Fuzzy Clustering Techniques For Image Segmentation Using Microscopic Images
Keywords:
Image Threshold, Median Filter, Contrast Stretching, Histogram Equalizations, Morphological Operations, Fuzzy C-Means Clustering, Fuzzy K-Means Clustering, Peak Signal Noise Ratio, Mean Absolute ErrorAbstract
The bacteria image segmentation of microscope slide imaging is an important and one of the challenging tasks in biomedical image processing. The key contribution of this work is based on algorithm and was executed step by step. The pre-processing technique is adaptive threshold and median filter for noise removal, contrast stretching and morphological operations. This are applied after Fuzzy C-means and Fuzzy K-means algorithms. Fuzzy C-means clustering algorithm used to segment the foreground object and segment the background object using Fuzzy K-means clustering. The Clustering is a major method used for grouping of mathematical and image data in data mining and image processing applications. Clustering makes the job of image recovery easy by finding the images as similar as given in the query image. The simulation result shows the PSNR, MAE metrics used comparing resultant of segment images. The comparison between the results of traditional as well as the proposed methods shows that the proposed method yields better results both in visual perception as well as in quantitative analysis.
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