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Author: search.filters.author.Kaewaramsri, Yothin

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    Fractal dimension for classifying 3D brain MRI using improved triangle box-counting method
    (2017-02-23)
    Kaewaramsri, Yothin
    ;
    Alfarozi, Syukron Abu Ishaq
    ;
    Woraratpanya, Kuntpong
    ;
    Kuroki, Yoshimitsu
    Although many papers have used fractal dimension (FD) to analyze magnetic resonance imaging (MRI) for detecting various brain diseases, especially Alzheimer's disease (AD), they have been unsuccessful to classify the AD patients in case of healthy and AD brain-MRIs. The significant problems are from (i) the lack of the efficient FD estimation method and (ii) the failure of applying statistical analysis to discriminate the subjects in MRIs. Therefore, this paper proposes an alternative way to overcome these problems by using an improved triangle box-counting method (ITBC) for effective FD estimation and using machine learning for brain-MRI discrimination. The proposed method is evaluated its performance with the Alzheimer's disease patient discrimination dataset of open access series of imaging studies (OASIS). The experimental results show that the pro-posed method can achieve the classification accuracy rate up to 86.20% whereas the statistical analysis approaches cannot discriminate healthy and AD.
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    Texture analysis assessment for images
    (2017-02-23)
    Titijaroonroj, Taravichet
    ;
    Kaewaramsri, Yothin
    ;
    Suttapakti, Ungsumalee
    ;
    Woraratpanya, Kuntpong
    ;
    Kuroki, Yoshimitsu
    Commonly, the existing metrics such as mean square error (MSE), peak signal-To-noise ratio (PSNR), quality index (QI), structural similarity index metric (SSIM), and quality index based on local variance (QILV) use the image intensity-based statistics approach to assess the quality of distorted images. These metrics are successful in discriminating the quality of distorted images, such as de-noising, JPEG compressed, and blur images. However, they are unsuccessful in discriminating the quality of channel decomposition images. Therefore, this paper proposes the texture analysis assessment (TAA) to measure the quality of both normally distorted images and channel decomposition images. The proposed metric uses image intensity statistics in conjunction with texture analysis for quality discrimination of slightly different distorted and channel decomposition images. The texture analysis based on edge orientation is an important part employed to measure precise image errors. The experimental results illustrate that the TAA metric can evidently discriminate the quality of normally distorted images and channel decomposition images, when compared with state-of-The-Art metrics. Furthermore, the perceived visual quality and the quality value of TAA are corresponding; the lower visual quality human-eye perceives, the lower quality value TAA measures, and vice versa.
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    Improved triangle box-counting method for fractal dimension estimation
    (2015-01-01)
    Kaewaramsri, Yothin
    ;
    Woraratpanya, Kuntpong
    A fractal dimension (FD) is an effective feature, which characterizes roughness and self-similarity of complex objects. However, the FD in nature scene requires the effective method for estimation. The existing methods focus on the improvement of selecting the suitable height of box-counts. This cannot overcome the overcounting problem, which is a key factor to have an impact on the accuracy of the FD estimation. This paper proposes a more accurate FD estimation, an improved triangle box-counting method, to increase the precision of box-counts associated with box sizes. The triangle-box-partition technique provides the double precision for box-counts, thus it can solve the overcounting issue and enhance the accuracy of the FD estimation. The proposed method is evaluated its performance in terms of fitting error. The experimental results show that the proposed method outperforms the existing methods, including differential box-counting (DBC), improved DBC (IDBC), and box-counting with adaptable box height (ADBC) methods.