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. 2017 Nov 1;5(1):14.
doi: 10.1007/s13755-017-0034-9. eCollection 2017 Dec.

A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm

Umit Budak  1 , Abdulkadir Şengür  2 , Yanhui Guo  3 , Yaman Akbulut  2
Affiliations

A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm

Umit Budak et al. Health Inf Sci Syst. .

Abstract

Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection. To this end, green channel decomposition, Gaussian filtering, median filtering, back ground determination, and subtraction operations are applied to input colored fundus images. After pre-processing, a candidate MAs extraction procedure is applied to detect potential regions. A five-stepped procedure is adopted to get the potential MA locations. Finally, deep convolutional neural network (DCNN) with reinforcement sample learning strategy is used to train the proposed system. The DCNN is trained with color image patches which are collected from ground-truth MA locations and non-MA locations. We conducted extensive experiments on ROC dataset to evaluate of our proposal. The results are encouraging.

Keywords: Color fundus images; Deep convolutional neural network; Diabetic retinopathy; Microaneurysms detection; Reinforcement sample learning strategy.

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Figures

Fig. 1
Fig. 1
Proposed MAs detection scheme
Fig. 2
Fig. 2
Spiral sequence of gray scale values
Fig. 3
Fig. 3
Segmentation of the spiral sequence of gray scale values
Fig. 4
Fig. 4
a Inew1 image, b Inew2 image
Fig. 5
Fig. 5
Thresholded images. a Inew1 image, b Inew2 image
Fig. 6
Fig. 6
a Detected vessel, b detected candidate MAs
Fig. 7
Fig. 7
Sample patches for MAs
Fig. 8
Fig. 8
Sample patches for non-MAs
Fig. 9
Fig. 9
Detected candidate MAs, red circles show the ground-truth MA locations
Fig. 10
Fig. 10
DCNN classification results, blue circles show the detected MAs and red circles show the ground-truth
Fig. 11
Fig. 11
The FROC curves of proposed method and compared methods
See this image and copyright information in PMC

References

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