Segmentation of diabetic macular edema in OCT retinal images

He Jintao; Chen Minghui; Jia Wenyu; Qin Xianfu; Wang Cheng; Zheng Gang

doi:10.12086/oee.2018.170605

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He Jintao, Chen Minghui, Jia Wenyu, et al. Segmentation of diabetic macular edema in OCT retinal images[J]. Opto-Electronic Engineering, 2018, 45(7): 170605. doi: 10.12086/oee.2018.170605

Citation:

He Jintao, Chen Minghui, Jia Wenyu, et al. Segmentation of diabetic macular edema in OCT retinal images[J]. Opto-Electronic Engineering, 2018, 45(7): 170605. doi: 10.12086/oee.2018.170605

Segmentation of diabetic macular edema in OCT retinal images

Institute of Biomedical Optics & Optometry, Shanghai Institute for Minimally Invasive Therapy, University of Shanghai for Science and Technology, Shanghai 200093, China

Fund Project: Supported by Project supported by the National Science Foundation for Young Scientists of China (61308115), Shanghai Municipal Natural Science Foundation (13ZR1457900), and Industrial Technology and medical Research Funds of Shanghai (15DZ1940400)

More Information

^*Corresponding author: Chen Minghui, E-mail: cmhui.43@163.com

Received Date 08 November 2017

Revised Date 15 March 2018

Published Date 01 July 2018

Abstract

Abstract

Diabetic macular edema (DME) is one of the important reasons leading to blindness. Its pathological features are mainly manifested in the accumulation of fluid in the retina. A method for segmentation of diabetic macular edema in optical coherence tomography (OCT) retinal images is proposed. Firstly, through the image preprocessing, we exclude the impact of speckle noise and blood vessels on the final segmentation results. We used the improved level set method to solve the problem of segmentation effectively and calculated the area of edema area, which provides quantitative analytic tools for clinical diagnosis and therapy. Finally, we validated the method in this study on 15 OCT retina images with DME adults. The precision, sensitivity and dice similarity coefficient (DSC) for DME segmentation are 81.12%, 86.90% and 80.05%, respectively.
- diabetic macular edema /
- optical coherence tomography /
- level set /
- image segmentation

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References

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Overview

Overview

Overview: Diabetic macular edema (DME) is one of the important reasons leading to blindness, and the incidence of diabetic retinopathy is usually associated with macular edema symptoms. Its pathological features are mainly manifested in the accumulation of fluid in the retina. The ophthalmologist could identify and diagnosis the cystoids edema in the optical coherence tomography (OCT) image, but it is time-consuming and labor intensive. Furthermore, it is necessary to manually mark each slice of the OCT images if the ophthalmologist wants to quantify the cystic edema. Thus, an automated algorithm is proposed to enable automatic segmentation of cystoids edema and to provide an area of cystic edema in the slice to estimate the volume. The method is divided into two steps of preprocessing and segmentation. The first step, preprocessing, we should eliminate the difference in signal-to-noise of the images. A variety of reasons would result in the difference between images, such as the images come from different patients. It would also result in intensity inhomogeneity of retinal OCT images. It would also lead to intensity inhomogeneity between each layer of the retinal in the images. Therefore, it is important to eliminate these effects. To prevent the interference of blood vessels leading to false detection, we use the gamma transformation to change the brightness, which not only removes the effects of the blood vessels but also retains the characteristics of the region of interest. For speckle noise, we remove the noise effectively by the anisotropic filtering method. We eliminate the noise of the retinal OCT images as well as preserve the edge information of each layer. The second step, segmentation, we extract the cystic edema area by improved level set function. Due to the distance regularizing term, the model is no re-initialization which overcoming the shortcomings of the traditional level set function allowing for flexible initialization of level set function. Another advantage of the method is that the Gaussian kernel function reduces the sensitivity to noise. Finally, we utilize the relationship between the number of pixels and the size of the image (an image region of 0.04 mm2 contains a set of 16 pixels×52 pixels), which calculated the area of the edema area in the OCT image quantitatively. Experiment result shows that the precision, sensitivity and dice similarity coefficient (DSC) for DME segmentation are 81.12%, 86.90% and 80.05%, respectively. It can be seen that the edema area can be divided effectively. The method provides quantitative analytic tools for clinical diagnosis and therapy.