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Introduction: Accurate segmentation of brain tissue in magnetic resonance imaging (MRI) is an important step in the analysis of brain images. There are automated methods used to segmentation the brain and minimize the disadvantages of manual segmentation, including time consuming and misinterpretations. These procedures usually involve a combination of skull removal, bias field correction, and segmentation. Therefore, segmented tissue quality assessment segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is required for the analysis of neuroimages. Material and Methods: This paper presents the performance evaluation of three automatic methods brain segmentation, fluid and white matter suppression [FSL, Freesurfer (FreeSurfer is an open source package for the analysis and visualization of structural, functional, and diffusion neuroimaging data from cross-sectional and longitudinal studies) and SPM12 (Statistical Parametric Mapping)]. Segmentation with SPM12 was performed on three tissue probability maps: i) threshold 0.5, ii) threshold 0.7 and iii) threshold 0.9. In order to compare and evaluate the automatic methods, the reference standard method, i.e., manual segmentation, was performed by three radiologists. Results: Comparison of GM, WM and CSF segmentation in MR images was performed using similarities between manual and automatic segmentation. The similarity between the segmented tissues was calculated using diagnostic criteria. Conclusion: Several studies have examined the classification of GM, WM, and CSF using software packages. In these studies, different results have been obtained depending on the type of method and images used and the type of segmented tissues. In this study, the evaluation of the segmentation of these packages with reference standard method is performed. The results can help users in selecting an appropriate segmentation tool for neuroimages analysis.

Background: With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Objectives: In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM). Participants: For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD. Measure: ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software. Results: The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups. Conclusions: Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.