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Multiple classifier fusion and optimization for automatic focal cortical dysplasia detection on magnetic resonance images

(2018) IEEE ACCESS. 6. p.73786-73801
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Abstract
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.
Keywords
Focal cortical dysplasia, magnetic resonance image, brain lesion detection, optimal weighted multiple classifiers, genetic algorithm, MRI, FEATURES, REGISTRATION, TEXTURE, ROBUST, SEGMENTATION, LESIONS

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MLA
Qu, Xiaoxia et al. “Multiple Classifier Fusion and Optimization for Automatic Focal Cortical Dysplasia Detection on Magnetic Resonance Images.” IEEE ACCESS 6 (2018): 73786–73801. Print.
APA
Qu, X., Yang, J., Platisa, L., Kumcu, A., Ai, D., Goossens, B., Bai, T., et al. (2018). Multiple classifier fusion and optimization for automatic focal cortical dysplasia detection on magnetic resonance images. IEEE ACCESS, 6, 73786–73801.
Chicago author-date
Qu, Xiaoxia, Jian Yang, Ljiljana Platisa, Asli Kumcu, Danni Ai, Bart Goossens, Tingzhu Bai, et al. 2018. “Multiple Classifier Fusion and Optimization for Automatic Focal Cortical Dysplasia Detection on Magnetic Resonance Images.” Ieee Access 6: 73786–73801.
Chicago author-date (all authors)
Qu, Xiaoxia, Jian Yang, Ljiljana Platisa, Asli Kumcu, Danni Ai, Bart Goossens, Tingzhu Bai, Yongtian Wang, Jing Sui, Karel Deblaere, and Wilfried Philips. 2018. “Multiple Classifier Fusion and Optimization for Automatic Focal Cortical Dysplasia Detection on Magnetic Resonance Images.” Ieee Access 6: 73786–73801.
Vancouver
1.
Qu X, Yang J, Platisa L, Kumcu A, Ai D, Goossens B, et al. Multiple classifier fusion and optimization for automatic focal cortical dysplasia detection on magnetic resonance images. IEEE ACCESS. IEEE; 2018;6:73786–801.
IEEE
[1]
X. Qu et al., “Multiple classifier fusion and optimization for automatic focal cortical dysplasia detection on magnetic resonance images,” IEEE ACCESS, vol. 6, pp. 73786–73801, 2018.
@article{8586305,
  abstract     = {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.},
  author       = {Qu, Xiaoxia and Yang, Jian and Platisa, Ljiljana and Kumcu, Asli and Ai, Danni and Goossens, Bart and Bai, Tingzhu and Wang, Yongtian and Sui, Jing and Deblaere, Karel and Philips, Wilfried},
  issn         = {2169-3536},
  journal      = {IEEE ACCESS},
  keywords     = {Focal cortical dysplasia,magnetic resonance image,brain lesion detection,optimal weighted multiple classifiers,genetic algorithm,MRI,FEATURES,REGISTRATION,TEXTURE,ROBUST,SEGMENTATION,LESIONS},
  language     = {eng},
  pages        = {73786--73801},
  publisher    = {IEEE},
  title        = {Multiple classifier fusion and optimization for automatic focal cortical dysplasia detection on magnetic resonance images},
  url          = {http://dx.doi.org/10.1109/access.2018.2883583},
  volume       = {6},
  year         = {2018},
}

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