Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


osu1357269157.pdf (1.71 MB)

ETD Abstract Container

Abstract Header

De-noising of Real-time Dynamic Magnetic Resonance Images by the Combined Application of Karhunen-Loeve Transform (KLT) and Wavelet Filtering

Palaniappan, Prashanth
http://rave.ohiolink.edu/etdc/view?acc_num=osu1357269157

Abstract Details

2013, Master of Science, Ohio State University, Electrical and Computer Engineering.
A hybrid filtering method called Karhunen Loeve Transform-Wavelet (KW) filtering is presented to de-noise dynamic cardiac magnetic resonance images that simultaneously takes advantage of the intrinsic spatial and temporal redundancies of real-time cardiac cine. This new image filtering technique combines two well-established methods: temporal Karhunen-Loeve transform (KLT) and spatial adaptive wavelet filtering. KW filtering has four steps: 1. Apply KLT along the temporal direction, generating a series of “eigenimages”. Because of the high temporal correlations, most of the energy is concentrated into a few eigenimages. 2. Marcenko-Pastur (MP) law is used to identify and discard the noise-only eigenimages; 3. 2-D spatial wavelet filter with adaptive threshold is applied to each eigenimage. An adaptive threshold is used to define the wavelet filter strength for each of the eigenimages based on the noise variance and standard deviation of the signal, resulting in stronger filtering of the eigenimages that primarily contain noise. 4. Apply the inverse KLT to the filtered eigenimages to generate a new series of cine images with reduced image noise. KW filter was compared with 2 other filters – Spatial Wavelet filter and Temporal KLT filter in terms of SNR gain and edge sharpness. For four volunteer data acquired using rate 5 acceleration, KW filter showed an SNR gain of 98%. For a matched value of SNR gain between KW filter, Wavelet filter and KLT filter, KW filter preserved 93.83% of original image sharpness while Wavelet filter and KLT filter preserved 82.23% and 88.05% respectively.
Orlando P. Simonetti (Advisor)
Yuan F. Zheng (Committee Member)
Yu Ding (Committee Member)
72 p.
Electrical Engineering
Noise reduction; SNR; Image Processing; MRI

Recommended Citations

Citations

  • Palaniappan, P. (2013). De-noising of Real-time Dynamic Magnetic Resonance Images by the Combined Application of Karhunen-Loeve Transform (KLT) and Wavelet Filtering [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357269157

    APA Style (7th edition)

  • Palaniappan, Prashanth. De-noising of Real-time Dynamic Magnetic Resonance Images by the Combined Application of Karhunen-Loeve Transform (KLT) and Wavelet Filtering. 2013. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1357269157.

    MLA Style (8th edition)

  • Palaniappan, Prashanth. "De-noising of Real-time Dynamic Magnetic Resonance Images by the Combined Application of Karhunen-Loeve Transform (KLT) and Wavelet Filtering." Master's thesis, Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357269157

    Chicago Manual of Style (17th edition)

osu1357269157
380
© 2013, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.