Morphological Clustering of Diagnostic Images by Advanced Hard C Means

Authors

  • Rahna Abdulla CV

DOI:

https://doi.org/10.37591/jomtra.v5i2.1742

Abstract

In the proposed approach, morphology technique is used to find the area affected in the diagnostic image  and extract the same using adaptive threshold technique. To get more accurate segmentation, Hars C means algorithm is used.

Here in this project compare and analyze the qualitative and quantitative performance of different operators. Adaptive canny operator have better performance than sobel and Roberts operator.

 By using the proposed method can improve PSNR, Noise correlation value, computational fastness, Robustness, reliability, sensitivity, specificity and accuracy etc. This algorithm not only applicable to medical field but also can be used in GIS images and Radar images for the detection of minerals ,volcanoes etc.

Keywords: Hard C-means, advanced canny operator, Sobel, Roberts

Cite this Article
Rahna Abdulla. Morphological Clustering of Diagnostic Images by Advanced Hard C-means. Journal of Multimedia
Technology & Recent Advancements. 2018; 5(2): 25–33p.

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Published

2018-09-27

Issue

Vol. 5 No. 2 (2018)

Section

Research Articles

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