Texture analysis of MR images of patients with Mild Traumatic Brain Injury

<p>Abstract</p> <p>Background</p> <p>Our objective was to study the effect of trauma on texture features in cerebral tissue in mild traumatic brain injury (MTBI). Our hypothesis was that a mild trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection but could be detected with texture analysis (TA).</p> <p>Methods</p> <p>We imaged 42 MTBI patients by using 1.5 T MRI within three weeks of onset of trauma. TA was performed on the area of mesencephalon, cerebral white matter at the levels of mesencephalon, corona radiata and centrum semiovale and in different segments of corpus callosum (CC) which have been found to be sensitive to damage. The same procedure was carried out on a control group of ten healthy volunteers. Patients' TA data was compared with the TA results of the control group comparing the amount of statistically significantly differing TA parameters between the left and right sides of the cerebral tissue and comparing the most discriminative parameters.</p> <p>Results</p> <p>There were statistically significant differences especially in several co-occurrence and run-length matrix based parameters between left and right side in the area of mesencephalon, in cerebral white matter at the level of corona radiata and in the segments of CC in patients. Considerably less difference was observed in the healthy controls.</p> <p>Conclusions</p> <p>TA revealed significant changes in texture parameters of cerebral tissue between hemispheres and CC segments in TBI patients. TA may serve as a novel additional tool for detecting the conventionally invisible changes in cerebral tissue in MTBI and help the clinicians to make an early diagnosis.</p

Texture Analysis as a Tool for Tissue Characterization in Clinical MRI

Magnetic resonance imaging (MRI) is a valuable tool for medical diagnosis, as it is a non-invasive technique that allows superior visualisation of soft tissues. Because of the vast growth of the acquired information from medical images the development of new computer-aided diagnosis (CAD) systems has become increasingly important. The application of texture analysis (TA) in the diagnostic interpretation of MR images has become a rapidly expanding field of research. The goal of this thesis was to test the feasibility of texture analysis methods in diagnostic radiology. In this dissertation, texture analysis was applied to three different clinical materials. This study investigates whether the texture could be used to discriminate breast cancer and visible and non-visible changes in brain MRI of mild traumatic brain injuries and multiple sclerosis patients and, if so, which is the optimal texture analysis method for these applications. This study showed that TA could provide a quantitative method to aid radiologists in the detection and classification of pathological findings. A case-specific selection of the texture parameters from histogram-, co-occurrence-, run-length- and wavelet- based methods would be the optimal solution for the evaluated clinical applications. However, larger study samples are needed to further validate these findings. Another conclusion was that the texture analysis process should be simplified considerably and implemented in other CAD systems to be considered for clinical use in the future

Texture Analysis as a Tool for Tissue Characterization in Clinical MRI

Magnetic resonance imaging (MRI) is a valuable tool for medical diagnosis, as it is a non-invasive technique that allows superior visualisation of soft tissues. Because of the vast growth of the acquired information from medical images the development of new computer-aided diagnosis (CAD) systems has become increasingly important. The application of texture analysis (TA) in the diagnostic interpretation of MR images has become a rapidly expanding field of research. The goal of this thesis was to test the feasibility of texture analysis methods in diagnostic radiology. In this dissertation, texture analysis was applied to three different clinical materials. This study investigates whether the texture could be used to discriminate breast cancer and visible and non-visible changes in brain MRI of mild traumatic brain injuries and multiple sclerosis patients and, if so, which is the optimal texture analysis method for these applications. This study showed that TA could provide a quantitative method to aid radiologists in the detection and classification of pathological findings. A case-specific selection of the texture parameters from histogram-, co-occurrence-, run-length- and wavelet- based methods would be the optimal solution for the evaluated clinical applications. However, larger study samples are needed to further validate these findings. Another conclusion was that the texture analysis process should be simplified considerably and implemented in other CAD systems to be considered for clinical use in the future