| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 96 === Recently, requirements of image processing techniques used for helping biomedical diagnosis have become popular. In traditional systems, they’re time consuming because a lot of observations and reorganizations of human resources are needed. For solving this problem, a standard system that decreases lots of time and extra human resources is needed. About this, in this thesis, an image analysis system for automatic counting cancer cell colonies is proposed. In a detection of a curative effect of liver cancer, the Clonogenic assay is a golden standard. It’s used to assay the cells that are named HA22T of liver cancer. The steps of Clonogenic assay consist of seeding process, treating process, waiting process, making a location by dyeing, and counting process. This thesis proposed an automatic counting system to take place of counting by human for solving the problem that mentioned above. The automatic counting system consists of an image process and a fuzzy inference system (FIS). In an image process, the scanner is used to scan the image of a dish and store the scanned images into the computer. After that, an image process method that called Hough transform is used to find the relative position of the dish. After finding the relative position of the dish, an image subtraction is used to separate targets image and backgrounds image and perform a feature extraction according to the experience of a doctor. In the FIS, the total number of the cancer cell colonies is distinguished and calculated. The advantages of this thesis are summarized as follows: 1) the proposed system can distinguish whether the cells form a dense or sparse region in the colony; 2) the proposed system adopts a fuzzy inference system (FIS) to obtain a better performance of distinguishing the cancer cell colonies; 3) the proposed system can take place of counting by human.
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