Abstract
While diagnosis imaging is an indispensable technology in medical field, it causes an increase in the burden to radiologists. In recent years, computer-aided diagnosis (CAD) system for supporting a radiologist has been developed to solve this problem. Temporal subtraction, which is one of CAD, is a technique to generate images emphasizing temporal changes in lesions and facilitates diagnosis of radiologists. To make a temporal subtraction image, image registration technique is required. In this chapter, we propose an image registration method for image registration of current image and previous image to generate temporal subtraction images in a short time. The proposed method consists of three steps: (i) segmentation of the region of interest (ROI) using position information of the spine based on anatomical information, (ii) using global image matching to select pairs of previous image and current image in which the same portion is depicted, and iii) final image matching based on salient region features (SRF). We performed our registration technique to the synthetic data and confirmed usefulness of the proposed method. The rotated synthesis image gives TP 100.0% and FP 12.16%. The synthesis image obtained by applying a Gaussian filter gives TP 70.40% and FP 0.00%. The synthesis image obtained by adding artificial pseudo-lesion region gives TP 99.45% and FP 17.89%. The synthesis image obtained by adding random noise of 5% gives TP 83.05% and FP 16.95%. Furthermore, radiologist conducted comparative experiments without and with temporal subtraction images created by proposed method. As a result, radiologist showed high reading performance by using temporal subtraction images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ministry of Health, Labor and Welfare, Vital statistics, http://www.mhlw.go.jp/toukei/saikin/hw/jinkou/suikei16/index.html
R.E. Coleman, Clinical features of metastatic bone disease and risk of skeletal morbidity. Clin. Cancer Res. 12(20), 6243–6249 (2006)
G.J. Cook et al., Detection of bone metastases in breast cancer by FDG PET: Differing metabolic activity in osteoblastic and osteolytic lesions. J. Clin. Oncol. 16(10), 3375–3379 (1998)
K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)
K. Doi et al., Computer-aided diagnosis in radiology: potential and pitfalls. Eur. J. Radiol. 31(2), 97–109 (1999)
Z. Zhong et al., 3D-2D deformable image registration using feature-based nonuniform meshes. BioMed Res. Int. 2016 (2016)
A. Sotiras et al., Deformable medical image registration: A survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)
B. Rodriguez-Vila et al., Methodology for registration of distended rectums in pelvic CT studies. Med. Phys. 39(10), 6351–6359 (2012)
X. Huang et al., Hybrid image registration based on configural matching of scale invariant salient region features. CCVPRW 11, 167–179 (2004)
D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, (2005)
X.S. Yang, Cuckoo Search via Levy Flights, in World Congress on Nature & Biologically Inspired Computing, IEEE Publications, (2009), pp. 210–214, arXiv:1003.1594v1
R. Sakamoto, Temporal subtraction of serial CT images with large deformation diffeomorphic metric mapping in the identification of bone metastases. Radiology 285(2), –629, 639 (2017)
Acknowledgement
This work was supported by Leading Initiative for Excellent Young Researchers of the Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746) and JSPS KAKENHI Grant Number 16K14279, 17K10420.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Sato, S. et al. (2021). Image Registration Method for Temporal Subtraction Based on Salient Region Features. In: Li, Y., Lu, H. (eds) 3rd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-46032-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-46032-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46031-0
Online ISBN: 978-3-030-46032-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)