Nurfauzi, R. and Nugroho, H. A. and Ardiyanto, I. and Frannita, E. L. (2021) Autocorrection of lung boundary on 3D CT lung cancer images*. JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 33 (5). pp. 518-527. ISSN 1319-1578
Full text not available from this repository. (Request a copy)Abstract
Lung cancer in men has the highest mortality rate among all types of cancer. Juxta-pleural and juxtavascular are the most common nodules located on the lung surface. A computer aided detection (CADe) system is effective for assisting radiologists in diagnosing lung nodules. However, the lung segmentation step requires sophisticated methods when juxta-pleural and juxta-vascular nodules are present. Fast computational time and low error in covering nodule areas are the aims of this study. The proposed method consists of five stages, namely ground truth (GT) extraction, data preparation, tracheal extraction, separation of lung fusion and lung border correction. The used data consist of 57 3D CT lung cancer images taken from selected LIDC-IDRI dataset. These nodules are determined as the outer areas labeled by four radiologists. The proposed method achieves the fastest computational time of 0.32 s per slice or 60 times faster than that of conventional adaptive border marching (ABM). Moreover, it produces under segmentation of nodule value as low as 14.6%. It indicates that the proposed method has a potential to be embedded in the lung CADe system to cover pleural juxta and vascular nodule areas in lung segmentation. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Item Type: | Article |
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Uncontrolled Keywords: | CADe system; Lung; Segmentation; Boundary correction |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 23 Oct 2024 08:27 |
Last Modified: | 23 Oct 2024 08:27 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8924 |