scholarly journals Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

2020 ◽  
Vol 10 (2) ◽  
pp. 116
Author(s):  
Yu Wang ◽  
Qi Qi ◽  
Xuanjing Shen
Keyword(s):  
Brain Mri ◽  
Texture Features ◽  
Directional Pattern ◽  
Input Image ◽  
Segmentation Algorithm ◽  
Brain Atlas ◽  
Superpixel Segmentation ◽  
Simple Linear Iterative Clustering ◽  
Fuzzy Regions ◽  
Magnetic Resonance Imaging Mri

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

scholarly journals A novel framework for efficient identification of brain cancer region from brain MRI

2019 ◽  
Vol 9 (2) ◽  
pp. 1410 ◽  
Author(s):  
Parvathi Angadi ◽  
M Nagendra ◽  
Hanumanthappa M
Keyword(s):  
Brain Cancer ◽  
Directed Acyclic Graph ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Cost Effective ◽  
Input Image ◽  
Accuracy Level ◽  
Significant Research ◽  
Magnetic Resonance Imaging Mri ◽  
Detection Schemes

Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.

scholarly journals EXTH-30. EXPANDING THE UTILITY OF PRE-CLINICAL CONTRAST ENHANCED CT (CE-CT) FOR TUMOR DETECTION IN ORTHOTOPIC GBM MODELS USING RADIOMICS

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii93-ii93
Author(s):  
Kate Connor ◽  
Emer Conroy ◽  
Kieron White ◽  
Liam Shiels ◽  
William Gallagher ◽  
...  
Keyword(s):  
Imaging Modality ◽  
Texture Features ◽  
Tumor Detection ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Pet Ct ◽  
Enhanced Ct ◽  
Magnetic Resonance Imaging Mri ◽  
Work Supports ◽  
Selection Of

Abstract Despite magnetic resonance imaging (MRI) being the gold-standard imaging modality in the glioblastoma (GBM) setting, the availability of rodent MRI scanners is relatively limited. CT is a clinically relevant alternative which is more widely available in the pre-clinic. To study the utility of contrast-enhanced (CE)-CT in GBM xenograft modelling, we optimized CT protocols on two instruments (IVIS-SPECTRUM-CT;TRIUMPH-PET/CT) with/without delivery of contrast. As radiomics analysis may facilitate earlier detection of tumors by CT alone, allowing for deeper analyses of tumor characteristics, we established a radiomic pipeline for extraction and selection of tumor specific CT-derived radiomic features (inc. first order statistics/texture features). U87R-Luc2 GBM cells were implanted orthotopically into NOD/SCID mice (n=25) and tumor growth monitored via weekly BLI. Concurrently mice underwent four rounds of CE-CT (IV iomeprol/iopamidol; 50kV-scan). N=45 CE-CT images were semi-automatically delineated and radiomic features were extracted (Pyradiomics 2.2.0) at each imaging timepoint. Differences between normal and tumor tissue were analyzed using recursive selection. Using either CT instrument/contrast, tumors > 0.4cm3 were not detectable until week-9 post-implantation. Radiomic analysis identified three features (waveletHHH_firstorder_Median, original_glcm_Correlation and waveletLHL_firstorder_Median) at week-3 and -6 which may be early indicators of tumor presence. These features are now being assessed in CE-CT scans collected pre- and post-temozolomide treatment in a syngeneic model of mesenchymal GBM. Nevertheless, BLI is significantly more sensitive than CE-CT (either visually or using radiomic-enhanced CT feature extraction) with luciferase-positive tumors detectable at week-1. In conclusion, U87R-Luc2 tumors > 0.4cm3 are only detectable by Week-8 using CE-CT and either CT instrument studied. Nevertheless, radiomic analysis has defined features which may allow for earlier tumor detection at Week-3, thus expanding the utility of CT in the preclinical setting. Overall, this work supports the discovery of putative prognostic pre-clinical CT-derived radiomic signatures which may ultimately be assessed as early disease markers in patient datasets.

scholarly journals Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Norio Takata ◽  
Nobuhiko Sato ◽  
Yuji Komaki ◽  
Hideyuki Okano ◽  
Kenji F. Tanaka
Keyword(s):  
Mouse Brain ◽  
Brain Structures ◽  
Brain Atlas ◽  
Resting State Functional Connectivity ◽  
Brain Science ◽  
Step Procedure ◽  
Public Resources ◽  
Large Brain ◽  
Magnetic Resonance Imaging Mri ◽  
And Function

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

scholarly journals Methomyl poisoning presenting with decorticate posture and cortical blindness

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Chih-Ming Lin
Keyword(s):  
Brain Mri ◽  
Vital Signs ◽  
Altered Mental Status ◽  
Cortical Blindness ◽  
High Signal ◽  
Blurred Vision ◽  
Cerebral Disease ◽  
Cerebral Magnetic Resonance Imaging ◽  
Neurological Effects ◽  
Magnetic Resonance Imaging Mri

Methomyl is a potent pesticide that is widely used in the field of agriculture. The systemic toxic effects of methomyl have been well described. However, the neurological effects of methomyl intoxication are not well understood. In this study, we report a 61-year-old Taiwanese man sent to our emergency department because of altered mental status. His family stated that he had consumed liquid methomyl in a suicide attempt. He was provided cardiopulmonary resuscitation because of unstable vital signs. He was then sent to an intensive care unit for close observation. On the second day of admission, he regained consciousness but exhibited irregular limb and torso posture. On the sixth day, he started to complain of blurred vision. An ophthalmologist was consulted but no obvious abnormalities could be identified. On suspicion of cerebral disease, a neurologist was consulted. Further examination revealed cortical blindness and decorticate posture. Cerebral magnetic resonance imaging (MRI) was arranged, which identified bilateral occipital regions lesions. The patient was administered normal saline and treated with aspirin and piracetam for 3 weeks in hospital. During the treatment period, his symptom of cortical blindness resolved, whereas his decorticate posture was refractory. Follow-up brain MRI results supported our clinical observations by indicating the disappearance of the bilateral occipital lesions and symmetrical putaminal high signal abnormalities. In this article, we briefly discuss the possible mechanisms underlying the cerebral effects of methomyl poisoning. Our study can provide clinicians with information on the manifestations of methomyl intoxication and an appropriate treatment direction.

Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

2021 ◽  
Vol 12 (3) ◽  
pp. 185-207
Author(s):  
Anjali A. Shejul ◽  
Kinage K. S. ◽  
Eswara Reddy B.
Keyword(s):  
Feature Extraction ◽  
Age Estimation ◽  
Learning Algorithm ◽  
Directional Pattern ◽  
Input Image ◽  
Deep Belief Network ◽  
Appearance Model ◽  
Belief Network ◽  
Sine Cosine Algorithm ◽  
New Feature

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

Pediatric Reversible Cerebral Vasoconstriction Syndrome: Two Unique Cases with a Review of all Reported Children

2021 ◽  
Author(s):  
Neelu Desai ◽  
Rahul Badheka ◽  
Nitin Shah ◽  
Vrajesh Udani
Keyword(s):  
Magnetic Resonance ◽  
Cerebral Arteries ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Reversible Cerebral Vasoconstriction Syndrome ◽  
Neurological Deficits ◽  
Cerebral Vasoconstriction ◽  
Magnetic Resonance Imaging Mri ◽  
Reversible Encephalopathy ◽  
Mr Angiogram

AbstractReversible cerebral vasoconstriction syndrome (RCVS) has been well described in adults, but pediatric cases are yet under recognized. We describe two children with RCVS and review similar already published pediatric cases. The first patient was a 10-year-old girl who presented with severe headaches and seizures 3 days after blood transfusion. Brain magnetic resonance imaging (MRI) showed changes compatible with posterior reversible encephalopathy syndrome and subarachnoid hemorrhage. Magnetic resonance angiogram showed diffuse vasoconstriction of multiple cerebral arteries. The second patient was a 9-year-old boy who presented with severe thunderclap headaches. Brain MRI showed isolated intraventricular hemorrhage. Computed tomography/MR angiogram and digital subtraction angiogram were normal. A week later, he developed focal neurological deficits. Repeated MR angiogram showed diffuse vasospasm of multiple intracranial arteries. Both children recovered completely. A clinico-radiological review of previously reported childhood RCVS is provided.

scholarly journals Anfis to Detect Brain Tumor Using MRI

2018 ◽  
Vol 7 (3.27) ◽  
pp. 209
Author(s):  
Susmita Mishra ◽  
M Prakash ◽  
A Hafsa ◽  
G Anchana
Keyword(s):  
Fuzzy Logic ◽  
Brain Tumor ◽  
Graphical Representation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
Histogram Equalization ◽  
Input Image ◽  
Inference System ◽  
Magnetic Resonance Imaging Mri ◽  
Contrast Stretching

Processing of Magnetic Resonance Imaging(MRI) is one of the widely known best techniques to diagnose brain tumor since it gives better results than ultrasound or X-Ray images. The main objective is to diagnose the presence and extraction of brain tumor using MRI images. Image preprocessing includes contrast stretching, noise filtering and Adaptive Histogram Equalization(AHE). AHE gives a graphical representation of digital image without enhancing above the desired level. The next stage involves transferring the redundant information in input image to reduced set of features is called feature selection and is done by color, shape or texture of an image. Image is segmented using incorporation of Artificial Neural Networks(ANN) and Fuzzy logic called Adaptive Neuro-Fuzzy Inference System(ANFIS) wherein we get the desired output to differentiate tumor affected and normal image with its severity level. Since we deal with uncertainty much more, fuzzy logic serves as a vibrant tool in representing human knowledge as IF-THEN rules. MATLAB has been implemented in detection and extraction of tumor at an early stage. 

scholarly journals Prevalence and Clinical Implications of Incidentally Detected Parotid Lesions as Blind Spot on Brain MRI: A Single-Center Experience

Medicina ◽  
2021 ◽  
Vol 57 (8) ◽  
pp. 836
Author(s):  
In-Chul Nam ◽  
Hye-Jin Baek ◽  
Kyeong-Hwa Ryu ◽  
Jin-Il Moon ◽  
Eun Cho ◽  
...  
Keyword(s):  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Blind Spot ◽  
Clinical Implications ◽  
Single Center Experience ◽  
Superficial Lobe ◽  
Mean Size ◽  
Magnetic Resonance Imaging Mri ◽  
Clinical Awareness ◽  
Low Prevalence

Background and objective: This study was conducted to assess the prevalence and clinical implications of parotid lesions detected incidentally during brain magnetic resonance imaging (MRI) examination. Materials and Methods: Between February 2016 and February 2021, we identified 86 lesions in the brain MRI reports of 84 patients that contained the words “parotid gland” or “PG”. Of these, we finally included 49 lesions involving 45 patients following histopathological confirmation. Results: Based on the laboratory, radiological or histopathological findings, the prevalence of incidental parotid lesions was low (1.2%). Among the 45 study patients, 41 (91.1%) had unilateral lesions, and the majority of the lesions were located in the superficial lobe (40/49, 81.6%). The mean size of the parotid lesions was 1.3 cm ± 0.4 cm (range, 0.5 cm–2.8 cm). Of these, 46 parotid lesions (93.9%) were benign, whereas the remaining three lesions were malignant (6.1%). Conclusions: Despite the low prevalence and incidence of malignancy associated with incidental parotid lesions detected on brain MRI, the clinical implications are potentially significant. Therefore, clinical awareness and appropriate imaging work-up of these lesions are important for accurate diagnosis and timely management.

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