Spatial denoising methods for low count functional images

Mingwu Jin, Jaehoon Yu, Wei Chen, Guiyang Hao, Xiankai Sun, Glen Balch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Portable functional imaging devices can be used in oncological surgeries to locate residual tumors for better patient recovery and survival. Taking the patient dose and the limited time of surgery into account, the count in such images could be very low. In this study, we investigate effectiveness of different spatial denoising methods, such as Gaussian filtering, bilateral filtering, Rudin-Osher and Fatemin (ROF) denoising, and non-local means filtering, on low count functional images. We also propose a new denoising method based on maximum a posteriori (MAP) criterion. The simulation study shows that the simple methods, such as Gaussian and bilateral filtering, may be as effective as the advanced searching or iterative methods as measured by the relative root mean square error when the count is low. Further investigations using more realistic simulations or real functional images and tumor detection performance are needed to evaluate these methods at high noise levels.

Original languageEnglish (US)
Title of host publication2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467398626
DOIs
StatePublished - Oct 3 2016
Event2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015 - San Diego, United States
Duration: Oct 31 2015Nov 7 2015

Other

Other2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015
CountryUnited States
CitySan Diego
Period10/31/1511/7/15

Fingerprint

Surgery
Tumors
surgery
tumors
Iterative methods
Mean square error
root-mean-square errors
Residual Neoplasm
Imaging techniques
Recovery
simulation
recovery
Noise
dosage
Equipment and Supplies
Survival
Neoplasms

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging
  • Instrumentation

Cite this

Jin, M., Yu, J., Chen, W., Hao, G., Sun, X., & Balch, G. (2016). Spatial denoising methods for low count functional images. In 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015 [7582233] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSSMIC.2015.7582233

Spatial denoising methods for low count functional images. / Jin, Mingwu; Yu, Jaehoon; Chen, Wei; Hao, Guiyang; Sun, Xiankai; Balch, Glen.

2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7582233.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jin, M, Yu, J, Chen, W, Hao, G, Sun, X & Balch, G 2016, Spatial denoising methods for low count functional images. in 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015., 7582233, Institute of Electrical and Electronics Engineers Inc., 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015, San Diego, United States, 10/31/15. https://doi.org/10.1109/NSSMIC.2015.7582233
Jin M, Yu J, Chen W, Hao G, Sun X, Balch G. Spatial denoising methods for low count functional images. In 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7582233 https://doi.org/10.1109/NSSMIC.2015.7582233
Jin, Mingwu ; Yu, Jaehoon ; Chen, Wei ; Hao, Guiyang ; Sun, Xiankai ; Balch, Glen. / Spatial denoising methods for low count functional images. 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015. Institute of Electrical and Electronics Engineers Inc., 2016.
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