Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Combining Hough Transform and Fuzzy Unsupervised Learning Strategy in Automatic Segmentation of Large Bowel Obstruction Area from Erect Abdominal Radiographs

  • Kwang Baek Kim (Department of Artificial Intelligence, Silla University) ;
  • Doo Heon Song (Department of Computer Games, Yong-in Art & Science University) ;
  • Hyun Jun Park (Department of Artificial Intelligence Software, Cheongju University)
  • Received : 2023.09.22
  • Accepted : 2023.10.25
  • Published : 2023.12.31
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Abstract

The number of senior citizens with large bowel obstruction is steadily growing in Korea. Plain radiography was used to examine the severity and treatment of this phenomenon. To avoid examiner subjectivity in radiography readings, we propose an automatic segmentation method to identify fluid-filled areas indicative of large bowel obstruction. Our proposed method applies the Hough transform to locate suspicious areas successfully and applies the possibilistic fuzzy c-means unsupervised learning algorithm to form the target area in a noisy environment. In an experiment with 104 real-world large-bowel obstruction radiographs, the proposed method successfully identified all suspicious areas in 73 of 104 input images and partially identified the target area in another 21 images. Additionally, the proposed method shows a true-positive rate of over 91% and false-positive rate of less than 3% for pixel-level area formation. These performance evaluation statistics are significantly better than those of the possibilistic c-means and fuzzy c-means-based strategies; thus, this hybrid strategy of automatic segmentation of large bowel suspicious areas is successful and might be feasible for real-world use.

Keywords

Acknowledgement

This manuscript is a partial result of a study on the "Leaders in Industry-university Cooperation 3.0" Project, supported by the Ministry of Education and National Research Foundation of Korea.

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