Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

Abstract

The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.

Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

“…This study complements previous work in using combined MRI and spectroscopy features in classifying brain tumors [13][14][15][16]. We have chosen to apply similar methods of supervised pattern recognition and computer-aided diagnosis to study a very specific question in glioma imaging, thereby relating machine learning techniques with image segmentation and clinical prediction.…”

“…While other researchers have found success in treating each voxel in an image as an separate instance in training [38], others have used prior knowledge of the acquisition procedure to reduce correlations between samples by attempting to choose only the most uncorrelated voxels [14]. The approach used in this study was to condense information from the entire ROI in a single patient, so that correlations between samples in a single patient are largely removed, and each patient contributes equally to the training data.…”

“…However, there has been somewhat less work on combining imaging data with spectroscopy. When this combination has been performed, the accuracy of the classification has been shown to be superior to using images or spectroscopy alone [13][14][15][16].…”

Brain Tumour Disease Pattern Identification from Metabolites in Magnetic Resonance Spectroscopy Graph using Data Mining Techniques

“…[6] [7]As shown in table normal and abnormal values used in classification are depicted.All research in the field indicates that the metabolite NAA decreases in almost all brain tumours and Cho increases which thereby leads in increased value for the ratio Cho/NAA. In data classification, the classifier is evaluated by a confusion matrix.…”

Multidimensional Texture Characterization: On Analysis for Brain Tumor Tissues Using MRS and MRI

“…Limited work has been carried out in the area of characterization and analysis of 3D (volumetric) textures using MRI, MRS, and both MRI and MRS [5,9,10,24,25]. Dou et al [27] proposed the glial tumor segmentation method using data fusion of MRI and MRS based on fuzzy-based method.…”