Abstract
We propose a new method for detecting esophageal abnormal regions from endoscopic images based on the features of the dual -tree complex discrete wavelet transform (DT-CDWT) and persistent homology. We only have to detect normal regions exactly to detect an abnormal region. More precisely, we perform two steps to detect normal regions. In the first step, we use the feature of color to detect normal regions. To this end, an input endoscopic image is converted into CIEL*a*b* color spaces, and a composite image is created from the a* and b* components. In the second step, we detect normal regions based on topological features. We divide the composite image into small blocks. We obtain the features of zero- and one-dimensional holes by applying the DT-CDWT and persistent homology to each block. We calculate the lifetime using the birth and death times of the holes. Finally, we detect the normal region from the endoscopic image based on the lifetime. We describe the proposed method in detail and the experimental results show that the method can assist doctors for endoscopic diagnoses.
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Acknowledgements
Thanks are due to Dr. Sakata Yasuhisa and other staff of the Department of Internal Medicine, Saga University, Japan, for their helpful suggestions and comments. The medical images provided by them and their specialist advice motivated the authors to attempt this study. This work was partially supported by JSPS KAKENHI Grant Number 19K03623.
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Watarai, K., Omura, H., Minamoto, T. (2020). Esophageal Abnormality Detection from Endoscopic Images Using DT-CDWT and Persistent Homology. In: Latifi, S. (eds) 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-030-43020-7_30
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DOI: https://doi.org/10.1007/978-3-030-43020-7_30
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