SEGMENTATION OF MR BRAIN TUMOR USING PARALLEL ANT COLONY OPTIMIZATION ALGORITHM

     One of the most complex tasks in digital image processing is image segmentation. The recent revolution in medical imaging resulting from techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) can provide detailed information about the disease and can identify many pathologic conditions, giving an accurate diagnosis. Methods based on statistical frameworks can be further divided into non-parametric methods or parametric methods. In non-parametric methods, the density model of the prior relies entirely on the data itself, i.e. the K-nearest neighbors (K-NN) method. Non-parametric methods are adaptive, but its limitation is the need for a large amount of labeled training data. In contrast, non-parametric methods rely only on the explicit functional form of the intensity density function of the MR image. This design-based image segmentation algorithm that uses a biologically inspired technique based on Ant Colony Optimization (ACO) algorithm. The meta-heuristic algorithm operates on the image pixel data and a region/ neighborhood map to form a context in which they can merge. Hence, we segment the MR brain image using parallel ant colony optimization algorithm. Simulation results show that the design method has better results and it can effectively segment the fine details. The suggested image segmentation strategy is tested on a set of MR Brain images by changing the level of image segmentation and iterations.

Reference Paper: Segmentation of MR Brain tumor using Parallel ACO

Author’s Name: J.Jaya and K.Thanushkodi

Source: International Journal of Computer and Network Security

Year:2010

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SIMULATION VIDEO DEMO                                                                                                                                     



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