Earticle

초록

영어

Segmentation is very important in accurate surgical planning such as liver transplantation and resection and identification of lung nodules. In a computed tomography image, Gaussian noise inevitably occurs in the process of acquiring the image. Gaussian noise can interfere with segmentation and interfere with accurate diagnosis, resulting in fatal consequences for patients. Therefore, this study attempted to compare and evaluate which filter is most effective for denoising in performing segmentation. For the study, Gaussian noise was added to Shepp-Logan phantom and mean, median, and Wiener filter were applied, respectively. Thereafter, segmentation was performed using the thresholding technique to evaluate using F1-score and IoU to find the most suitable filter for denoising. As a results, the F1-score was 0.62 for noise-added image, 0.697 for mean filter, 0.858 for median filter, and 0.88 for Wiener filter. The IoU was 0.449 for noise-added image, 0.535 for mean filter, 0.752 for median filter, and 0.785 for Wiener filter. Therefore, the Wiener filter was confirmed to be the most effective for denoising, and it was confirmed that the F1-score improved 1.42 times and the IoU 1.75 times compared to the noise-added image. In conclusion, various denoising filter methods were analyzed to improve the accuracy of segmentation, and Wiener filter is expected to be used in an efficient way.

한국어

Computed Tomography (CT) 영상에서 Gaussian noise는 영상이 획득되는 과정에서 불가피하게 발생되며, 이는 segmentation의 성능 및 진단의 정확도를 저하시키는 요인으로 작용한다. 따라서, 본 연구에서는 denoising filter의 종류에 따른 영역확장 (region growing, RG) 기반 segmentation 알고리즘의 성능을 정량적으로 비교 평가하였다. 실험을 위해 Shepp-Logan phantom 영상으로부터 0.05의 표준편차 값을 갖는 Gaussian noise를 부가하고 각각 mean, median, Wiener filter를 적용하였다. 이후, RG 기반 segmentation 알고리즘을 적용하였으며, 이에 대한 정량적 평가를 위해 F1-score와 IoU를 측정하였다. 결과적으로, F1-score 및 IoU 인자 모두 noisy, mean filter, median filter 그리고 Wiener filter 순으로 우수한 값을 보였다. 특히, Wiener filter를 적용하였을 때 noisy 영상과 비교하여 F1-score 및 IoU 인자는 각각 약 1.42배 및 1.75배 향상됨을 확인하였다. 결론적으로, CT 영상의 segementation 정확도를 향상시키기 위해 적합한 denoising filter를 적용해야 함을 증명하였다.

목차

요약
Abstract
Ⅰ. 서론
Ⅱ. 재료 및 방법
1. Shepp-Logan phantom 모델링 및 denoising filter 적용
2. 영역 확장 기반 segmentation 알고리즘 모델링
3. Segmentation 성능의 정량적 평가
Ⅲ. 결과 및 고찰
Ⅳ. 결론
Ⅴ. 참고문헌
요약

키워드

Image segmentation Denoising Gaussian noise Wiener filter mean filter median filter Image segmentation Denoising Gaussian noise Wiener filter mean filter median filter

저자

• Sewon Lim [ 임세원 | Department of Radiological Science, Gachon University ]
• Minji Park [ 박민지 | Department of Health Science, General Graduate School, Gachon University ]
• Dohwa Lee [ 이도화 | Department of Radiological Science, Gachon University ]
• Hajin Kim [ 김하진 | Department of Radiological Science, Gachon University ]
• Seong-Hyeon Kang [ 강성현 | Department of Biomedical Engineering, Eulji University ] Corresponding Author

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