Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55592
Title: New hierarchical approach for microaneurysms detection with matched filter and machine learning
Authors: Wu, J
Xin, J
Hong, L
You, J 
Zheng, N
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, 25-29 August 2015, 7319351, p. 4322-4325 How to cite?
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.
URI: http://hdl.handle.net/10397/55592
ISBN: 9781424492718
ISSN: 1557-170X
DOI: 10.1109/EMBC.2015.7319351
Appears in Collections:Conference Paper

SFX Query Show full item record

SCOPUSTM   
Citations

5
Last Week
0
Last month
Citations as of Dec 9, 2017

Page view(s)

69
Last Week
0
Last month
Citations as of Dec 17, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.