Enhancement of Bone Metastasis from CT Images Based on Salient Region Feature Registration

In recent years, the development of the computer-aided diagnosis (CAD) systems to support radiologist is attracting attention in medical research field. One of them is temporal subtraction technique. It is a technique to generate images emphasizing temporal changes in lesions by performing a differe...

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Bibliographic Details
Published in2018 18th International Conference on Control, Automation and Systems (ICCAS) pp. 1329 - 1332
Main Authors Sato, Suguru, Lu, Huimin, Kim, Hyoungseop, Murakami, Seiichi, Ueno, Midori, Terasawa, Takashi, Aoki, Takatoshi
Format Conference Proceeding
LanguageEnglish
Published Institute of Control, Robotics and Systems - ICROS 01.10.2018
Subjects
Bone Metastasis
Bones
Computed tomography
Computer Aided Diagnosis
Image matching
Image registration
Image segmentation
Lesions
Normalized Cross Correlation
Salient Region Feature
Segmentation
Strain
Temporal Subtraction Technique
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Summary:In recent years, the development of the computer-aided diagnosis (CAD) systems to support radiologist is attracting attention in medical research field. One of them is temporal subtraction technique. It is a technique to generate images emphasizing temporal changes in lesions by performing a differential operation between current and previous image of the same subject. In this paper, we propose an image registration method for image registration of current and previous image, to generate temporal subtraction images from CT images and enhanced bone metastasis region. The proposed registration method is composed into three main steps: i) automatic segmentation of the region of interest (ROI) using position information of the spine based on biology, ii) use global image matching to select pairs from previous and current image, and iii) final image matching based on salient region feature. We perform registration technique on synthetic data and confirm usefulness of the proposed method. Furthermore, radiologist conduct comparative experiments without and with temporal subtraction images created by proposed method. As a result, they show high reading performance by using temporal subtraction images.