Performance evaluation on segmentation methods for medical images

This paper presents the performance evaluation of different segmentation algorithms for medical images. Accuracy and clarity are very important issues for medical imaging and same in the case with segmentation. In this paper, we have proposed a level set method without reinitialization with some specific shapes for segmentation and compared our proposed approach for segmentation with three other approaches where the first approach is based on region splitting based region growing category, second is based on region merging based region growing category and third is based on level set. This comparison is based on six different performance parameters namely Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Maximum Difference (MD), Normalized Cross Correlation (NCC), Normalized Absolute Error (NAE) and Structural Content (SC). We have compared the proposed approach with three above mentioned approaches for several images, out of which results for four images are provided in this paper, and we find that the approach is better than the other one.

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