Auto-Recognition System of Cecum
Date Issued
2015
Date
2015
Author(s)
Chen, Hsiao-Chuan
Abstract
In this thesis, we proposed a system which can classify the cecum image or not automatically. Our system use the feature of cecum to recognize the input images. With the advance of technology, economic prosperity and the trend of western food, the diseases of digestive system are on the rise, especially the colorectal cancer. Since A.D. 2007, colorectal cancer had become top one cancer people who got in Taiwan. Over 10,000 people are diagnosed to have colon cancer every year. According to government statistics, the colorectal cancer is the most expensive cancer in Taiwan in 2014. If the colorectal cancer has been found and cured in Tis and T1, almost 90% patients can survive after five years. There is no symptom in the early stage of colorectal cancer, so examination regularly can reduce the risk of colorectal cancer. Besides fecal occult blood test, the doctor can also check more detail by colonoscopy. From the above, colonoscopy quality is very important, we should keep it in high sensitivity. To make high quality and faster workflow, a lot of things still need to work hard. In this thesis, we focus on the cecal intubation rate (CIR) which is an important quality of colonoscopy. According to the study [9], it is a positive correlation between CIR and colorectal cancer. Thus, we proposed the cecum recognition method. We test 650 images include cecum images and not cecum images. In our experiment results, our method can provide a classification and have a good performance with another new 100 cecum images which was selected strictly by NTU hospital. Keywords: cecum, colorectal cancer, cecal intubation rate, pattern recognition, image segmentation, cross validation, line detection, classification
Subjects
cecum
colorectal cancer
cecal intubation rate
pattern recognition
image segmentation
cross validation
line detection
classification
SDGs
Type
thesis
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