Segmentation Results through FCM clustering with MATLAB program

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SEGMENTATION RESULTS THROUGH FCM CLUSTERING

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Notes

• The thresholding factor of FCM is varied and segmentation outputs are observed for different values of “Thresholding factor”.

• Jaccard index is calculated for each segmentation output with respect to the provided “Ground truth image”.

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Sample 1

GROUND TRUTH IMAGE

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FCM SEGMENTATION RESULTS

THRESHOLDING FACTOR U1(V)>0.4

JACCARD INDEX = 0.1362


JACCARD INDEX=0.2859

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SAMPLE 2

GROUND TRUTH IMAGE

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FCM SEGMENTATION RESULTS



JACCARD INDEX= 0.2498


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SAMPLE 3

GROUND TRUTH IMAGE

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FCM SEGMENTED RESULTS





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JACCARD INDEX=0

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SAMPLE 4

GROUND TRUTH IMAGE

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SAMPLE 5

GROUND TRUTH IMAGE

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Program for Segmentation and Jaccard Index Calculation

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Program(continued)

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Observations

• The Ideal threshold value for best result is different for different sample images.

• The Image has few unwanted features like shadows, hair, other little impressions etc., which have appeared in our “Segmentation Outputs ”.

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Observations(continued)

• Preprocessing the image before segmenting it using FCM clustering algorithm is required to improve the Jaccard Index.

• The Jaccard Index is a better similarity measure compared to spatial overlap index as it compares only the white regions of the images i.e., see sample 3, result 4.

Engineering

Segmentation Results through FCM clustering with MATLAB program