KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Subject: search.filters.subject.digital image processing

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Detection of fibrosis in liver biopsy images by using Bayesian classifier
    (2015-02-27)
    Meejaroen, Kanyanat
    ;
    Chaweechan, Charoen
    ;
    Khodsiri, Wanus
    ;
    Khu-Smith, Vorapranee
    ;
    Watchareeruetai, Ukrit
    In this paper, an image-processing-based method designed to detect fibrosis in liver biopsy images is proposed. The proposed method first enhances the color difference between liver tissue and fibrosis areas. Then, a low-pass filtering is applied to each color band to reduce noise. In order to calculate the percentage of fibrosis against total liver tissue, the background area, i.e. empty slide area, is detected. Next, Bayesian classifier is used to separate fibrosis from liver tissue based on the color information. Finally, the proportion of the fibrosis area to the tissue area is computed. Experimental results show that the proposed method can estimate and detect fibrosis in the liver biopsy images with the classification accuracy of 91.42%. In addition, the average difference between the percentage of fibrosis obtained from the proposed method and that in ground truth images is 2.29 points.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Identification, counting, and sizing of dispersed phase droplet of scanning electron microscopy micrograph using digital image processing
    (2012-12-01)
    Phankokkruad, Manop
    ;
    Wacharawichanant, Sirirat
    The identification of dispersed phase droplet in scanning electron microscopy (SEM) image is the heart of the process polymer blends, especially for the development of the polymeric materials and improvement of the polymer properties. Manual identification is the hard work and inaccurate method. To solve this problem, a digital image processing (DIP) method based on Hough transform is proposed for automatically identifies the dispersed phase droplet in SEM images. By combining the characteristics of SEM images and the DIP method, this method performed hierarchical Hough transform on the circular droplet to detect the object boundary in the SEM images. The DIP method has been experimented on variety of SEM images and very promising results have been achieved given more accuracy. Experimental results show that the proposed method with high adaptability is more accurate and rapidly than the traditional method. © 2012 IEEE.