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4.A Appendix: Measures of Binary Test Performance

Test result

 

Disease

 

Present

Absent

 

 

 

pos.

a

b

neg.

c

d

 

 

 

True positives Correct positive test result: a = number of diseased persons with a positive test result.

True negatives Correct negative test results: d = number of nondiseased persons with a negative test result.

False positives Incorrect positive test result: b =number of nondiseased persons with a positive test result.

False negatives Incorrect negative test result: c = number of diseased persons with a negative test result.

Sensitivity (a=(a + c)) proportion of people with the target disorder who have a positive test result. Known as true positive fraction (TPF).

Specificity (d=(b + d)) proportion of people without the target disorder who have a negative test result. False positive fraction, FPF = (1 specificity).

Positive predictive value (PPV) the probability of disease among all persons with a positive test result. PPV = a=(a + b).

Negative predictive value (NPV) the probability of nondisease among all persons with a negative test result. NPV = d=(c + d).

5

Retinal Markers for Early Detection of Eye Disease

Alireza Osareh

CONTENTS

5.1

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

121

5.2

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

122

5.3

Nonproliferative Diabetic Retinopathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

123

5.4

Chapter Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

124

5.5

Related Works on Identification of Retinal Exudates and the Optic Disc . . . .

128

5.6

Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

132

5.7

Pixel-Level Exudate Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

134

5.8

Application of Pixel-Level Exudate Recognition on the Whole Retinal Image

137

5.9

Locating the Optic Disc in Retinal Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

139

5.10

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

148

 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

150

5.1Abstract

Diabetic retinopathy (DR) is a severe and widely spread eye disease that can be regarded as manifestation of diabetes on the retina. Screening to detect retinopathy disease can lead to successful treatments in preventing visual loss. Intraretinal fatty (hard) exudates are a visible sign of diabetic retinopathy and also a marker for the presence of coexistent retinal edema.

Detecting retinal exudate lesions in a large number of images generated by screening programs is very expensive in professional time and open to human error. Thus, we explore the benefits of developing an automated decision support system for the purpose of detecting and classifying exudate pathologies of diabetic retinopathy. The retinal images are automatically analyzed in terms of pixel resolution and an assessment of the level of retinopathy is derived.

Following some key preprocessing steps, color retinal image pixels are classified to exudate and nonexudate classes. K nearest neighbor, Gaussian quadratic, and

121