BRAIN TUMOR DETECTION USING ACO
With technology advancement in medical imaging resulting from techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) can provide detailed information about disease and can identify many pathologic conditions, giving an accurate diagnosis. Several methods have been proposed to segment brain MR images. The methods are either based on the intrinsic structure of the data or based on statistical frameworks. Structural based methods rely on apparent spatial regularities of image structures such as edges, and regions.
In this Matlab design image segmentation algorithm that uses a biologically inspired technique based on ant colony optimization (ACO) algorithm. First image acquisition followed by pre-processing to remove film artifacts. Enhancement stage is to improve the visual appearance of Magnetic Resonance Image (MRI) and removal of high frequency components from the images. Finally, Segmentation using parallel Ant Colony Optimization (ACO) System where, the labels created from the MRF method and the posterior energy function values for each pixel are stored in a solution matrix. The goal of this method is to find out the optimum label of the image that minimizes the posterior energy function value. Initially assign the values of number of iterations (N), number of ants (K), initial pheromone value (T0). To validate the image segmentation strategy is tested on a set of MR Brain images as shown in the simulation video demo.
REFERENCES
Reference Paper-1: Segmentation of MR Brain tumor using Parallel ACO
Author’s Name: J.Jaya and K.Thanushkodi
Source: IJCNS
Year: 2010
You can DOWNLOAD the Matlab code to execute the design.
SIMULATION VIDEO DEMO
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