Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images

다국어 초록 (Multilingual Abstract)

Objectives: Glaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, thenumber of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statisticalfeatures and the k-nearest neighbor algorithm as the classifier. Methods: We propose three statistical features, namely,the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features areobtained through feature extraction followed by feature selection using the correlation feature selection method. To classifythose features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images.
Results: To evaluate the performance of the proposed method, 84 fundus images were used as experimental data consistingof 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy,and the overall result achieved in this work was 95.24%, respectively. Conclusions: This research showed that the proposedmethod using three statistics features achieves good performance for glaucoma detection

참고문헌 (Reference)

1 Quigley HA, "The number of people with glaucoma worldwide in 2010 and 2020" 90 (90): 262-267, 2006

2 Septiarini A, "The contour extraction of cup in fundus images for glaucoma detection" 6 (6): 2797-2804, 2016

3 김태연, "The Recent Progress in Quantitative Medical Image Analysis for Computer Aided Diagnosis Systems" 대한의료정보학회 17 (17): 143-149, 2011

4 Kotsiantis SB, "Supervised machine learning: a review of classification techniques" 31 : 249-268, 2007

5 Cheng J, "Superpixel classification based optic disc and optic cup segmentation for glaucoma screening" 32 (32): 1019-1032, 2013

6 Karthikeyan S, "Performance analysis of gray level cooccurrence matrix texture features for glaucoma diagnosis" 11 (11): 248-257, 2014

7 Septiarini A, "Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation" 11 (11): 945-952, 2017

8 Marin D, "Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images" 118 (118): 173-185, 2015

9 Quigley HA, "Number of people with glaucoma worldwide" 80 (80): 389-393, 1996

10 Ashraf M, "Neural Information Processing" Springer 272-280, 2012

11 Murthi A, "Medical decision support system to identify glaucoma using cup to disc ratio" 68 (68): 406-413, 2014

12 Wiharto Wiharto, "Interpretation of Clinical Data Based on C4.5 Algorithm for the Diagnosis of Coronary Heart Disease" 대한의료정보학회 22 (22): 186-195, 2016

13 Singh A, "Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image" 124 : 108-120, 2016

14 Yousefi S, "Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points" 61 (61): 1143-1154, 2014

15 Ali MA, "Glaucoma detection based on local binary patterns in fundus photographs" 2014

16 Khalid NE, "Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation" 42 : 255-262, 2014

17 Hall MA, "Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper" 235-239, 1999

18 Mookiah MR, "Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features" 33 : 73-82, 2012

19 Annu N, "Classification of glaucoma images using wavelet based energy features and PCA" 4 (4): 1369-1374, 2013

20 Sheshadri HS, "Breast tissue classification using statistical feature extraction of mammograms" 23 (23): 105-107, 2006

21 Septiarini A, "Automatic glaucoma detection based on the type of features used : a review" 72 (72): 366-375, 2015

22 Muramatsu C, "Automated segmentation of optic disc region on retinal fundus photographs: comparison of contour modeling and pixel classification methods" 101 (101): 23-32, 2011

23 Aruchamy S, "Automated glaucoma screening in retinal fundus images" 10 (10): 129-136, 2015

24 Dey A, "Automated glaucoma detection using support vector machine classification method" 11 (11): 1-12, 2016

25 Salam AA, "Automated detection of glaucoma using structural and non structural features" 5 (5): 1519-, 2016

26 Noronha KP, "Automated classification of glaucoma stages using higher order cumulant features" 10 (10): 174-183, 2014

27 Gamero GE, "Atlas of glaucoma" CRC Press 2007

28 Vanaja S, "Analysis of feature selection algorithms on classification : a survey" 96 (96): 28-35, 2014

29 Mary MC, "An empirical study on optic disc segmentation using an active contour model" 18 : 19-29, 2015

30 Issac A, "An adaptive threshold based image processing technique for improved glaucoma detection and classification" 122 (122): 229-244, 2015

31 Saeys Y, "A review of feature selection techniques in bioinformatics" 23 (23): 2507-2517, 2007

32 Welfer D, "A morphologic two-stage approach for automated optic disk detection in color eye fundus images" 34 (34): 476-485, 2013