Automatic classification of Alzheimer disease based on MRI volumetric features

According to the Alzheimer's Association, Alzheimer’sdisease (AD) is the most common type of dementia (%60-80) that affects memory,language, judgment, etc. [1]. Clinical diagnosis is the goldstandard for AD [2]. Mild cognitive impairment (MCI) is a phase between usual forgetfulnessdue to aging and AD. Every individual with MCI does not develop AD. Magnetic ResonanceImaging (MRI) can detect brain anomalies related to MCI and AD and might beused to differentiate these two conditions. In this study our goal was to perform automaticclassification of healthy, MCI and AD patients using volumetric featuresextracted from segmented MRI scans. Here, we employed labeled MR images from Alzheimer'sDisease Neuroimaging Initiative (ADNI) database. There were a total of 351 MRIscans from 22 healthy subjects, 20 MCI patients, and 18 AD patients. The slicesfrom MRI scans were segmented using CAT12 toolbox in Statistical ParametricMapping (SPM) software implemented in MATLAB. After the segmentation, SPMprovided us white matter, grey matter, cerebrospinal fluid, total intracranialvolume and cortical thickness values, which we call MRI volumetric features.Along with sex and age information, volumetric features were employed as theinput for the classification phase of our study. The methods investigated herewere k-nearest neighbors (kNN), random forest, naïve Bayes, and support vector machines.In the classification, kNN performed the best among other methods resulting in 91.5%accuracy with 10-fold cross-validation. The number of correctly classifiedinstances was 321 out of 351 scans (71/77 AD, 134/145 MCI, 116/129 healthy). This study showed that volumetric features have agreat potential in automatic discrimination of healthy, MCI and AD patients.&nbsp;</p

Identification of Intracranial Calcifications and Hemorrhages Using MRI-Based Quantitative Susceptibility Mapping

Thedifferentiation of intracranial hemorrhage and calcification on conventional MRimages is often challenging. Both pathologies show varying signal intensitieson T1- and T2-weighted images. Thus, Computed Tomography is often required inconfirmation of calcification. Phaseinformation in Susceptibility Weighted Imaging can provide contrast indifferentiation of two pathologies. Recently, Quantitative SusceptibilityMapping (QSM) has been shown to be useful in this separation since QSM providesa map of local tissue magnetic susceptibility by utilizing both magnitude andphase data and performing a dipole deconvolution. Inthis study, we explored efficiency of QSM in identification of intracranialcalcifications and hemorrhages on seven different cases (six hemorrhage caseswith several lesions and one calcification case with both calcification andhemorrhage) and compared its findings with the detection using SWI phase data. SWIphase images showed seventeen out of nineteen hemorrhage lesions but failed toidentify almost any lesions whether it’s a hemorrhage or calcification while eighteenout of nineteen hemorrhage lesions were identified on QSM images. Similarly,the calcified lesion was identified on QSM images but not on SWI phase images. Basedon our results, we conclude that QSM provides great utility in identificationof hemorrhage and calcification.&nbsp;</p

What does the water inside the brain tell us? Diffusion tensor imaging

The humanbrain consists of about 75 percent water. Diffusion tensorimaging (DTI) is an advanced magnetic resonance (MR) technique imaging that hasbeen developed for diagnostic and research in medicine. It can be use DTItractography to better understand degenerating axons of white matter lesions insome neurological diseases such as MS, AD, trauma, cerebral ischemia, epilepsy,brain tumors and metabolic disorders.</p

What does the water inside the brain tell us? Diffusion tensor imaging

The humanbrain consists of about 75 percent water. Diffusion tensorimaging (DTI) is an advanced magnetic resonance (MR) technique imaging that hasbeen developed for diagnostic and research in medicine. It can be use DTItractography to better understand degenerating axons of white matter lesions insome neurological diseases such as MS, AD, trauma, cerebral ischemia, epilepsy,brain tumors and metabolic disorders.</p

Automatic Blurry Colon Image Detection Using Laplacian Operator Based Features

Colonoscopy and wireless capsule endoscopy are the most common techniquesto monitor and detect abnormalities in the colon. During this process, probe orcapsule movement causes blurry images. Detection and removal of blurry imagesis critical for further automatic abnormality detection procedures. Severalmethods based on wavelet transform, Canny edge detection, discrete Fourier andcosine transform have been proposed so far. The Laplacian operator-basedapproaches have not been used on colon images yet. In this study, we extractedfour features from colon images based on Laplacian operator for thediscrimination of blurry images from visually normal images. The features werethe energy and variance of the Laplacian of the images, the average of pixelsobtained using diagonal and modified Laplacian operator. We used 80 frames (40of them were blurry) selected from videos that are available in an open-sourcedatabase (https://www.gastrointestinalatlas.com/). The features were utilizedas the inputs to various classification methods, where cubic support vectormachines resulted in the best performance. The classification accuracy valueswe obtained were 82.5%. The results of this study indicated that for theautomatic detection of blurry images in colon videos Laplacian operator-basedfeatures were feasible.</p

The Quality of Life and Depression Status of Healthcare Workers' Children in the COVID-19 Pandemic

Introduction: COVID-19 disease has adversely affected almost all families and children's physical, psychosocial, and mental health. We wanted to find out how the pandemic affected the quality of life and depression levels of the children of healthcare workers. Material and Methods: A survey of 287 children was conducted to compare the children of healthcare professionals and other occupational groups. The Children's Depression Inventory and Paediatric Quality of Life Inventory tests evaluated children's depression and quality of life. Results: Children's Depression Inventory scores were higher in children of COVID-19 infected healthcare workers than in the children of non-infected (p=0.04). The total Paediatric Quality of Life, Physical and Psychosocial Health Scores were lower in the healthcare workers' children (p=0.004, 0.01, 0.007). Conclusion: Children of healthcare workers are more affected physically and psychosocially than other children during the pandemic. Healthcare workers and their children will be motivated and encouraged if they are supported during the pandemic

Motion artifact detection in colonoscopy images

Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts’ do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts’ time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approac

Motion artifact detection in colonoscopy images

Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts' do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts' time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach

Endothelium-derived Microparticles Are Increased in Teenagers With Cobalamin Deficiency

Introduction: Vitamin B-12 (cobalamin) deficiency may be a significant cause of hyperhomocysteinemia, and high homocysteine (Hcy) levels are associated with an increased risk of cardiovascular disease. Endothelium-derived microparticles (EMPs) are a new marker in endothelial dysfunction and atherosclerosis, which play a role in cardiovascular diseases' pathogenesis. This study aimed to evaluate the EMPs, the markers of endothelial dysfunction and atherosclerosis, and lipid profile in teenagers with cobalamin deficiency. Materials and Methods: This prospective study included 143 teenagers, 75 vitamin B-12 deficient patients and 68 healthy controls between 11 and 18 years of age. Routine laboratory tests, hemogram, vitamin B-12, folic acid, ferritin, Hcy, lipid profile and EMPs were examined and compared. EMP subgroups were analyzed by flow cytometry method according to the expression of membrane-specific antigens. The microparticles released from the endothelium studied were VE-cadherin (CD144), S-endo1 (CD146), and Endoglin (CD105). Results: The present study demonstrates that circulating CD105+ EMP, CD144+ EMP, CD146+ EMPs, and Hcy were increased, and high-density lipoprotein (HDL) cholesterol was reduced in teenagers with cobalamin deficiency. Vitamin B-12 showed a negative correlation with EMPs and Hcy, positive correlation with folate and HDL. All EMPs showed a significant positive correlation with triglyceride, vitamin B-12, and HDL. Conclusion: Vitamin B-12 deficiency may predispose to endothelial damage and atherosclerosis by increasing EMPs and harms lipid metabolism in the long term