Regularization methods for solving third-order volterra filter with improved convergence speed: In-vivo application

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

Abstract

The differential diagnosis of idiopathic Parkinson's Disease (iPD) from atypical parkinsonian syndromes can be very difficult at the early stages of these diseases. Trancranial Ultrasound Imaging (TCUI) of the Substantia Nigra (SN) is one method that has been shown to aide in this early differential diagnosis. TCUI is done to detect hyperechogenicity in the SN, which is defined as an echogenic area above a threshold size of 0.2 cm2. Because B-mode ultrasound images are often noisy, determining the size of the echogenic area can be difficult. Harmonic imaging using a Third-Order Volterra (ToVF) filter is one solution that has been successful in filtering out the noise in these images, allowing a more reliable diagnosis. In this paper, we show that regularization methods such as Truncated Singular Value Decomposition (TSVD) and Tikhonov method can be used to solve for the Volterra Filter's coefficient much more quickly than least mean square (LMS) methods studied previously without sacrificing image quality. This finding has implications in terms of the Volterra Filter's viability for use in real-time harmonic imaging applications.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1187-1190
Number of pages4
Volume2018-April
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/4/184/7/18

Fingerprint

Imaging techniques
Ultrasonics
Substantia Nigra
Ultrasonography
Differential Diagnosis
Parkinsonian Disorders
Singular value decomposition
Least-Squares Analysis
Image quality
Parkinson Disease
Noise
Early Diagnosis

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Cunningham, J., Zheng, Y., Subramanian, T., & Almekkawy, M. K. (2018). Regularization methods for solving third-order volterra filter with improved convergence speed: In-vivo application. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (Vol. 2018-April, pp. 1187-1190). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363783
Cunningham, James ; Zheng, Yi ; Subramanian, Thyagarajan ; Almekkawy, Mohamed Khaled. / Regularization methods for solving third-order volterra filter with improved convergence speed : In-vivo application. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. pp. 1187-1190
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Cunningham, J, Zheng, Y, Subramanian, T & Almekkawy, MK 2018, Regularization methods for solving third-order volterra filter with improved convergence speed: In-vivo application. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. vol. 2018-April, IEEE Computer Society, pp. 1187-1190, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/4/18. https://doi.org/10.1109/ISBI.2018.8363783

Regularization methods for solving third-order volterra filter with improved convergence speed : In-vivo application. / Cunningham, James; Zheng, Yi; Subramanian, Thyagarajan; Almekkawy, Mohamed Khaled.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. p. 1187-1190.

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

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Cunningham J, Zheng Y, Subramanian T, Almekkawy MK. Regularization methods for solving third-order volterra filter with improved convergence speed: In-vivo application. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April. IEEE Computer Society. 2018. p. 1187-1190 https://doi.org/10.1109/ISBI.2018.8363783