GridSnake: A Grid-based implementation of the Snake segmentation algorithm

Medical imaging is becoming a key technique to visualize the internal structure of the body. Magnetic resonance imaging (MRI) is currently used to take different spatial images of organs, such as the heart. The output of such an analysis is a set of images representing different views of the body or of an organ. There exist many algorithms to pre-process and analyze medical images such as the well known Snake segmentation algorithm. An issue in medical imaging is the large size of images, that require large and efficient data stores, and the high computational power needed to process them. For these reasons, the Grid is being more and more used as an ideal environment for medical image processing. This paper presents a first experience in porting the Snake algorithm on a Globus-based Grid.

[1]  A. Herment,et al.  An automated myocardial segmentation in cardiac MRI , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Marcus Schreckenberg,et al.  High-resolution transthoracic real-time three-dimensional echocardiography: quantitation of cardiac volumes and function using semi-automatic border detection and comparison with cardiac magnetic resonance imaging. , 2004, Journal of the American College of Cardiology.

[3]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[4]  Peter Brezany,et al.  A Roadmap for caGrid, an Enterprise Grid Architecture for Biomedical Research , 2008, HealthGrid.

[5]  Mario Cannataro,et al.  The knowledge grid , 2003, CACM.

[6]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[7]  Marie-Pierre Jolly,et al.  Combining Edge, Region, and Shape Information to Segment the Left Ventricle in Cardiac MR Images , 2001, MICCAI.

[8]  J. Raymond Geis.  Medical Imaging Informatics: How It Improves Radiology Practice Today , 2007, Journal of Digital Imaging.

[9]  Thomas Tolxdorff,et al.  DICOM Image Communication in Globus-Based Medical Grids , 2008, IEEE Transactions on Information Technology in Biomedicine.

[10]  Hermann Ney,et al.  Automatic categorization of medical images for content-based retrieval and data mining. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  Hakan Bulu,et al.  Comparison of 3D Segmentation Algorithms for Medical Imaging , 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

[12]  Rodrigo Weber dos Santos,et al.  Automatic Segmentation of Cardiac MRI Using Snakes and Genetic Algorithms , 2008, ICCS.

[13]  Bernard Gibaud,et al.  THE DICOM STANDARD: A BRIEF OVERVIEW , 2008 .

[14]  A. Rahmouni,et al.  Segmentation of cardiac cine-MR images and myocardial deformation assessment using level set methods. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[15]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[16]  Daniel Rueckert,et al.  Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm , 2003, MICCAI.

[17]  W. Gaasch,et al.  Assessment of left ventricular diastolic function and recognition of diastolic heart failure. , 2007, Circulation.