segmentation results through fcm clustering with matlab program
TRANSCRIPT
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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
THRESHOLDING FACTOR U1(V)>0.6
JACCARD INDEX=0.2859
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THRESHOLDING FACTOR U1(V)>0.8
THRESHOLDING FACTOR U1(V)>0.99
JACCARD INDEX=0.8350
JACCARD INDEX=0.2960
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SAMPLE 2
GROUND TRUTH IMAGE
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FCM SEGMENTATION RESULTS
THRESHOLDING FACTOR U1(V)>0.4
THRESHOLDING FACTOR U1(V)>0.6
JACCARD INDEX= 0.2498
JACCARD INDEX=0.2898
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THRESHOLDING FACTOR U1(V)>0.8
THRESHOLDING FACTOR U1(V)>0.99
JACCARD INDEX=0.5031
JACCARD INDEX=0.6309
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SAMPLE 3
GROUND TRUTH IMAGE
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FCM SEGMENTED RESULTS
THRESHOLDING FACTOR U1(V)>0.4
THRESHOLDING FACTOR U1(V)>0.6
JACCARD INDEX=0.3750
JACCARD INDEX=0.4443
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THRESHOLDING FACTOR U1(V)>0.8
THRESHOLDING FACTOR U1(V)>0.99
JACCARD INDEX=0.0929
JACCARD INDEX=0
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SAMPLE 4
GROUND TRUTH IMAGE
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FCM SEGMENTED RESULTS
THRESHOLDING FACTOR U1(V)>0.4
THRESHOLDING FACTOR U1(V)>0.6
JACCARD INDEX=0.3817
JACCARD INDEX=0.7398
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THRESHOLDING FACTOR U1(V)>0.8
THRESHOLDING FACTOR U1(V)>0.99
JACCARD INDEX=0.8120
JACCARD INDEX=0.1684
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SAMPLE 5
GROUND TRUTH IMAGE
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FCM SEGMENTED RESULTS
THRESHOLDING FACTOR U1(V)>0.4
THRESHOLDING FACTOR U1(V)>0.6
JACCARD INDEX=0.3801
JACCARD INDEX=0.7525
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THRESHOLDING FACTOR U1(V)>0.8
THRESHOLDING FACTOR U1(V)>0.99
JACCARD INDEX=0.6881
JACCARD INDEX=0.0298
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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.