Yoon, Hong-Jun
(2012)
MULTIRIDGELETS FOR TEXTURE ANALYSIS.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Directional wavelets have orientation selectivity and thus are able to efficiently represent highly anisotropic elements such as line segments and edges. Ridgelet transform is a kind of directional multi-resolution transform and has been successful in many image processing and texture analysis applications. The objective of this research is to develop multi-ridgelet transform by applying multiwavelet transform to the Radon transform so as to attain attractive improvements. By adapting the cardinal orthogonal multiwavelets to the ridgelet transform, it is shown that the proposed cardinal multiridgelet transform (CMRT) possesses cardinality, approximate translation invariance, and approximate rotation invariance simultaneously, whereas no single ridgelet transform can hold all these properties at the same time. These properties are beneficial to image texture analysis. This is demonstrated in three studies of texture analysis applications. Firstly a texture database retrieval study taking a portion of the Brodatz texture album as an example has demonstrated that the CMRT-based texture representation for database retrieval performed better than other directional wavelet methods. Secondly the study of the LCD mura defect detection was based upon the classification of simulated abnormalities with a linear support vector machine classifier, the CMRT-based analysis of defects were shown to provide efficient features for superior detection performance than other competitive methods. Lastly and the most importantly, a study on the prostate cancer tissue image classification was conducted. With the CMRT-based texture extraction, Gaussian kernel support vector machines have been developed to discriminate prostate cancer Gleason grade 3 versus grade 4. Based on a limited database of prostate specimens, one classifier was trained to have remarkable test performance. This approach is unquestionably promising and is worthy to be fully developed.
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Details
Item Type: |
University of Pittsburgh ETD
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Status: |
Unpublished |
Creators/Authors: |
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ETD Committee: |
Title | Member | Email Address | Pitt Username | ORCID |
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Committee Chair | Li, Ching-Chung | | | | Committee Member | Boston, John | | | | Committee Member | Chaparro, Luis | | | | Committee Member | El-Jaroudi, Amro | | | | Committee Member | Sun, Mingui | | | |
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Date: |
2 February 2012 |
Date Type: |
Publication |
Defense Date: |
28 July 2011 |
Approval Date: |
2 February 2012 |
Submission Date: |
1 November 2011 |
Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
Number of Pages: |
118 |
Institution: |
University of Pittsburgh |
Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
Degree: |
PhD - Doctor of Philosophy |
Thesis Type: |
Doctoral Dissertation |
Refereed: |
Yes |
Uncontrolled Keywords: |
Wavelet, Ridgelet, Multiridgelet, Cardinal Multiridgelet, Image Texture Analysis, Prostate Cancer Classification |
Date Deposited: |
02 Feb 2012 16:30 |
Last Modified: |
15 Nov 2016 13:35 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/6163 |
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