Academic Commons

Articles

Improving statistics for hybrid segmentation of high-resolution multichannel images

Angelini, Elsa D.; Imielinska, Celina Z.; Jin, Yinpeng; Laine, Andrew F.

High-resolution multichannel textures are difficult to characterize with simple statistics and the high level of detail makes the selection of a particular contour using classical gradient-based methods not effective. We have developed a hybrid method that combines fuzzy connectedness and Voronoi diagram classification for the segmentation of color and multichannel objects. The multi-step classification process relies on homogeneity measures derived from moment statistics and histogram information. These color features have been optimized to best combine individual channel information in the classification process. The segmentation initialization requires only a set of interior and exterior seed points, minimizing user intervention and the influence of the initialization on the overall quality of the results. The method was tested on volumes from the Visible Human and on brain multi-protocol MRI data sets. The hybrid segmentation produced robust, rapid and finely detailed contours with good visual accuracy. The addition of quantized statistics and color histogram distances as classification features improved the robustness of the method with regards to initialization when compared to our original implementation.

Files

Also Published In

Title
Medical imaging 2002 : Image processing : 24-28 February 2002, San Diego, USA ; Proceedings of SPIE, vol. 4684
DOI
https://doi.org/10.1117/12.467182

More About This Work

Academic Units
Biomedical Engineering
Publisher
SPIE
Published Here
August 25, 2010
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.