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Volume-Preserving Deformable Registration Method for Temporal Change Analysis on Medical Images

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Authors
박성진
Advisor
신영길
Major
공과대학 컴퓨터공학과
Issue Date
2012-08
Publisher
서울대학교 대학원
Keywords
B-spline deformable registrationCompute unified cevice architecture (CUDA)Follow-up studyGrey-level co-occurrence matrix (GLCM) texture analysisGround glass opacity (GGO) noduleNodule growth assessmentVolume-preserving constraint
Description
학위논문 (박사)-- 서울대학교 대학원 : 전기컴퓨터공학부, 2012. 8. 신영길.
Abstract
Lung cancer screening for identifying early lung cancer has been achieved by follow-up computed tomography (CT) techniques. In lung cancer screening,benign and malignant nodules can be classified through nodule growth assessment by measuring temporal changes in the volume and intensity of nodules in succeding follow-up CT scans. Various automatic or semi-automatic nodule segmentation methods have been developed for nodule volumetry and growth assessment for solid nodules. However, ground glass opacity (GGO)nodules are very elusive to automatically segment due to their inhomogeneous interior. The nodule growth assessment of GGO nodules can be achieved by the subtraction after registration between follow-up CT scans. During the
registration, the volume of nodule regions in the floating image should be preserved, whereas the volume of other regions in the floating image should be
aligned to that in the reference image. Several constraints for preserving the nodule volume have been proposed in the analysis of nodule changes that
occur between reference and floating images. However, they are not appropriate for GGO nodules due to the lung respiratory motion and the difficulty of nodule segmentation. In this dissertation, we propose an accurate and fast deformable registration method. It applies the volume-preserving constraint to candidate regions of GGO nodules, which are automatically detected by gray-level cooccurrence matrix (GLCM) texture analysis. Considering that GGO nodules can be characterized by their inner inhomogeneity and high intensity, we identify the candidate regions of GGO
nodules based on the homogeneity values calculated by the GLCM and the intensity values. Furthermore, we accelerate our deformable registration by using Compute Unified Device Architecture (CUDA). In the deformable
registration process, the computationally expensive procedures of the floating image transformation and the cost function calculation are accelerated by using
CUDA. The experimental results demonstrated that our method almost perfectly preserves the volume of GGO nodules in the floating image as well as effectively aligns the lung between the reference and floating images. Regarding the computational performance, our CUDA-based method delivers about 45.9× faster registration than the conventional method. Our method can be successfully applied to a GGO nodule follow-up study and can be extended to the volume-preserving registration and subtraction of specific diseases in
other organs.
Language
English
URI
https://hdl.handle.net/10371/119989
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Theses (Ph.D. / Sc.D._컴퓨터공학부)
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