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Can I read Patient Oriented and Robust Automatic Liver Segmentation for Pre-evaluation of Liver Transplantation on EtoBox?
Patient Oriented and Robust Automatic Liver Segmentation for Pre-evaluation of Liver Transplantation by M. Alper Selver; Aykut Kocaoğlu; Güleser K. Demir; Hatice Doğan; Oğuz Dicle; Cüneyt Güzeliş is a Medicine article available to read on EtoBox.
What is Patient Oriented and Robust Automatic Liver Segmentation for Pre-evaluation of Liver Transplantation about?
Identifying liver region from abdominal computed tomography-angiography (CTA) data sets is one of the essential steps in evaluation of transplantation donors prior to the hepatic surgery. However, due to gray level similarity of adjacent organs, injection of contrast media and partial volume effects; robust segmentation of the liver is a very difficult task. Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder. In this paper, we propose a three stage (i.e. pre-processing, classification, post-processing); automatic liver segmentation algorithm that adapts its parameters according to each patient by learning the data set characteristics in parallel to segmentation process to address all the challenging aspects mentioned above. The efficiency in terms of the time requirement and the overall segmentation performance is achieved by introducing a novel modular classification system consisting of a K-Means based simple classification system and an MLP based complex one which are combined with a data-dependent and automated switching mechanism that decides to apply one of
Who reads Patient Oriented and Robust Automatic Liver Segmentation for Pre-evaluation of Liver Transplantation?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- M. Alper Selver; Aykut Kocaoğlu; Güleser K. Demir; Hatice Doğan; Oğuz Dicle; Cüneyt Güzeliş
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0010-4825)
- Published
- 2008
- Language
- EN
- Field
- Medicine (Physical Sciences)