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