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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 11347
School of Cognitive Sciences
  Title:   Three-dimensional coupled-object segmentation using symmetry and tissue type information
1.  Payam Bahman Bijari
2.  Alireza Akhondi Asl
3.  Hamid Soltanianzadeh
  Status:   Published
  Journal: Computerized Medical Imaging and Graphics
  Vol.:  34
  Year:  2010
  Pages:   236-249
  Supported by:  IPM
This paper presents an automatic method for segmentation of brain structures using their symmetry and tissue type information. The proposed method generates segmented structures that have homogenous tissues. It benefits from general symmetry of the brain structures in the two hemispheres. It also benefits from the tissue regions generated by fuzzy c-means clustering. All in all, the proposed method can be described as a dynamic knowledge-based method that eliminates the need for statistical shape models of the structures while generating accurate segmentation results. The proposed approach is implemented in MATLAB and tested on the Internet Brain Segmentation Repository (IBSR) datasets. To this end, it is applied to the segmentation of caudate and ventricles three-dimensionally in magnetic resonance images (MRI) of the brain. Impacts of each of the steps of the proposed approach are demonstrated through experiments. It is shown that the proposed method generates accurate segmentation results that are insensitive to initialization and parameter selection. The proposed method is compared to four previous methods illustrating advantages and limitations of each method.

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