Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images |
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| Institution: | 1. NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 117456 Singapore, Singapore;2. Department of Electrical and Computer Engineering, National University of Singapore, 117583 Singapore, Singapore;3. Siemens Corporation, Corporate Research and Technology, Princeton, NJ 08540, USA;1. BURL Concepts, Inc., San Diego, CA, USA;2. Department of Radiology, University of California San Diego, San Diego, CA, USA;3. Department of Neurosciences, University of California San Diego, San Diego, CA, USA;1. LITIS EA 4108 – QuantIF, University of Rouen, France;2. Department of Nuclear Medicine, Centre Henri-Becquerel & LITIS EA 4108 – QuantIF, France;3. LPNR, UFR-SSMT, University of Cocody, 22 BP 582 Abidjan 22, Côte d’Ivoire;1. LabHuman, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain;2. Fisabio Oftalmológica Médica, Bifurcación Pío Baroja-General Avilés, s/n, 46015 Valencia, Spain;1. INSERM, U703, 152 rue du Docteur Yersin, 59120 CHRU Lille, France;2. Université Littoral Côte d’Opale, Laboratoire d’Informatique, Signal et Image de la Côte d’Opale, France;3. Service de Radiologie, Hôpital Claude Huriez, CHRU de Lille, France |
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| Abstract: | 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. |
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| Keywords: | Segmentation Labeling Multi-region Atlas information Topological graph Multi-level set Medical image |
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