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Author: search.filters.author.Supot, Sookpotharom

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    Automatic segmentation of blood vessels in retinal image based on fuzzy K-median clustering
    (2007-12-01)
    Supot, Sookpotharom
    ;
    Thanapong, Chaichana
    ;
    Chuchart, Pintavirooj
    ;
    Manas, Sangworasil
    This paper presents an efficient method for automatic segmentation of blood vessels in retinal images. Specifically, we also delineate vascular intersections/crossovers. The proposed algorithm is composed of three steps: matched filter, fuzzy k-median (FKMED), and length filter. The segmentation results are compared with clinically generated vessel segmentation and are evaluated in terms of sensitivity and specificity. The results are encouraging and will be used for further application such as personal identification. © 2007 IEEE.
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    Segmentation of magnetic resonance images using discrete curve evolution and fuzzy clustering
    (2007-12-01)
    Supot, Sookpotharom
    ;
    Thanapong, Chaichana
    ;
    Chuchart, Pintavirooj
    ;
    Manas, Sangworasil
    The region clustering of a Magnetic Resonance Imaging (MRI) image is more complicate than a Computed Topography (CT) image because a MRI image composes of three components such as T1-weighted, T2-weighted, and Proton Density (PD) in each layer. However, the MRI images provide more detail than the CT images. Therefore, we propose a technique of the region clustering of MRI image by using Fuzzy c-means (FCM). The fuzzy c-means algorithm is an iterative operation, that is very time-consuming and makes the algorithm impractical for using in image segmentation. To cope with this problem, the discrete curve evolution (DCE) technique is applied to find the actual cluster center to refine the initial value of the fuzzy c-means algorithm, which reduces the convergence time. In experimental results, the proposed technique provides the same segmentation accuracy as the fuzzy c-means technique. Moreover, this technique takes lower computational time comparing to the previous method. © 2007 IEEE.
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    Medical Image Compression Using Tree-Structured Vector Quantization and Fuzzy C-Means
    (2002-01-01)
    Supot, Sookpotharom
    ;
    Yuttana, Kitjaidure
    ;
    Manas, Sangworasil
    Compression of magnetic resonance images (MRI) has proved to be more difficult than other medical imaging modalities. In an average sized hospital, many tera bytes of digital imaging data (MRI) are generated every year, almost all of which has to be kept. Compression of medical images is currently being performed by using different algorithms. In this paper, Fuzzy Clustering Method is used for the image Tree Structure Vector Quantization (TSVQ). First, MR image is used for the feature vector. Then use this feature vector to design a classification tree by Fuzzy C-Means (FCM) algorithm to split two clusters. At every nonterminal, the centroid of the feature vectors clustered in each child node is computed to be the testing vector. At every leaf, the centroid of the training image blocks corresponding to their feature vectors falling on the same terminal node is calculated to be the codevector. All codevectors in the leaves are composed of a codebook. By doing so, the algorithm can preserve the edge of image, make good image quality, and reduce the processing time while constructing Tree Structured Codebook.
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    Codebook design algorithm for classified vector quantization based on fuzzy clustering
    (2002-01-01)
    Supot, Sookpotharom
    ;
    Manas, Sangworasil
    Classified Vector Quantization (CVQ) is used for coding images that achieves good perceptual results while reducing the computational load of the process. In this paper, image is sub-divided into 4×4 pixel blocks (vectors). Each vector is classified into an edge vector and a shade vector. Both edge vectors and shade vectors are used to design the codebooks of CVQ by Fuzzy C-Means (FCM) method. By doing so, the CVQ-FCM method can preserve the edge of image, make good image quality, and reduce the processing time while constructing the codebooks.