NDLI: Computer Aided Detection for breast calcification clusters based on improved instance selection and an adaptive neuro-fuzzy network

Content Provider	IEEE Xplore Digital Library
Author	Xiao-dong Wang Jun Feng Yao-lin Li Zhan Li Qiu-ping Wang
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. & Technol., Northwest Univ., Xi'an, China (Xiao-dong Wang; Jun Feng; Yao-lin Li; Zhan Li) \|\| Med. Coll., Xi'an JiaoTong Univ., Xi'an, China (Qiu-ping Wang)
Abstract	In this paper, we propose an novel instance selection algorithm and an improved adaptive neuro-fuzzy algorithm for Computer Aided Detection (CAD) of mammography. Firstly, the X-Ray images are partitioned into blocks. Secondly, the texture model is built for all negative packages instances. The distances from the unknown instances to the average model of negative packages are calculated. The instance with the fastest distance is selected as the suspicious area. Afterwards, the main features of suspicious regions are extracted for classification. Specifically, we propose to use an adaptive neuro-fuzzy classification Linguistic hedge (ANFC-LH) algorithm for CAD. The experimental results show that this method not only has the ability to automatically extract Regions of Interest (ROI), but also can greatly reduce the computation time while keeping the detection performance. At the same time, the better accuracy rate and true positive rate are achieved compared with traditional methods.
Sponsorship	IEEE Circuits Syst. Soc.
Starting Page	184
Ending Page	189
File Size	862816
Page Count	6
File Format	PDF
ISBN	9781467352536
DOI	10.1109/FSKD.2013.6816190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Solid modeling Adaptive Neuro-fuzzy Instance Selection Breast Statistical Modeling Feature extraction Educational institutions Classification algorithms Lesions Region of Suspicious
Content Type	Text
Resource Type	Article

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