Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

Saif Dawood Salman Al-shaikhli (Corresponding Author), Michael Ying Yang, Bodo Rosenhahn

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)


Medical image segmentation and anatomical structure labeling according to the types of the tissues are important for accurate diagnosis and therapy. In this paper, we propose a novel approach for multi-region labeling and segmentation, which is based on a topological graph prior and the topological information of an atlas, using a modified multi-level set energy minimization method in brain images. We consider a topological graph prior and atlas information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level in low resolution brain image that would be difficult to segment correctly using standard methods. The topological information of an atlas are transformed to the topological graph of a low resolution (noisy) brain image to obtain region labeling. We explain our algorithm and show the topological graph prior and label transformation techniques to explain how it gives precise multi-region segmentation and labeling. The proposed algorithm is capable of segmenting and labeling different regions in noisy or low resolution MRI brain images of different modalities. We compare our approaches with other state-of-the-art approaches for multi-region labeling and segmentation.
Original languageEnglish
Pages (from-to)725-734
Number of pages10
JournalComputerized medical imaging and graphics
Issue number8
Early online date23 Jun 2014
Publication statusPublished - 1 Dec 2014
Externally publishedYes




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