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Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs

Open Journal of Medical Imaging 2011年第2期1卷 26-42页

作者： Bassem A. Abdullah Akmal A. Younis pradip M. pattany Efrat Saraf-Lavi 不详

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI). The technique uses textural features to describe the blocks of each MRI slice along with position and neighborhood features. A trained support vector machine (SVM) is used to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions based on mainly the textural features with aid of the other features. The MRI slice blocks’ classification is used to provide an initial segmentation. A comprehensive post processing module is then utilized to refine and improve the quality of the initial segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated process without the need to manually define regions of interest (ROIs). In addition, the post processing module is generic enough to be applied to the results of any other MS segmentation technique to improve the segmentation quality. This technique is evaluated using ten real MRI data-sets with 10% used in the training of the textural-based SVM. The average results for the performance evaluation of the presented technique were 0.79 for dice similarity, 0.68 for sensitivity and 0.9 for the percentage of the detected lesion load. These results indicate that the proposed method would be useful in clinical practice for the detection of MS lesions from MRI.

关键词： MRI Texture Analysis Segmentation Multiple Sclerosis SVM ROI Post-Processing Fuzzy Rules

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A Novel Computerized Cognitive Test for the Detection of Mild Cognitive Impairment and Its Association with Neurodegeneration in Alzheimer’s Disease Prone Brain Regions

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Advances in Alzheimer's Disease 2023年第3期12卷 38-54页

作者： Rosie E. Curiel Cid D. Diane Zheng Marcela Kitaigorodsky Malek Adjouadi Elizabeth A. Crocco Mike Georgiou Christian Gonzalez-Jimenez Alexandra Ortega Mohammed Goryawala Natalya Nagornaya pradip pattany Efrosyni Sfakianaki Ubbo Visser David A. Loewenstein Center for Cognitive Neuroscience and Aging and Department of Psychiatry and Behavioral Sciences Miller School of Medicine University of Miami Miami Florida USA Center for Advanced Technology and Education Department of Electrical and Computer Engineering College of Engineering and Computing Florida International University Miami Florida USA Department of Radiology and Nuclear Medicine Miller School of Medicine University of Miami Miami Florida USA Department of Computer Science University of Miami Miami Florida USA

During the prodromal stage of Alzheimer’s disease (AD), neurodegenerative changes can be identified by measuring volumetric loss in AD-prone brain regions on MRI. Cognitive assessments that are sensitive enough to measure the early brain-behavior manifestations of AD and that correlate with biomarkers of neurodegeneration are needed to identify and monitor individuals at risk for dementia. Weak sensitivity to early cognitive change has been a major limitation of traditional cognitive assessments. In this study, we focused on expanding our previous work by determining whether a digitized cognitive stress test, the Loewenstein-Acevedo Scales for Semantic Interference and Learning, Brief Computerized Version (LASSI-BC) could differentiate between Cognitively Unimpaired (CU) and amnestic Mild Cognitive Impairment (aMCI) groups. A second focus was to correlate LASSI-BC performance to volumetric reductions in AD-prone brain regions. Data was gathered from 111 older adults who were comprehensively evaluated and administered the LASSI-BC. Eighty-seven of these participants (51 CU;36 aMCI) underwent MR imaging. The volumes of 12 AD-prone brain regions were related to LASSI-BC and other memory tests correcting for False Discovery Rate (FDR). Results indicated that, even after adjusting for initial learning ability, the failure to recover from proactive semantic interference (frPSI) on the LASSI-BC differentiated between CU and aMCI groups. An optimal combination of frPSI and initial learning strength on the LASSI-BC yielded an area under the ROC curve of 0.876 (76.1% sensitivity, 82.7% specificity). Further, frPSI on the LASSI-BC was associated with volumetric reductions in the hippocampus, amygdala, inferior temporal lobes, precuneus, and posterior cingulate.

关键词： Mild Cognitive Impairment Proactive Semantic Interference MRI Volume Cortical Thickness LASSI-L

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