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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