Shape-based filter for micro-aneurysm detection☆
Introduction
The incidence of diabetes is global and has serious health consequence for human population [1]. Diabetic retinopathy (DR) is one of the eye complications of diabetes that leads to decreased vision in sufferers and eventually to blindness. Micro-aneurysm (MA) is the first symptom of DR, which is a swell on the micro-vascular [2] caused by the thinning of vessel wall and blood pressure. Arguably, it is important to focus on techniques that detect micro-aneurysm as a precursor to the onset of hemorrhage and as an early diagnostic marker for DR. Furthermore, this early detection has significance for ophthalmological clinical diagnosis and precaution. The procedure of MA detection through color retinal imaging is usually performed by medical practitioners and is necessarily slow as well as demanding high human resource. Reasons adduced for this are the small sizes of MA and its random distribution in color retinal image. Consequently, development of automatic computer-aided screening systems for MA detection has attracted much attention from researchers.
The rest of the paper is organized as follows. In Section 2, short review of key existing methods in the literature is presented. The proposed method are described in Section 3. Experimental results and performance analysis are discussed in Section 4. Concluding remarks are offered in Section 5.
Section snippets
Related works
In general, MA has a quasi-circular or spherical structure that forms the basis of some structure-based methods. In a structure-based method, Lázár and Hajdu [3] used a local rotating cross-section profile to detect MA, in terms of the Gaussian-like distribution of MA. However, the results reported many false positives. They modified and improved this method by constructing an MA score map from different orientations [4]. A new criterion based on non-maximum suppression was proposed to detect
Proposed method
The proposed method can be divided into three modules, viz. preprocessing, candidate extraction and post processing, respectively. The details are shown in Fig. 1 depicting flowchart of the method.
Dataset description
Two databases are chosen to test and verify the performance of the proposed method. Retinopathy Online Challenge (ROC) [22] is firstly established to detect MA. The gold standard is annotated by four ophthalmologists. Then, the images from Tianjin Medical University Metabolic Diseases Hospital (Hospital) are collected, including 280 retinal images with 2180 × 2000 resolution at 50∘ field of view. Three ophthalmologists also labeled the MAs independently.
Evaluation of method and discussions
Evaluations are conducted at two
Conclusions
Micro-aneurysm is a small and dark target in retinal image. Therefore, the preprocessing algorithm of three-stage Gamma correction is used to enhance the whole lightness of the green channel image. Moreover, the local contrast of micro-aneurysm is efficient improved for post-processing. Based on the quasi-circular and Gaussian-like characteristics of micro-aneurysm, the circular bilateral Gabor filter can simultaneously improve small dark dots and suppress the surrounding complex background.
Declaration of Competing Interest
The authors declared that they have no conflicts of interest to this work We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted
Acknowledgments
This work was supported by National Natural Science Foundation of China under Grant No. 61902078.
Xinpeng Zhang received the Ph.D degree from Tianjin Polytechnic University, China. He is currently doing his Post Doctor in Guangdong University of Technology, Guangzhou, China. His areas of interest are image processing and deep learning.
References (25)
- et al.
Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images
Proceedings of the SPIE
(2009) Diagnosis and classification of diabetes mellitus
Diabetes Care
(2005)- et al.
Evaluation of retinal vessel segmentation methods for microaneurysms detection
Proceedings of IEEE international conference on image processing
(2009) - et al.
Microaneurysm detection on retinal images using a rotating cross-section based model
Proceedings of IEEE international symposium on biomedical imaging
(2011) - et al.
Retinal microaneurysm detection through local rotating cross-section profile analysis
IEEE Trans Med Imaging
(2013) - et al.
Optimal wavelet transform for the detection of microaneurysms in retina photographs
IEEE Trans Med Imaging
(2008) - et al.
Robust detection of microaneurysms for sight threatening retinopathy screening
Proceedings of sixth indian conference on computer vision, graphics and image processing, Bhubaneswar, India
(2008) - et al.
An accurate approach for microaneurysm detection in digital fundus images
Proceedings of IEEE international conference on pattern recognition, Stockholm, Sweden
(2014) - et al.
Automatic detection of red lesions in digital color fundus photographs
IEEE Trans. Med. Imaging
(2005) - et al.
Detection of microaneurysms using multi-scale correlation coefficients
Pattern Recognit.
(2010)
Automated microaneurysm detection using local contrast normalization and local vessel detection
IEEE Trans. Med. Imaging
Microaneurysm detection with Radon transform-based classification on retina images
Proceedings of IEEE international conference on engineering in medicine and biology society
Cited by (4)
Joint two-stage convolutional neural networks for intracranial aneurysms detection on 3D TOF-MRA
2023, Physics in Medicine and BiologyMicroaneurysms Detection in Color Fundus Image with Feature-based Background Suppression
2022, Proceedings - International Conference on Pattern RecognitionEarly diabetic retinopathy detection using augmented continuous particle swarm optimization clustering
2021, Assistive Technology Intervention in HealthcareA Survey on Microaneurysms Detection in Color Fundus Images
2020, 2020 2nd International Conference on Cybernetics and Intelligent System, ICORIS 2020
Xinpeng Zhang received the Ph.D degree from Tianjin Polytechnic University, China. He is currently doing his Post Doctor in Guangdong University of Technology, Guangzhou, China. His areas of interest are image processing and deep learning.
Zhitao Xiao received the Ph.D degree from Tianjin University, China. He is currently working as a Professor in the School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China. His areas of interest include image processing, pattern recognition and computer vision.
Fang Zhang received the Ph.D degree from Tianjin University, China. She is currently working as a Professor in the School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin, China. Her areas of interest are image processing, pattern recognition.
Philip O. Ogunbona received the Ph.D degree from Imperial College London, Britain. He is currently working as a Professor in the School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Australia. His areas of interest include image processing, pattern recognition and machine learning.
Jiangtao Xi received the Ph.D degree from University of Wollongong, Australia. He is currently working as a Professor in the School of Computing and Information Technology, University of Wollongong, Australia. His areas of interest are digital signal processing and electrical engineering.
Jun Tong received the Ph.D degree from City University of Hong Kong, HongKong. He is currently working as a Professor in the School of Computing and Information Technology, University of Wollongong, Australia. His areas of interest are signal processing.
- ☆
This paper is for CAEE special section SI-cih. Reviews processed and recommended for publication to the Editor-in-Chief by Guest Editor Dr. Hamed Vahdat-Nejad.