Active contours driven by Cuckoo Search strategy for brain tumour images segmentation |
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| Affiliation: | 1. University of Guanajuato, Engineering Division, Campus Irapuato–Salamanca, Carr. Salamanca–Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, C.P. 36885 Salamanca, Guanajuato, Mexico;2. Universidad Industrial de Santander, Carrera 27 - Calle 9, C.P. 680002 Bucaramanga, Colombia;3. Department of Neurosurgery, University of Leipzig, University Hospital, Germany;4. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Germany;1. Laboratory PRISME, Orléans university, 8 Rue Léonard de Vinci - 45072 Orléans, France;2. Irstea, UMR ITAP, 361, rue J.F. Breton B.P. 5095 34196, Montpellier Cedex 5, France;1. Department of Computer Science, UNICENTRO, Guarapuava, Brazil;2. Department of Biochemistry and Molecular Biology, UFPR, Curitiba, Brazil;3. CPGEI/DAINF, UTFPR, Curitiba, Brazil;1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;2. Emergency Medicine Department, University of Michigan, Ann Arbor, MI, USA;3. Electrical and Computer Engineering Department, McMaster University, Hamilton, ON, Canada;4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;1. The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea;2. Institut National de Recherche en Informatique et en Automatique, Ecole Normale Superieure, WILLOW Team, CNRS/ENS/INRIA UMR 8548, Paris, France |
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| Abstract: | In this paper, an alternative Active Contour Model (ACM) driven by Multi-population Cuckoo Search (CS) algorithm is introduced. This strategy assists the converging of control points towards the global minimum of the energy function, unlike the traditional ACM version which is often trapped in a local minimum. In the proposed methodology, each control point is constrained in a local search window, and its energy minimisation is performed through a Cuckoo Search via Lévy flights paradigm. With respect to local search window, two shape approaches have been considered: rectangular shape and polar coordinates. Results showed that the CS method using polar coordinates is generally preferable to CS performed in rectangular shapes. Real medical and synthetic images were used to validate the proposed strategy, through three performance metrics as the Jaccard index, the Dice index and the Hausdorff distance. Applied specifically to Magnetic Resonance Imaging (MRI) images, the proposed method enables to reach better accuracy performance than the traditional ACM formulation, also known as Snakes and the use of Multi-population Particle Swarm Optimisation (PSO) algorithm. |
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