Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

Zhang, Yudong; Wang, Shuihua; Yang, Xiao‐Jun; Dong, Zhengchao; Ge, Liangpeng; Phillips, P. Jonathon; Yuan, Ti‐Fei

doi:10.1186/s40064-015-1523-4

Cited by 71 publications

(27 citation statements)

References 53 publications

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“…A related distortion measure is the peak signal-to-noise ratio (PSNR), measured in decibels (dB). The problem with mean-square error is that it depends strongly on the image intensity scaling while PSNR rectifies this problem by scaling the mean-square error according to the image range [17]. PSNR is defined as follows:

\begin{matrix} T1 \\ P S N R (d B) = 10 \log_{10} \frac{\max I^{2}}{{M S E}^{'}}, \end{matrix}

where max⁡ I is the peak value of the original image.…”

Section: Resultsmentioning

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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\begin{matrix} T1 \\ P S N R (d B) = 10 \log_{10} \frac{\max I^{2}}{{M S E}^{'}}, \end{matrix}

where max⁡ I is the peak value of the original image.…”

Section: Resultsmentioning

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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“…They employed the probabilistic NN (PNN) for classification. Zhang et al [17,18] found spider-web plots had no effect on PBD. Das et al [19] suggested using a Ripplet transform (RT) in PBD.…”

Section: State-of-the-artmentioning

confidence: 99%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Wang

Zhang

Yang

et al. 2015

Entropy

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Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper. Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform (FRFT) and Shannon entropy. Afterwards, the Welch's t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10ˆK-fold stratified cross validation test showed that this proposed "FRFE + WTT + TSVM" yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed "FRFE + WTT + TSVM" method is superior to 20 state-of-the-art methods.

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“…c i (i = 1,2) are positive parameters, and o i (i = 1,2) is the same as in formula (10). By this mean, the TSVM constructed two hyperplanes [45]. The first term in equations of (18) and (19) is the sum of squared distances.…”

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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Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

Cited by 71 publications

References 53 publications

Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

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

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