Computer-Aided Diagnosis

Change detection of medical images using dictionary learning techniques and principal component analysis

[+] Author Affiliations
Varvara Nika

York University, Department of Mathematics and Statistics, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada

Paul Babyn

University of Saskatchewan, Department of Medical Imaging, 103 Hospital Drive, Saskatoon, Saskatchewan S7N 0W8, Canada

Hongmei Zhu

York University, Department of Mathematics and Statistics, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada

J. Med. Imag. 1(2), 024502 (Sep 22, 2014). doi:10.1117/1.JMI.1.2.024502
History: Received January 18, 2014; Revised May 15, 2014; Accepted September 3, 2014
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Abstract.  Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the L2 norm over the L1 norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient’s position and other acquisition artifacts.

© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Varvara Nika ; Paul Babyn and Hongmei Zhu
"Change detection of medical images using dictionary learning techniques and principal component analysis", J. Med. Imag. 1(2), 024502 (Sep 22, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.2.024502


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