Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine

Zhang, Yudong; Chen, Shu-Fang; Wang, Shuihua; Yang, Jianfei; Phillips, P. Jonathon

doi:10.1002/ima.22144

Cited by 120 publications

(54 citation statements)

References 69 publications

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“…Further, several other research teams also used SVMs without displaying the support vectors (Chen et al, 2015; Liu G., 2015; Tan et al, 2015; Chen M. Y. et al, 2016). This is like a face recognition system that recognizes faces quite well with a too complicated inner structure to display.…”

Section: Discussionmentioning

confidence: 99%

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Wang

Yang

et al. 2016

Front. Comput. Neurosci.

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Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way.Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC).Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM).Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier.Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

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Section: Discussionmentioning

confidence: 99%

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Wang

Yang

et al. 2016

Front. Comput. Neurosci.

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“…The conclusion was that a hybrid CPU-GPU system performed best [20]. The adoption of either GPU or hybrid CPU-GPU is largely dependent on the parallel adaption in an algorithm; an algorithm that exhibited “embarrassingly parallel problem” will be suitably used in GPU whereas hybrid CPU-GPU is suitably applied in an algorithm that exhibited “fine-grained parallelism.” For future work, the proposed method could be applied on Magnetic Resonance Images (MRI) where the ROI could be classified by using weighted-type fractional Fourier transform approach [21] prior to watermarking process. Since the watermarking is pixel oriented, thus it could be also applied on nature images with lesser restriction on image fidelity requirement as compared to medical images.…”

Section: Discussionmentioning

confidence: 99%

Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

Khor

Liew

Zain

2016

International Journal of Biomedical Imaging

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With the advancement of technology in communication network, it facilitated digital medical images transmitted to healthcare professionals via internal network or public network (e.g., Internet), but it also exposes the transmitted digital medical images to the security threats, such as images tampering or inserting false data in the images, which may cause an inaccurate diagnosis and treatment. Medical image distortion is not to be tolerated for diagnosis purposes; thus a digital watermarking on medical image is introduced. So far most of the watermarking research has been done on single frame medical image which is impractical in the real environment. In this paper, a digital watermarking on multiframes medical images is proposed. In order to speed up multiframes watermarking processing time, a parallel watermarking processing on medical images processing by utilizing multicores technology is introduced. An experiment result has shown that elapsed time on parallel watermarking processing is much shorter than sequential watermarking processing.

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“…The second one represents the sum of error variables. Therefore, minimizing Equations (18) and (19) will force the hyperplanes approximate to data in each class, and minimize the misclassification rate [46]. Finally, the constraint requires the hyperplane to be at a distance of more than one from points of the other class.…”

Section: Twin Support Vector Machinementioning

confidence: 99%

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

et al. 2016

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(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256ˆ256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s "variance and entropy (VE)" features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our "DTCWT + VE + TSVM" obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

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Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine

Cited by 120 publications

References 69 publications

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

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