NDLI: New hierarchical approach for microaneurysms detection with matched filter and machine learning

Content Provider	IEEE Xplore Digital Library
Author	Jiayi Wu Jingmin Xin Lai Hong You, J. Nanning Zheng
Copyright Year	2015
Description	Author affiliation: Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China (You, J.) \|\| Sch. of Software Eng., Shenzhen Inst. of Inf. Technol., Shenzhen, China (Lai Hong) \|\| Inst. of Artificial Intell. & Robot., Xi'an Jiaotong Univ., Xi'an, China (Jiayi Wu; Jingmin Xin; Nanning Zheng)
Abstract	Microaneurysms are regarded as the first signs of diabetic retinopathy (DR), but the microaneurysms are not clear in the color retinal images, and many researches were studied to detect and locate these lesions. In this paper, a new hierarchical computing-aided diagnosis approach is proposed for the microaneurysms detection by using the multi-scale and multi-orientation sum of matched filter (MMMF) and machine learning, where 37 dimensional features are extracted from each candidate. Furthermore, several classifiers such as the k-nearest neighbor (kNN), local linear discrimination analysis (LLDA) and support vector machine (SVM) are modified to distinguish the true microaneurysms from the false ones, which is a typical unbalanced classification problem. The effectiveness of the proposed method is verified through the training set of a publicly available database, and the experiment results show that the proposed method has better detection performance including the receiver operating characteristic (ROC) curve and the free-response receiver operating characteristic (FROC) curve. Moreover, the proposed method with 37 dimensional features outperforms that with other features and has a sensitivity from 1/8 to 8 with the average of all seven points being 0.286 tested on the same database.
Starting Page	4322
Ending Page	4325
File Size	969242
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319351
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Support vector machines Retina Lesions Sensitivity Training Image color analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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