- Research article
- Open Access
- Open Peer Review
Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study
© Wu et al.; licensee BioMed Central Ltd. 2014
- Received: 30 October 2013
- Accepted: 20 October 2014
- Published: 31 October 2014
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.
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.
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.
- Diabetic retinopathy
- Fundus lesions
Early detection of eye disease due to diabetes, glaucoma, and age-related macular degeneration has a significant impact on the prevention of blindness. It’s estimated that nearly one million patients would be screened every day worldwide for diabetic retinopathy(DR) by 2025. Detecting and counting lesions in the human retina like microaneurysms and exudates is important for clinical diagnosis of DR [1, 2], but is also a time-consuming task for ophthalmologists and open to human error. That is why much effort has been done to detect lesions in the human retina automatically. In this study, we proposed a computerized framework for automatic detection of exudate and microaneurysms and compared the morphological features in moderate and severe non-proliferative diabetic retinopathy.
Dataset selection and preparation
During November 2012 to October 2013, 20 patients diagnosed with DR by fundus image but without cataract degeneration, optic disk edema, macular degeneration, retinal vessel obstruction which could affect retinal images were included in our study. For these patients, 10 patients (7 males and 3 females, mean age: 60.8 ± 11.0 years old) were graded as moderate non-proliferative diabetes retinopathy (NPDR), and another 10 ones (6 males and 4 females, mean age: 63.2 ± 12.1) were graded as severe NPDR according to the international classification for NPDR . All the patients had been diagnosed with diabetes, with the disease course ranged from 8 to 16 years, mean age: 9.4 ± 4.9 years old. All the fundus images were obtained with the same 45° field of view (FOV) camera, with the macula at the center. The image acquisition conditions were consistent at 3504 × 2336 pixels. The patients accompanied with hypertension were excluded. The study protocol was conducted in accordance with the ethical guidelines of the 1995 Declaration of Helsinki. This study was approved by Ethics Committee of Nantong University. Informed consent was obtained from all patients. Before next processing, the size of tested images was reduced in size to 685 × 584 pixels and calibrated to prove the measurement of pathological changes comparable.
For exudates detection, different approaches have been proposed. We firstly adopted traditional threshold classification protocol to coarsely classify the background and exudate areas. Then, morphological operators including erosion and dilation were performed on segmented binary image to exclude the noise. To prevent the influence of pixel calibration, the area of total exudates were divided by the area of optic disk and this value was called exudates/disk ratio.
in which b is a structure element.
in which , , , n i denotes the number of pixels with gray value i. Then, morphological post-processing steps were performed on the binary logic image to detect microaneurysms.
The other protocol used in this study for microaneurysm detection is based on Hough transform. Hough detection is a useful method to detect line and circular features in images, which transforming the pixels in original image to parameter coordinate . In this way, linear arranged pixels with the same slope and intercept are shown as the same pixel in the parameter coordinates. Compared to a line, a circle is actually simpler to represent in parameter space, since the parameters of the circle can be directly transformed into the parameter space as x = a + rcos(θ), y = b + rsin(θ). And the location of circle center from the accumulator data could be determined in the parameter space represents.
In this experiment, the exudates/disk ratio and number of microaneurysms were expressed as x - ± SD. Student T test was used to compare the area of exudates and number of microaneurysms. The P value less than 0.05 was treated as statistically significant.
In this study, the number of microaneurysms in the moderate group were 14.5 ± 2.3; while was 27.5 ± 5.7 in the severe group, with significant difference (P < 0.05).
In clinical practices, fundus image observation could help clinicians to detect the microcirculation changes in vivo. However, such observation was usually dependant on the observers’ experience. Although some guidelines for qualification of lesions in four quadrants of retinal image, the description of different amounts of aneurysms and exudates increases clinicians’ work loads. Nowadays, different standards for DR have been published , morphological features of lesions are commonly mentioned parameters for disease severity grading. For any automatic detection and screening system developed for retinal illnesses, the detection of morphological structures such as the optic disc, macula, and vessels is extremely important. For retinal image analysis, a robust automatic algorithm could be applied to quantify the retinal vessel width, exudates, hemorrhage, microaneurysms, relieving the work load by ophthalmologists. Besides, such quantification data could be stored and distributed easily, thus significant for diagnosis and prognosis research compared with the simply observational experience made by clinicians. Some systems have been developed to determine DR stages—normal, mild moderate NPDR, severe NPDR and PDR stages [6–9]. In our further study, we plan to implement blood vessel geometric features for comparative analysis.
The computer framework used in our study was mainly based on mathematical morphological analysis, from segmentation and measurement, which is different from machine learning methods that train the classifier based on textual or intensity features and output the classified result as grades [10–14]. The latter way is a system-oriented framework that could determine the result, but lack of number values data output. Such number values are essential in clinical investigation as an image marker for diagnosis and prognosis. Meanwhile, such un-supervised segmentation technique could work without training samples and therefore could be applied into fundus image directly, which provides convenience for clinical practices. Besides applications in DR, morphologic structures, retinal parameters, and changes in retinal features are utilized in the automatic detection of retinal pathologies such as age-related macular degeneration (ARMD), glaucoma, and optic nerve hypoplasia (ONH) [15–17].
There are also some weaknesses for our computer framework in clinical practice. Taking microaneurysms’ detection for example, if no apriori knowledge is known about the size of microaneurysms then this process could be quite challenging. And the center of a circle can also be represented by a peak with a height less than the number of edge pixels, the incomplete or ellipse shaped circle will increase the segmented errors. Another limitation in our study is that the variation of FOV from different digital fundus cameras could affect the exudates/disk ratio because of different observation areas are investigated.
In conclusion, a computer framework based on mathematical morphological analysis could be utilized as a reference for clinicians to quantify exudates and microaneurysms in NPDR, and larger exudate area and more aneurysm counts might help grading the NPDR, larger samples and prospective clinical trials are needed for further validation.
This work was supported by the grant of National Natural Science Foundation of China (No. 81271668).
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