Automatic brain cropping enhancement using active contours initialized by a PCNN

Murali Murugavel Swathanthira Kumar; John M. Sullivan Jr.

doi:10.1117/12.811636

27 March 2009 Automatic brain cropping enhancement using active contours initialized by a PCNN

Murali Murugavel Swathanthira Kumar, John M. Sullivan Jr.

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72594I (2009) https://doi.org/10.1117/12.811636
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Murali Murugavel Swathanthira Kumar and John M. Sullivan Jr. "Automatic brain cropping enhancement using active contours initialized by a PCNN", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594I (27 March 2009); https://doi.org/10.1117/12.811636

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