Hippocampus Segmentation using a Local Prior Model on its Boundary


Segmentation techniques based on Active Contour Models have been strongly benefited from the use of prior information during their evolution. Shape prior information is captured from a training set and is introduced in the optimization procedure to restrict the evolution into allowable shapes. In this way, the evolution converges onto regions even with weak boundaries. Although significant effort has been devoted on different ways of capturing and analyzing prior information, very little thought has been devoted on the way of combining image information with prior information. This paper focuses on a more natural way of incorporating the prior information in the level set framework. For proof of concept the method is applied on hippocampus segmentation in T1-MR images. Hippocampus segmentation is a very challenging task, due to the multivariate surrounding region and the missing boundary with the neighboring amygdala, whose intensities are identical. The proposed method, mimics the human segmentation way and thus shows enhancements in the segmentation accuracy.

  • D. Zarpalas, A. Zafeiropoulos, P. Daras, N. Maglaveras, "Hippocampus Segmentation using a Local Prior Model on its Boundary", International Conference on Machine Vision, Image Processing, and Pattern Analysis (ICMVIPPA 2011), Venice, Italy, November 28-30, 2011.

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    Contact Information

    Dr. Petros Daras, Principal Researcher Grade Α
    1st km Thermi – Panorama, 57001, Thessaloniki, Greece
    P.O.Box: 60361
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    Email: daras@iti.gr