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Improving statistics for hybrid segmentation of high-resolution multichannel images

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

Title:
Improving statistics for hybrid segmentation of high-resolution multichannel images
Author(s):
Angelini, Elsa D.
Imielinska, Celina Z.
Jin, Yinpeng
Laine, Andrew F.
Date:
Type:
Articles
Department:
Biomedical Engineering
Permanent URL:
Book/Journal Title:
Medical imaging 2002 : Image processing : 24-28 February 2002, San Diego, USA ; Proceedings of SPIE, vol. 4684
Book Author:
Sonka, Milan
Publisher:
SPIE
Publisher Location:
Bellingham, Wash.
Abstract:
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.
Subject(s):
Biomedical engineering
Publisher DOI:
10.1117/12.467182
Item views:
283
Metadata:
text | xml

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