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http://dx.doi.org/10.6109/jkiice.2017.21.5.960

Genetic lesion matching algorithm using medical image  

Cho, Young-bok (Department of Computer Science, Chungbuk National University)
Woo, Sung-Hee (Department of Medical Information IT&Engineering, Korea National University of Transportation)
Lee, Sang-Ho (Department of Computer Science, Chungbuk National University)
Han, Chang-Su (Department of R&D Center, SONOUM Inc)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.5, 2017 , pp. 960-966 More about this Journal
Abstract
In this paper, we proposed an algorithm that can extract lesion by inputting a medical image. Feature points are extracted using SIFT algorithm to extract genetic training of medical image. To increase the intensity of the feature points, the input image and that raining image are matched using vector similarity and the lesion is extracted. The vector similarity match can quickly lead to lesions. Since the direction vector is generated from the local feature point pair, the direction itself only shows the local feature, but it has the advantage of comparing the similarity between the other vectors existing between the two images and expanding to the global feature. The experimental results show that the lesion matching error rate is 1.02% and the processing speed is improved by about 40% compared to the case of not using the feature point intensity information.
Keywords
Image Matching of Medical Image; Feature Point Extraction; Error Rate of Matching; Feature Point Similarity;
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