Multiple Classifier Fusion and Optimization Automation Focal Cortical Dysplasia Detection on Magnetic Resonance Images
文献类型：期刊论文
| 作者 | Qu, Xiaoxia1,2; Yang, Jian1 ; Platis, Ljiljana3; Kumcu, Asli3; Ai, Danni1; Goossens, Bart3; Bai, Tingzhu1; Wang, Yongtian1; Sui, Jing4; Deblaere, Karel5
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| 刊名 | IEEE ACCESS
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| 出版日期 | 2018 |
| 卷号 | 6页码:73786-73801 |
| 关键词 | Focal cortical dysplasia magnetic resonance image brain lesion detection optimal weighted multiple classifiers genetic algorithm |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2018.2883583 |
| 通讯作者 | Yang, Jian(jyang@bit.edu.cn) |
| 英文摘要 | In magnetic resonance (MR) images, detection of focal cortical dysplasia (FCD) lesion as a main pathological cue of epilepsy is challenging because of the variability in the presentation of FCD lesions. Existing algorithms appear to have sufficient sensitivity in detecting lesions but also generate large numbers of false-positive (FP) results. In this paper, we propose a multiple classifier fusion and optimization schemes to automatically detect FCD lesions in MR images with reduced FPs through constructing an objective function based on the F-score. Thus, the proposed scheme obtains an improved tradeoff between minimizing FPs and maximizing true positives. The optimization is achieved by incorporating the genetic algorithm into the work scheme. Hence, the contribution of weighting coefficients to different classifications can be effectively determined. The resultant optimized weightings are applied to fuse the classification results. A set of six typical FCD features and six corresponding Z-score maps are evaluated through the mean F-score from multiple classifiers for each feature. From the experimental results, the proposed scheme can automatically detect FCD lesions in 9 out of 10 patients while correctly classifying 31 healthy controls. The proposed scheme acquires a lower FP rate and a higher F-score in comparison with two state-of-the-art methods. |
| WOS关键词 | MRI ; FEATURES ; REGISTRATION ; TEXTURE ; ROBUST ; SEGMENTATION ; LESIONS |
| 资助项目 | National Key Research and Development Program of China[2017YFC0107800] ; National Science Foundation Program of China[61672099] ; National Science Foundation Program of China[81627803] ; National Science Foundation Program of China[61501030] ; National Science Foundation Program of China[61527827] ; China Scholarship Council[201206030018] ; CSC from Ghent University[01SC0213] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000454365500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Science Foundation Program of China ; China Scholarship Council ; CSC from Ghent University |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25629]  |
| 专题 | 自动化研究所_脑网络组研究中心 |
| 通讯作者 | Yang, Jian |
| 作者单位 | 1.Beijing Inst Technol, Sch Opt & Photon, Beijing Engn Res Ctr Mixed Real & Adv Display, Beijing 100081, Peoples R China 2.Capital Med Univ, Beijing Tongren Hosp, Radiol Dept, Beijing 100730, Peoples R China 3.Univ Ghent, Dept Telecommun & Informat Proc imec IPI TELIN, B-9000 Ghent, Belgium 4.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Ghent Univ Hosp, Dept Radiol, B-9000 Ghent, Belgium |
| 推荐引用方式 GB/T 7714 | Qu, Xiaoxia,Yang, Jian,Platis, Ljiljana,et al. Multiple Classifier Fusion and Optimization Automation Focal Cortical Dysplasia Detection on Magnetic Resonance Images[J]. IEEE ACCESS,2018,6:73786-73801. |
| APA | Qu, Xiaoxia.,Yang, Jian.,Platis, Ljiljana.,Kumcu, Asli.,Ai, Danni.,...&Philips, Wilfried.(2018).Multiple Classifier Fusion and Optimization Automation Focal Cortical Dysplasia Detection on Magnetic Resonance Images.IEEE ACCESS,6,73786-73801. |
| MLA | Qu, Xiaoxia,et al."Multiple Classifier Fusion and Optimization Automation Focal Cortical Dysplasia Detection on Magnetic Resonance Images".IEEE ACCESS 6(2018):73786-73801. |
入库方式： OAI收割
来源：自动化研究所
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