The present medical scenario though it is very trustworthy and reliable because of the latest medical imaging modalities like MRI and CT etc., manual segmentation is prominently used in early malignancy detection, which is time-consuming. So there is a need for the automated method in terms of both accuracy and time requirement, which will provide better insight to the medical expert. Nowadays the machine learning plays an important role in this aspect since it learns through experience. The purpose of this paper is to demonstrate and make comparison of neural network with a traditional approach. We have used computational phantom data set to be considered as medical images and compare the performance of neural network with traditional segmentation by using dice similarity coefficient. The result concludes that even noise increases in medical images, the neural network approach gives high dsc value than traditional techniques.

Medical imaging, Sheep Logan phantom dataset, Fuzzy C-means, neural network

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