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Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study.

Wu H, Zhang X, Geng X, Dong J, Zhou G - BMC Ophthalmol (2014)

Bottom Line: For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification.After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05).In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Medical School of Nantong University, Nantong 226001, China. dongjc@ntu.edu.cn.

ABSTRACT

Background: Detection of retinal lesions like micro-aneurysms and exudates are important for the clinical diagnosis of diabetes retinopathy. The traditional subjective judgments by clinicians are dependent on their experience and can be subject to lack of consistency and therefore a quantification method is worthwhile.

Methods: In this study, 10 moderate non-proliferative diabetes retinopathy (NPDR) patients and 10 severe NPDR ones were retrospectively selected as a cohort. Mathematical morphological methods were used for automatic segmentation of lesions. For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification. For micro-aneurysms detection, the images were pre-processed by top-hat and bottom-hat transformation, then Otsu method and Hough transform were used to classify micro-aneurysms. Post-processing morphological methods were used to preclude the false positive noise.

Results: After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05).

Conclusions: In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

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Related in: MedlinePlus

Hough detection results in 2D, 3D coordinates and overlapped in segmented image. (5a: left panel; 5b: middle panel; 5c: right panel).
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Fig5: Hough detection results in 2D, 3D coordinates and overlapped in segmented image. (5a: left panel; 5b: middle panel; 5c: right panel).

Mentions: For Hough detection, the highest peaks are corresponded to a particular radius in the accumulator data (Figure 5a). If the height of the peaks is equal compared to the number of edge pixels for a circle with the particular radius, the coordinates of the peaks does probably correspond to the center of such a circle (Figure 5b). In this study, we identified the number of microaneurysms automatically and overlapped the results in segmented retinal images (Figure 5c).Figure 3


Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study.

Wu H, Zhang X, Geng X, Dong J, Zhou G - BMC Ophthalmol (2014)

Hough detection results in 2D, 3D coordinates and overlapped in segmented image. (5a: left panel; 5b: middle panel; 5c: right panel).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4232650&req=5

Fig5: Hough detection results in 2D, 3D coordinates and overlapped in segmented image. (5a: left panel; 5b: middle panel; 5c: right panel).
Mentions: For Hough detection, the highest peaks are corresponded to a particular radius in the accumulator data (Figure 5a). If the height of the peaks is equal compared to the number of edge pixels for a circle with the particular radius, the coordinates of the peaks does probably correspond to the center of such a circle (Figure 5b). In this study, we identified the number of microaneurysms automatically and overlapped the results in segmented retinal images (Figure 5c).Figure 3

Bottom Line: For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification.After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05).In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Informatics, Medical School of Nantong University, Nantong 226001, China. dongjc@ntu.edu.cn.

ABSTRACT

Background: Detection of retinal lesions like micro-aneurysms and exudates are important for the clinical diagnosis of diabetes retinopathy. The traditional subjective judgments by clinicians are dependent on their experience and can be subject to lack of consistency and therefore a quantification method is worthwhile.

Methods: In this study, 10 moderate non-proliferative diabetes retinopathy (NPDR) patients and 10 severe NPDR ones were retrospectively selected as a cohort. Mathematical morphological methods were used for automatic segmentation of lesions. For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification. For micro-aneurysms detection, the images were pre-processed by top-hat and bottom-hat transformation, then Otsu method and Hough transform were used to classify micro-aneurysms. Post-processing morphological methods were used to preclude the false positive noise.

Results: After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05).

Conclusions: In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

Show MeSH
Related in: MedlinePlus