SEGMENTATION AND CLASSIFICATION OF ISCHEMIC STROKE USING OPTIMIZED FEATURES IN BRAIN MRI

Short stature is a common concern that necessitates pediatric endocrinology evaluation. Growth hormone (GH) deficiency is often included as an etiology. Brain and pituitary Magnetic Resonance Imaging (MRI) with gadolinium-based contrast agents (GBCAs) is the imaging modality of choice in assessing patients with GH deficiency. Given the significant strides made in MRI technology that allow improved spatial and contrast resolution, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. We preformed a retrospective review of otherwise healthy patients with short stature and/or GH deficiency who underwent brain and pituitary MRI without and with contrast, to assess the benefit of contrast administration. Introduction: Short stature is a common concern that necessitates pediatric endocrinology evaluation. The etiologies of short stature are diverse. GH deficiency is often included as an etiology although it accounts for only 1-2% of short stature cases. The prevalence of GH deficiency is reported to be ~ 1:3500. The vast majority of GH deficiency cases are idiopathic in nature with only 20% due to organic causes. The organic causes of GH deficiency include congenital central nervous system (CNS) anomalies, tumors and other pathologic conditions that involve the pituitary-hypothalamic region. As a result, the radiological assessment of the hypothalamic-pituitary region is considered standard of care for evaluating patients with GH deficiency. Although brain and pituitary MRI is the imaging modality of choice in assessing patients with GH deficiency, its yield in cases of isolated GH deficiency is very low. In a study of 40 otherwise normal patients with isolated GH deficiency, 35 (87.5%) had normal brain MRIs. The abnormal findings of brain MRI in the minority of isolated GH deficiency cases included pituitary hypoplasia, pituitary stalk agenesis, lack of the normal T1-weighted pituitary hyperintensity in the posterior part of the sella turcica, and the presence of a high-intensity signal at the infundibular level representing ectopic neurohypophysis. Traditionally, these brain and pituitary MRI images are obtained with the use of contrast material (gadolinium). The main purpose of using contrast material is for the evaluation of pituitary microadenomas. Given the fact that significant strides made in MRI technology and pituitary microadenomas are not appeared to be associated with GH deficiency, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. GBCAs have been shown to deposit in different tissues including the kidneys and the brain. The risk increases with repeated doses. The clinical significance of this deposition is unclear at this time but warrants caution especially in pediatric population who have a longer expected lifespan to manifest any delayed effects. Allergic reactions and gastrointestinal symptoms in pediatric patients can occur with GBCA administration, although the incidence is low. Using contrast material also increases the total cost of the MRI study and prolongs the time needed to complete it. Moreover, in order to use contrast material, intravenous venous (IV) access is required which causes discomfort and additional stress to children and their families. Therefore, we performed a retrospective review of otherwise healthy patients with short stature and/or growth hormone deficiency who underwent brain and pituitary MRI without and with contrast, to assess whether contrast administration led to diagnoses that would have otherwise been missed and/or impacted the patient’s clinical course.Objectives: - To compare the diagnostic yield of non-contrast MRI with pre and post-contrast MRI of the brain and pituitary in evaluation of pediatric patients with short stature and/or growth hormone deficiency.- A secondary objective is to measure the size of the pituitary gland and correlates it with peak growth hormone levels (using insulin/argenine). Methodology: We included patients who underwent brain/pituitary MRI with/without contrast performed at our institution between Jan 2013-Dec 2018 who have short stature/GH deficiency. We excluded patients with known diagnosis of other pituitary hormone deficiencies prior to obtaining MRI studies, genetic and neurological disorders, known tumors/malignancies of any type, or renal failure. Two pediatric neuroradiologists independently reviewed the brain and pituitary MRI of these patients (each read 50% of the cohort) blinded to the clinical data and diagnoses. Each radiologist initially reviewed only the non-contrast portions of the studies, and subsequently, the same radiologist reviewed the entire study, including pre- and post-contrast portions in a separate session. The two sessions were 6 weeks apart to avoid recall bias. Several imaging findings including size and morphology of pituitary gland, presence of congenital anomalies or focal lesions and any associated intracranial findings systematically recorded, and subsequently analyzed. Hypotheses: 1.The incidence of finding congenital pituitary cysts is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 2.The incidence of discovering abnormal infundibulum is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 3.Small pituitary size correlate with GH deficiency. Results: -We identified 327 patients with short stature/GH deficiency from Jan 2013-Dec 2018-224 (68.5%) are males and 103 (31.5%) are females. -The mean age at the time of imaging is 10 years and the median is 11 years. -161 (49.24%) have height z-score < -2.25 and 166 (50.76%) have height z-score > -2.25.-82 (25.07%) have IGF1 z-score for age < -2, 102 (31.19%) have z-score ≥-2 to ≤ -1, 141 (43.12%) have z-score > -1 and 2 (0.62%) have no level done.-63 (19.27%) have GH peak <5, 87 (26.61%) have GH peak 5-7.99, 53 (16.21%) have GH peak 8-9.99, 30 (9.17%) have GH peak > 10 and 94 (28.75%) did not undergo GH provocative testing. -The kappa coefficient for pars intermedia cyst on pre vs. post contrast imaging is 0.74 and 0.55 for the infundibulum on pre vs. post contrast imaging. -The mean pituitary height for patients with IGF z-score < -2 is 3.9 mm, 4 mm for z-score ≥- 2 to ≤ -1 and 4.3 mm for z-score > -1-The mean pituitary height for patients with peak GH < 5 is 3.8 mm, 4.2 mm for peak 5-7.99, 4.3 mm for peak 8-9.99 and 4.4 mm for peak > 10. Conclusion: This question has not been answered or even raised in the literature. Our findings suggest that the there is no added benefit to use gadolinium when obtaining brain/pituitary MRI for the evaluation of GH deficiency/short stature. Furthermore, it seems that there is an association between the pituitary height and the GH status of the cohort which is in line with previous published studies.

OBJECTIVESThe Oxfordshire Community Stroke Project (OCSP) classification is a simple clinical scheme for subdividing first ever acute stroke. Several small studies have shown that when an infarct is visible on...

Background: Fatigue is a common and distressing consequence of stroke, and the aetiology of post-stroke fatigue (PSF) is poorly understood. It is unclear whether chronic brain changes [cerebral atrophy and white matter lesions (WML)], stroke lesion location or certain clinical features are related to its development. The aim of this study was to identify, in patients with acute stroke, whether features in different brain regions on routine CT imaging or routinely collected clinical features predicted PSF at 1 month. Methods: In total, 107 patients (62% male) with acute ischaemic or haemorrhagic stroke were assessed for fatigue (Fatigue Assessment Scale), anxiety and depression (Hospital Anxiety and Depression Scale) at 1 month. Admission brain CT was rated using a structured scoring system for (i) severity of atrophy and (ii) severity of WML in different regions of the brain, and (iii) site of acute and previous vascular lesions. Results: Cerebral atrophy of mild or greater severity was present in 84 patients (77.5%) and WML of mild or greater severity was present in 54 patients (50.5%) in at least one of the evaluated brain regions. There was no association between PSF and severity of atrophy or WML, or presence of acute or previous vascular lesions. We used the Oxfordshire Community Stroke Project (OCSP) classification to explore the possible influence of lesion location because a minority of the patients (37.4%) had visible acute lesions. Fatigue scores were higher in patients with clinically diagnosed posterior strokes (p = 0.046), in females (p = 0.05) and in those with higher depression and anxiety scores (ρ = 0.52; p < 0.001 and ρ = 0.49; p < 0.001, respectively). Structural CT variables were not significant predictors of fatigue (log FAS) in a linear regression which controlled for age, sex, pre-stroke fatigue, OCSP classification, depression and anxiety. The significant predictors of fatigue were depression (β = 0.30; p = 0.007) and anxiety (β = 0.28; p = 0.013; adjusted R² = 0.254). Stroke subtype (according to the OCSP classification) was marginally predictive (β = 0.17; p = 0.05) and sex was not statistically significant (β = 0.15; p = 0.08). Conclusions: Features on routine post-stroke CT do not appear to associate with fatigue at 1 month. However, clinically diagnosed posterior strokes as well as female gender, anxiety and depression may be linked with fatigue. Therefore, clinical vigilance rather than CT features should be used to predict fatigue early after stroke. Further research is needed in this area to establish whether biological mechanisms underlie the development of PSF.

This study investigated the effect of a new approach, the support vector machine, as a classifier tool to identify the weeds in corn fields at early growth stage. Image segmentation was done by transforming original color images to gray level images according to the statistical values of red, green, blue components. The Gray Level Co-occurrence Matrix (GLCM) and statistical properties of the histogram from the gray level images were further used to obtain the texture features of the weeds and corn seedlings. These texture features were used in the classification procedure. Principle component analysis was used to select the texture features according to their better contributions to reduce space dimensions. A Support Vector Machine (SVM) classifier was employed to recognize the weeds and the corn seedlings. The results indicated that the SVM classifiers with different feature selections could identify successfully weed-corn with a higher accuracy ranged from 92.31 to 100%. A comparison study of the recognition capabilities of SVM and back-propagation (BP) neural-network classifier using the same data set was conducted. It was found that the SVM classifier provided the best recognition performance with an accuracy of 100%, which exceeded the accuracy of 80% given by the BP classifier. Key words: Corn seedling, weeds, texture, support vector machine, recognize.

Validation of the Oxfordshire Community Stroke Project syndrome diagnosis derived from a standard symptom list in acute stroke

According to the World Health Organization, cancer is the second most common cause of death worldwide (WHO). Death from cancer may sometimes be avoided through early diagnosis, although this is not always practicable. A tumour, unlike cancer, may be benign, pre-cancerous, or malignant. Contrary to malignant tumours, benign tumours may frequently be surgically removed and seldom metastasize to other organs or tissues. The method most often used to differentiate between various types of tumours is magnetic resonance imaging (MRI) (MRI). But because it depends on human subjectivity, it could be challenging for one person to see a lot of data. The radiologist's ability to identify brain tumours early mostly depends on their level of training. Before determining whether the tumour is benign or malignant, the diagnostic process for it could not be finished. A biopsy is often carried out to determine if the tissue is benign or cancerous. The biopsy of a brain tumour is often delayed until the last brain operation, in contrast to tumours discovered elsewhere in the body. An efficient diagnostics tool for tumour segmentation and classification from MRI images is crucial to obtaining accurate diagnoses, avoiding surgery, and eliminating subjectivity. The classification and segmentation of the tumour part of the MRI brain picture is the goal of this study. Prior to feature extraction in the first study, pre-processing is done using the Gray Level Co-occurrence Matrix (GLCM). Based on these features, the data is then separated into normal, glioma, meningioma, and pituitary groups. In prior studies, classification was handled by traditional Deep Neural Networks (DNN), with the best weights selected using optimization methods. Accordingly, a recent study recommends including Long Short-Term Memory based on Recurrent Neural Networks (RNN) (LSTM). The Local Direction Ternary Pattern (LDTP), the Gray Level Co-occurrence Matrix (GLCM), and the Le-Net features are all combined in the proposed Hybrid GLCM-LDTP-Le-Net Feature extraction approach. The hybrid parameters produced by the feature extraction process and the brain MRI data are used by the RNN-LSTM model to categorise the data as benign or malignant. By removing mixed features from the input data, the suggested hybrid feature extraction improves the learnt models' accuracy. The accuracy of 98.99 % of the suggested hybrid feature extraction approach was higher than the current CNN-based model methodologies.

Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier

Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques

Aim This study aims to investigate whether machine learning (ML) could identify the volumetric changes in the corpus callosum (CC) and its sub-regions (genu, body, and splenium) due to aging and sex differences. Material and Methods Brain magnetic resonance imaging (MRI) images obtained from 301 healthy male and female subjects were used in the study. The volume measurements of the corpus callosum and its sub-regions were calculated from MRI images using MRICloud software. The classifications of age (young/adult) and sex (female/male) were performed using the Classification Learner Tool in MATLAB 2020b (MathWorks, Natick, MA, USA). Classifiers including k-Nearest Neighbor (KNN), support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and Ensemble classifiers were evaluated using 10-fold cross-validation. Results Machine learning classification revealed moderate accuracy in distinguishing sex (best: Fine Gaussian SVM, 65.4% accuracy, AUC = 0.60) and higher accuracy in distinguishing age groups (young/adult; best: Fine Gaussian SVM, 83.7% accuracy, area under the curve (AUC) = 0.67) based on corpus callosum and its sub-region volumes derived from MRI. Conclusion This preliminary data suggests that ML can provide indications to assess the impact of age and sex on corpus callosum volume based on MRI data. The observed classification accuracies, particularly for sex, suggest that larger datasets are needed to enhance the accuracy of these models in future investigations.

Background: Neurocognitive disorder (NCD) is common after stroke, with major NCD appearing in about 10% of survivors of a first-ever stroke. We aimed to classify clinical- and imaging factors related to rapid development of major NCD 3 months after a stroke, so as to examine the optimal composition of factors for predicting rapid development of the disorder. We hypothesized that the prediction would mainly be driven by neurodegenerative as opposed to vascular brain changes.Methods: Stroke survivors from five Norwegian hospitals were included from the “Norwegian COgnitive Impairment After STroke” (Nor-COAST) study. A support vector machine (SVM) classifier was trained to distinguish between patients who developed major NCD 3 months after the stroke and those who did not. Potential predictor factors were based on previous literature and included both vascular and neurodegenerative factors from clinical and structural magnetic resonance imaging findings. Cortical thickness was obtained via FreeSurfer segmentations, and volumes of white matter hyperintensities (WMH) and stroke lesions were semi-automatically gathered using FSL BIANCA and ITK-SNAP, respectively. The predictive value of the classifier was measured, compared between classifier models and cross-validated.Results: Findings from 227 stroke survivors [age = 71.7 (11.3), males = (56.4%), stroke severity NIHSS = 3.8 (4.8)] were included. The best predictive accuracy (AUC = 0.876) was achieved by an SVM classifier with 19 features. The model with the fewest number of features that achieved statistically comparable accuracy (AUC = 0.850) was the 8-feature model. These features ranked by their weighting were; stroke lesion volume, WMH volume, left occipital and temporal cortical thickness, right cingulate cortical thickness, stroke severity (NIHSS), antiplatelet medication intake, and education.Conclusion: The rapid (<3 months) development of major NCD after stroke is possible to predict with an 87.6% accuracy and seems dependent on both neurodegenerative and vascular factors, as well as aspects of the stroke itself. In contrast to previous literature, we also found that vascular changes are more important than neurodegenerative ones. Although possible to predict with relatively high accuracy, our findings indicate that the development of rapid onset post-stroke NCD may be more complex than earlier suggested.

ABSTRACTTumor detection and segmentation are essential for cervical cancer (CC) treatment and diagnosis. This study presents a model that segmented the tumor, uterus, and vagina based on deep learning automatically on magnetic resonance imaging (MRI) images of patients with CC. The tumor detection dataset consists of 68 CC patients' diffusion‐weighted magnetic resonance imaging (DWI) images. The segmented dataset consists of 73 CC patients' T2‐weighted imaging (T2WI) images. First, the three clear images of the patient's DWI images are detected using a single‐shot multibox detector (SSD). Second, the serial number of the clearest image is obtained by scores, while the corresponding T2WI image with the same serial number is selected. Third, the selected images are segmented by employing the semantic segmentation (U‐Net) model with the squeeze‐and‐excitation (SE) block and attention gate (SE‐ATT‐Unet). Three segmentation models are implemented to automatically segment the tumor, uterus, and vagina separately by adding different attention mechanisms at different locations. The target detection accuracy of the model is 92.32%, and the selection accuracy is 90.9%. The dice similarity coefficient (DSC) on the tumor is 92.20%, pixel accuracy (PA) is 93.08%, and the mean Hausdorff distance (HD) is 3.41 mm. The DSC on the uterus is 93.63%, PA is 91.75%, and the mean HD is 9.79 mm. The DSC on the vagina is 75.70%, PA is 85.46%, and the mean HD is 10.52 mm. The results show that the proposed method accurately selects images for segmentation, and the SE‐ATT‐Unet is effective in segmenting different regions on MRI images.

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data.

Magnetic Resonance Imaging (MRI) might be a problematic assignment for tumor fluctuation and complexity because of brain image classification. This work presents the brain tumor classification system using Convolutional Gated Recurrent Neural Network (CGRNN) algorithm based on MRI images. The proposed tumor recognition framework comprises of four stages, to be specific preprocessing, feature extraction, segmentation and classification. Extraction of identified tumor framework features was accomplished utilizing Gray Level Co-occurrence Matrix (GLCM) strategy. At long last, the Convolutional Gated Recurrent Neural Network Classifier has been created to perceive various kinds of brain disease. The proposed framework can be effective in grouping these models and reacting to any variation from the abnormality. The entire framework is isolated into different types of phases: the Learning/Training Phase and the Recognition/Test Phase. A CGRNN classifier under the scholarly ideal separation measurements is utilized to decide the chance of every pixel having a place with the foreground (tumor) and the background. MATLAB software is used in the development of the simulation of the proposed system. The suggested method's simulation results show that the analysis of brain tumours is stable. It shows that the proposed brain tumor classifications are superior to those from brain MRIs than existing brain tumor classifications. The overall accuracy of the proposed system is 98.45%.

Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, as projected in the related Magnetic Resonance Imaging (MRI). The early identification of AD is essential for preventive treatment; thus, different machine/deep learning (ML/DL) approaches applied on MRI scans from patients at different AD stages have been proposed in recent years. Here, a new method, namely CurvMRI, for AD detection from MRI images using Fast Curvelet Transform (FCT) is proposed. The approach is realized via a sequence of steps, i.e., feature extraction, feature reduction, and classification. MRI images are obtained from a Kaggle dataset containing five AD stages, from where Cognitive Normal (CN) (493/87 (training/testing)) and AD (145/26) MRI images were selected for binary classification. The FCT with wrapping method was implemented, and higher-order statistics, such as kurtosis and skewness, as well as energy and variance, were then used to extract features from the curvelet sub-bands. Features were then concatenated and fed to a Support Vector Machine (SVM) classifier, giving an accuracy of 77.6%, which outperforms the most common DL classification approaches applied to the same dataset. These results showcase the potentiality of the proposed CurvMRI to efficiently discriminate AD from CN in MRI images, and provide a fast and easy to implement ML tool for assisting physicians in AD detection.

Here, a brain tumor classification method using the support vector machine (SVM) algorithm by utilizing discrete wavelet transform (DWT) transformation and feature extraction of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) has been implemented using the magnetic resonance imaging (MRI) image belong to the low-grade glioma (LGG) or high-grade glioma (HGG) group. SVM algorithm used as a classification method has been widely used in research that raises the topic of classification. Through the formation of a hyperplane between 2 data classes, the SVM algorithm can be said to be a reliable method but does not require complicated computations. The DWT transformation is intended to provide clearer feature details from the MRI image, so that when the feature extraction algorithm is applied, it is expected that the extracted features will differ between benign tumor MRI images and malignant tumor MRI images. In 1 level DWT using high-low (HL) sub-band yield the highest specificity, sensitivity, and accuracy than using 3 levels using HL or low-high (LH) sub-band in LGG MRI image.Compared with another research, our proposed method is slightly better in terms of accuracy to classify the brain tumor image with achieved the accuracy of 98.6486%.