Abnormal Brain Magnetic Resonance Research Articles

Retrospective Evaluation of Patients Admitted to the Pediatric Neurology Outpatient Clinic with Headache: Experience of a Tertiary Hospital.

It was aimed to determine the etiological and clinical features of pediatric patients with headache complaints. The files of patients who were admitted to the pediatric neurology outpatient clinic with headache were reviewed retrospectively. Patients' age, gender, features of headache, symptoms accompanying headache, available blood tests, brain magnetic resonance (MR) and electroencephalography (EEG) results were recorded. Of the total 470 patients, aged between 3 and 17 years, 291 (61.9%) were female and 179 (39.1%) were male. The mean age of the patients was 12.38±3.45 years. According to age groups, there were 16 (3.4%) patients under the age of 5, 159 (33.8%) between the ages of 6-11, and 295 (62.8%) patients aged 12-17 years. While 289 (61.5%) patients were diagnosed with primary headache, 122 (26.0%) patients were diagnosed with secondary headache, and headaches of 59 (12.5%) patients could not be classified. The most common primary headaches were tension-type headache (TTN) (n=177, 37.7%) and migraine (n=111, 23.6%). The 86 (70.5%) of the patients with secondary headache were diagnosed with sinusitis. Abnormal neurological examination finding was determined in 8 (1.7%) patients. Brain MR was performed in 439 (93.4%) of all patients and abnormal brain MR findings were detected in 52 (11.8%) patients. EEG was performed in 205 (43.6%) of all patients and abnormal EEG findings were detected in 24 (11.7%) patients. According to age groups, headache was most common in the 12-17 age group. The most common causes of headache were TTN and migraine, respectively. The most common secondary headache cause was sinusitis. We think that physical and neurological examination still maintains its priority in determining the causes of headache.

Expansion of the mutation spectrum and phenotype of USP7-related neurodevelopmental disorder.

Hao-fountain syndrome (HAFOUS) is a neurodevelopmental syndrome characterized by global developmental and severe language delays, behavioral abnormalities (including autism), and mild dysmorphic impairment of intellectual development. It is a dominant genetic disease caused by USP7 gene (*602519) mutations on chromosome 16p13.2. So far, only 15 cases with 14 deleterious variants in the USP7 gene have been reported. This study describes three unrelated patients with USP7 variants. Besides, we identified novel de novo heterozygous USP7 variants using trio-whole exome sequencing and verified by Sanger sequencing. Furthermore, clinical characteristics were evaluated by reviewing the medical records. The three identified variants, i.e., one frameshift variant (c.247_250del, p.Glu83Argfs × 18) and two missense variants (c.992A > G, p.Tyr331Cys; c.835T > G, p.Leu279Val) are unreported. The predominant clinical manifestations of the three patients included: DD/ID; language impairment; abnormal behavior; abnormal brain magnetic resonance (dilation of lateral ventricles, dilation of Virchow-Robin spaces, dilated the third ventricle, abnormal cerebral white matter morphology in bilateral occipital lobes, hypodysplasia of the corpus callosum, arachnoid cyst, delayed myelination, and widened subarachnoid space); some also had facial abnormalities. In summary, DD/ID is the most prevalent clinical phenotype of HAFOUS, although some patients also exhibit language and behavioral abnormalities. For the first time in China, we identified three variants of the USP7 gene using whole-genome sequence data. This work expands the USP7 gene mutation spectrum and provides additional clinical data on the clinical phenotype of HAFOUS.

An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm

The segmentation and classification of brain magnetic resonance (MR) images are the crucial and challenging task for radiologists. The conventional methods for analyzing brain images are time-consuming and ineffective in decision-making. Thus, to overcome these limitations, this work proposes an automated and robust computer-aided diagnosis (CAD) system for accurate classification of normal and abnormal brain MR images. The proposed CAD system has the ability to assist the radiologists for diagnosis of brain MR images at an early stage of abnormality. Here, to improve the quality of images before their segmentation, contrast limited adaptive histogram equalization (CLAHE) is employed. The segmentation of the region of interest is obtained using the multilevel Otsu's thresholding algorithm. In addition, the proposed system selects the most significant and relevant features from the texture and multiresolution features. The multiresolution features are extracted using discrete wavelet transform (DWT), stationary wavelet transform (SWT), and fast discrete curvelet transform (FDCT). Moreover, the Tamura and local binary pattern (LBP) are used to extract the texture features from the images. These features are used to classify the brain MR images using feedforward neural network (FNN) classifier, where different meta-heuristic optimization algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and gbest-guided gravitational search algorithm (GG-GSA) are employed for optimizing the weights and biases of FNN. The extensive experimental results on DS-195, DS-180, and three standard datasets show that the classification accuracy of GG-GSA based FNN classifier outperforms all mentioned meta-heuristic-based classifiers and several state-of-the-art methods.

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model

Purpose Automated classification of brain magnetic resonance (MR) images has been an extensively researched topic in biomedical image processing. In this work, we propose a new approach for classifying normal and abnormal brain MR images using bi-dimensional empirical mode decomposition (BEMD) and autoregressive (AR) model

Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM

Image classification is one of the typical computational applications widely used in the medical field, especially for abnormality detection in magnetic resonance (MR) brain images. Medical image classification is a pattern recognition technique in which different images are categorized into several groups based on some similarity measures. One of the significant applications is the tumor type identification in abnormal MR brain images. The proposed multi-class brain tumor classification system comprises feature extraction and classification. In feature extraction, the attributes of the co-occurrence matrix and the histogram are represented within the feature vector. In this work, the advantage of both co-occurrence matrix and histogram to extract the texture feature from every segment is used for better classification. In classification, the fuzzy logic-based hybrid kernel is designed and applied to train the support vector machine for automatic classification of four different types of brain tumors such as Meningioma, Glioma, Astrocytoma, and Metastases. Based on the experimental results, the proposed brain tumor classification method is more robust than other traditional methods in terms of the evaluation metrics, sensitivity, specificity, and accuracy.

Fuzzy Based Experimental Verification of Significance of Skull Tissue removal in Brain Tumor Image segmentation

In anatomical aspects, magnetic resonance (MR) imaging offers more accurate information for medical examination than other medical images such as X-ray, ultrasonic and CT images. Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One of the reasons behind the inferior segmentation efficiency is the presence of artifacts in the MR images. One such artifact is the extracranial tissues (skull). These extracranial tissues often interfere with the normal tissues during segmentation that accounts for the inferior segmentation efficiency. In this paper, an automated segmentation and lesion detection algorithm for high segmentation efficiency is proposed for abnormal MR brain images. The proposed segmentation algorithm consists of three steps. In the first step, extracranial tissues are removed using morphological operations. In the second step, fuzzy C-means algorithm is used to segment the MR brain images into four groups: white matter, gray matter, cerebrospinal fluid and the abnormal tumor region. Finally, pseudo-colouring operation is performed on the segmented image to detect the abnormal tumor region. The proposed method has been applied to abnormal images from four different types namely metastase, meningima, glioma and astrocytoma.the superior nature of the proposed approach is justified by performing a comparative analysis on skull stripped images and non-skull stripped images. Experimental results suggest that the proposed approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy.