded an average of 0.97 and 0.94 respectively.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594I (27 March 2009); doi: 10.1117/12.811636
Show Author Affiliations
Murali Murugavel Swathanthira Kumar, Worcester Polytechnic Institute (United States)
John M. Sullivan Jr., Worcester Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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