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An integrated segmentation and visualization tool for MR brain image processing

2007; SPIE; Volume: 6509; Linguagem: Inglês

10.1117/12.710457

1996-756X

Audrey H. Zhuang, Daniel J. Valentino, Val Stambolstian, Ivo D. Dinov, Arthur W. Toga,

Image Retrieval and Classification Techniques

The automated segmentation of brain structures is an important step in many neuroimaging analyses. A variety of automated segmentation tools exist, however, most segmentation results are imperfect, and require manual editing of the resulting contours or surfaces. A new, integrated segmentation and visualization tool, the LONI Anatomist, was developed to provide an open architecture for applying automated segmentation algorithms and interactive tools to manually edit the automated segmentation results. Two automated segmentation algorithms were developed to skullstrip MR brain images and were integrated in the LONI Anatomist: a two-dimensional model-based level set (2D MLS) algorithm and a three-dimensional MLS algorithm. These MLS algorithms were based on the Level Set methods by incorporating two constraints into the level set framework to evolve the zero level set surface in 2D space and 3D space respectively. In the LONI Anatomist, the evolution of the level set was displayed in real time, and final results were corrected using easy-to-use interactive editing tools. Additional tools were provided to visualize the results, such as color overlays of 2D contours over the original gray-scale slices, 3D surface visualization, etc. The LONI Anatomist was implemented in Java using a portable imaging framework (the jViewbox) for medical image display and manipulation, using the Java Image I/O plug-ins for reading/writing DICOM, MINC, ANALYZE image files, and using the Java Advanced Imaging classes for image processing. The design of the system provides a framework for researchers to integrate more mathematical algorithms for converting the algorithms into practical use.