Browsing by Author "Chen, Hsien-Tse"

Now showing 1 - 2 of 2

Automatic Counting Cancer Cell Colonies using Fuzzy Inference System
(2011-03-01)
Lin, Sheng-Fuu
;
Chen, Hsien-Tse
;
Lin, Yi-Hsien
;
電機工程學系
;
Department of Electrical and Computer Engineering
This paper examines the efficacy of liver cancer treatment using HA22T cancer cells specific to the Taiwanese population. The Clonogenic Assay is the current standard method for detecting liver cancer. This paper uses image processing technology and a fuzzy inference system to identify in-vitro colonies of cancer cells. A scanner was first used to capture an image of the culture dish. This image was then analyzed using image processing techniques and the Hough transform to establish the relative position of the dish. Image segmentation was accomplished by image differencing, while feature extraction was based on the features specific to the image. Decision-making was then carried out using a fuzzy inference system to calculate the number of colonies within the image. In summary, this paper proposes a fuzzy cancer cell colony identification system based on a fuzzy inference system (FIS) that successfully identifies cancer cell colonies that are indiscernible to the naked eye.
AUTOMATIC IDENTIFYING LARYNGOPHARYNGEAL REFLUX USING ARTIFICIAL NEURAL NETWORK
(2012-02-01)
Lin, Sheng-Fuu
;
Chen, Hsien-Tse
;
Tsai, Tung-Lung
;
電機工程學系
;
Department of Electrical and Computer Engineering
Laryngopharyngeal Reflux mostly leads to burns of the pharynx and larynx by reflux from gastric acid and also leads to different degrees of burns in the esophagus and the stomach. This paper aims to develop a technique for analyzing pharyngeal and laryngeal images. With techniques of digital image processing, this paper can choose the suitable images from burns of the pharynx and larynx to obtain the feature zones of burns of the pharynx and larynx. Artificial neural network helps physicians to develop the diagnostic standard about the burns severity of Laryngopharyngeal Reflux. This paper divides the types of the complications into three degrees and compares with other ways (Hanson et al.(5) and Ilgner et al.(6)). The results can be the technical assistance in helping physicians to diagnose the severity of Laryngopharyngeal Reflux and to make a more precise diagnosis.