Tissue segmentation and classification of MRSI data using canonical correlation analysis.
In this article an accurate and efficient technique for tissue typing is presented. The proposed technique is based on Canonical Correlation Analysis, a statistical method able to simultaneously exploit the spectral and spatial information characterizing the Magnetic Resonance Spectroscopic Imaging (MRSI) data. Recently, Canonical Correlation Analysis has been successfully applied to other types of biomedical data, such as functional MRI data. Here, Canonical Correlation Analysis is adapted for MRSI data processing in order to retrieve in an accurate and efficient way the possible tissue types that characterize the organ under investigation. The potential and limitations of the new technique have been investigated by using simulated as well as in vivo prostate MRSI data, and extensive studies demonstrate a high accuracy, robustness, and efficiency. Moreover, the performance of Canonical Correlation Analysis has been compared to that of ordinary correlation analysis. The test results show that Canonical Correlation Analysis performs best in terms of accuracy and robustness.
Laudadio
Teresa
T
Department of Electrical Engineering, Division ESAT-SCD, Katholieke Universiteit Leuven, Leuven-Heverlee, Belgium. laudadio@esat.kuleuven.ac.be
Pels
Pieter
P
De Lathauwer
Lieven
L
Van Hecke
Paul
P
Van Huffel
Sabine
S
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000295:instrumentation / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic