Shubhajoy Das,Debashis Das,



Magnetic Resonance Imaging,K-means algorithm,Genetic Algorithms,Ant Colony Optimization ,Image segmentation,unsupervised classification,support vector machine,Medical Image processing,


The main purpose of segmentation is to partition an image based on features into different regions. Unsupervised classification algorithms K means, K-nearest neighbor, neural networks can be used to perform efficient image segmentation. Image segmentation is an important step to perform classification of images. Segmentation algorithms such as watershed segmentation, support vector machines can be used to find the region of interest. A genetic algorithm based image segmentation algorithm, ant colony optimization algorithm is proposed and we compare it with k-means segmentation. We apply some segmentation algorithms in industry standard datasets and view the results of our segmentation algorithms. Segmentation is a basic task in image processing and can be applied in large number of domains. We emphasize on how a segmentation algorithm can be developed to segment out tum ours from medical magnetic resonance images. We have used the open CV python package for our image processing tasks.


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