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Wavelet K-Means Clustering and Fuzzy-Based Method for Segmenting MRI Images Depicting Parkinson’s Disease

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Abstract

The early diagnosis of Parkinson’s disease (PD) is important to reduce deaths due to this disease. Images of patients with PD obtained through magnetic resonance imaging (MRI) show gray matter, white matter, and cerebrospinal fluids; images of these components provide physicians with important information to determine the severity of the disease. Therefore, methods for segmenting these regions in images have been employed. However, difficulties have been encountered in the segmentation of the PD MRI images owing to the unclear boundaries between the gray matter and white matter and the regions being contained in homogeneous and unclear structures. Therefore, we propose a hybrid wavelet k-means clustering (KMC) and fuzzy median filter (FMF) method. First, detailed information from the MRI images was extracted using discrete wavelet transform; these images were enhanced by increasing the pixel values. The enhanced images were then fed into the KMC model for segmentation. Finally, the segmented images were input into the FMF for removing uncertainty and noise. This method can also be used for segmenting the MRI images of other diseases such as tuberous sclerosis (TBS) and atrial fibrillation (AF). The results of a qualitative and quantitative evaluation conducted using an open-source benchmark dataset and a clinical dataset were presented. The peak signal-to-noise ratio, structural similarity, and mean-squared error were used to compare the efficiency of the proposed method with that of other segmentation methods. The results proved that the proposed method can assist in the early detection of PD, TBS, and AF from MRI.

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Source images, b ground truth, and segmented images using the c Otsu method, d FCM, e KMC, f PNN, g neutrosophic, h deep learning, and i proposed method

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Source images, b ground truth, and segmented images using the c Otsu method, d FCM, e KMC, f PNN, g neutrosophic, h deep learning, and i proposed method

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Acknowledgements

This study was funded in part by the Ministry of Science and Technology, Taiwan, under Grant MOST108-2221-E-027-111-MY3, in part by the joint project between the National Taipei University of Technology and the Chang Gung Memorial Hospital under Grant NTUT-CGMH-106-05, and in part by the National Taipei University of Technology International Joint Research Project, NTUT-IJRP-109-03.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan

    Yo-Ping Huang & Kanika Bhalla

  2. Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, 41349, Taiwan

    Yo-Ping Huang & Hung-Chi Chu

  3. Department of Information Management, National Penghu University, Penghu, 88046, Taiwan

    Yeong-Ching Lin

  4. Department of Neurology, Chang Gung Memorial Hospital, Taoyuan, 33333, Taiwan

    Hung-Chou Kuo

  5. Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, 45221, USA

    Wen-Jang Chu & Jing-Huei Lee

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  1. Yo-Ping Huang
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Correspondence to Yo-Ping Huang or Jing-Huei Lee.

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Huang, YP., Bhalla, K., Chu, HC. et al. Wavelet K-Means Clustering and Fuzzy-Based Method for Segmenting MRI Images Depicting Parkinson’s Disease. Int. J. Fuzzy Syst. 23, 1600–1612 (2021). https://doi.org/10.1007/s40815-021-01053-6

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