| Summary: | 碩士 === 中原大學 === 電子工程研究所 === 94 === Using a capsule endoscope is better than using the traditional endoscope in detecting abnormal problems of small intestines, such as chime blocked, bleeding, and white dots on the wall of small intestines. However, each examination with the capsule endoscope produces several tens of thousands images, resulting in diagnosis difficulty for a doctor. Therefore, the goal of this thesis is to develop an automatic recognition system for the capsule endoscope images in order to save time for the doctor to view the images and expedite the examination process.
The recognition system proposed in this thesis is basically a four-stage classifier; the first and the second stages are aimed at large-area abnormality, and the third and the fourth stages are aimed at irregular–shape, scattered-distribution and small-area abnormality. Here are the descriptions for these four stages:
1.The first stage uses the HSI color model to select the images with large yellow-green abnormal area (could be chime blocked).
2.The second stage uses the FCM clustering analysis to further recognize the images with large abnormal area and are not selected in the first stage (could be bleeding).
3.Most of the images entering the third stage are either normal and uniform or with small abnormal areas. Thus, the purpose of the third stage is to select the normal uniform images.
4.Finally, the last stage uses a relatively more complicated back-propagation neural network (BPNN) to detect the images with small abnormal areas (could be white dots on the wall of small intestines).
Experimental results show that the system can do the screening of capsule endoscope images effectively, reducing the time for the doctor to make diagnosis. Moreover, the detection accuracy of the unusual images is up to 80%. Besides, abnormal images usually occurs in sequence. Thus, even a few images are erroneously classified, the abnormality of the small intestines can still be detected by examing their neighboring images. In order not to miss any suspicious image in the detection, the system is designed to make the detection sensitivity for unusual images as high as possible. As a result, some normal images could be determined as abnormal in this way. Since the purpose of this system is to serve as an assisting tool for examination and the number of normal images is usually much more than that of unusual images, the system can still help doctors a lot by screening out many normal images even though the system may make incorrect classification in some cases.
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