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Table 1 Segmentation sensitivity for, respectively, the clustering result expressed in each color space and the fusion result given by our algorithm for the dataset shown in Figure 4

From: Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

  Sensitivity segmentation (%)
  R H Y X A L Fusion
Image 1 0.9770 0.8796 0.9582 0.9618 0.9695 0.9719 0.9959
Image 2 0.9364 0.8609 0.9409 0.9563 0.9339 0.9641 0.9944
Image 3 0.9359 0.8743 0.9583 0.9600 0.9395 0.9658 0.9879
Image 4 0.9536 0.8722 0.9711 0.9713 0.9441 0.9676 0.9927
Image 5 0.9147 0.8747 0.9466 0.9498 0.9166 0.9671 0.9926
Image 6 0.9577 0.8571 0.9649 0.9669 0.9398 0.9685 0.9898
Image 7 0.9487 0.8642 0.9658 0.9661 0.9359 0.9682 0.9831
Image 8 0.9785 0.8726 0.9767 0.9776 0.9652 0.9734 0.9982
Image 9 0.9770 0.9295 0.9541 0.9593 0.9619 0.9754 0.9912
Image 10 0.8576 0.9918 0.9975 0.9972 0.9943 0.9952 0.9991
Image 11 0.9865 0.9971 0.9975 0.9977 0.9858 0.9748 0.9982
Image 12 0.9578 0.9666 0.8297 0.8065 0.8096 0.9385 0.9881