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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.
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참고문헌 (8)
참고문헌 신청
T. Heimann / 2009 / Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets / IEEE Transactions on Medical Imaging 28:125~1265
Anamika Ahirwar / 2013 / Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI / Information Technology and Computer Science 5:44~53
D. Furukawa / 2007 / Automatic liver segmentation based on maximum a posterior probability estimation and level set method / Proc. MICCAI Workshop 3-D Segment at. Clinic: A Grand Challenge :117~124
Anter, AM. / 2013 / Automatic computer- aided segmentation for liver and hepatic lesions using hybrid segmentations techniques / Computer Science and Information Systems :193~198
Nobuyuki Otsu / 1979 / A threshold selection method from gray-level histograms / IEEE Trans. Sys., Man., Cyber 9:62~66
Anamika Ahirwar / 2013 / Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI / Information Technology and Computer Science 5:44~53
D. Furukawa / 2007 / Automatic liver segmentation based on maximum a posterior probability estimation and level set method / Proc. MICCAI Workshop 3-D Segment at. Clinic: A Grand Challenge :117~124
Anter, AM. / 2013 / Automatic computer- aided segmentation for liver and hepatic lesions using hybrid segmentations techniques / Computer Science and Information Systems :193~198
Nobuyuki Otsu / 1979 / A threshold selection method from gray-level histograms / IEEE Trans. Sys., Man., Cyber 9:62~66
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