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Author: search.filters.author.Chuchart, PintaviroojHas files: No

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    Feasibility and conceptual design of non-invasive LF system for therapeutic applications
    (2014-01-20)
    Wichai, Sangnark
    ;
    Surapong, Pongyupinpanich
    ;
    Chuchart, Pintavirooj
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    Nathupakorn, Dechsupa
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    Supaporn, Kiattisin
    Since non-destructive therapeutic methods are taking into account in modem clinical therapy, this paper proposes the design concept of a configurable non-invasive radio frequency (RF) system for treatment applications. The system is designed based on the amplitude shift keying (ASK) technique and resonance RF of a marked cellular organism. At the resonance frequency, self-regulation mechanism of a cell is operated i.e. recovery and reconstruction. Carrier and low frequency are modulated systematically in order to perform particular resonance RF patterns. The modulated frequency is able to configure, to sweep and to radiate via spiral copper antenna in wide frequency spectrum from 0.1 MHz to 50 MHz with adjustable output power from 5-Watt to 50-Watt. Trial experiment with normal cancer cells at 1.52 MHz within 10 minutes reports that the cells are relatively respond to the RF frequency. The vitro testing results report that the generated RF signal effects to the development rate of cancer cells.
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    Automatic segmentation of blood vessels in retinal image based on fuzzy K-median clustering
    (2007-12-01)
    Supot, Sookpotharom
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    Thanapong, Chaichana
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    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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    Extraction blood vessels from retinal fundus image based on fuzzy C-median clustering algorithm
    (2007-12-01)
    Chaichana, Thanapong
    ;
    Wiriyasuttiwong, Watcharachai
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    Somporn, Reepolmaha
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    Chuchart, Pintavirooj
    ;
    Manas, Sangworasil
    An automated method detection and extraction of blood vessels in retinal images would be described. The proposed algorithm is composed of three steps: matched filtering, fuzzy c-median (FCMED) clustering and label filtering. First, the matched filter technique is to enhance visualization of the blood vessels in retinal image. Secondly, the FCMED clustering is to keep the spatial structure of vascular tree segments. Finally, label filter technique is used to remove misclassified pixels. The algorithm has been tested on twenty sets in ocular fundus images, and experimental results are compared with clinically generated vessel segmentation and are evaluated in terms of sensitivity and specificity. This method performs well in analyzing anatomical structures in retinal image. © 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.