Comparative Analysis of Fuzzy C- Means and K-Means Clustering in the Case of Image Segmentation

Image segmentation is an essential step in image processing and analysis. Neural Network, Fuzzy C-Means and K-Means clustering techniques are very famous segmentation techniques. Neural Network is best for segmentation but only classifies trained images. For every image, we do not apply the neural network. Further, we check which other two types of segment techniques are best. For this purpose, we apply these two types of segment techniques on different images. The dice and bfscore performance were estimated for these algorithms and found that if the image is less complex, then Fuzzy C-Means gives better result. If the image is more complex, then K-Means clustering gives better result. In the case of medical data, K - Means clustering is fast.