| Summary: | 碩士 === 國立雲林科技大學 === 電機工程系 === 107 === In this paper, we use amplified narrow-band imaging (NBI) endoscopic images of the stomach as a data set, there are 66 and 60 images of the training set and test set, respectively. We extract the scale-invariant feature transform (SIFT) feature and find the abnormal region of early gastric cancer. First, we capture the region of interest in an image and filter out the bright and dark blocks. The images segmented into different block sizes, such as 40×40, 50×50, and 60×60, which are partially overlapping. For each block, we determine the SIFT features and then cluster these feature vectors to the bag of visual words (BOVW). Therefore, each image can be represented as a histogram of visual words, which can be used as an input for classifier training. In our experiments, the highest average precision and recall rates reached 85% and 81%, respectively.
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