JESS: Java extensible snakes system

Tim McInerney; M. Reza Akhavan Sharif; Nasrin Pashotanizadeh

doi:10.1117/12.594574

29 April 2005 JESS: Java extensible snakes system

Tim McInerney, M. Reza Akhavan Sharif, Nasrin Pashotanizadeh

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.594574
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Snakes (Active Contour Models) are powerful model-based image segmentation tools. Although researchers have proven them especially useful in medical image analysis over the past decade, Snakes have remained primarily in the academic world and they have not become widely used in clinical practice or widely available in commercial packages. A number of confusing and specialized variants exist and there has been no standard open-source implementation available. To address this problem, we present a Java Extensible Snakes System (JESS) that is general, portable, and extensible. The system uses Java Swing classes to allow for the rapid development of custom graphical user interfaces (GUI's). It also incorporates the Java Advanced Imaging (JAI) class library, which provide custom image preprocessing, image display and general image I/O. The Snakes algorithm itself is written in a hierarchical fashion, consisting of a general Snake class and several subclasses that span the main variants of Snakes including a new, powerful, robust subdivision-curve Snake. These subclasses can be easily and quickly extended and customized for any specific segmentation and analysis task. We demonstrate the utility of these classes for segmenting various anatomical structures from 2D medical images. We also demonstrate the effectiveness of JESS by using it to rapidly build a prototype semi-automatic sperm analysis system. The JESS software will be made publicly available in early 2005.

Citation Download Citation

Tim McInerney, M. Reza Akhavan Sharif, and Nasrin Pashotanizadeh "JESS: Java extensible snakes system", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594574

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