Registration of brain images by a multi-resolution sequential method

2005 ◽  
pp. 165-174 ◽  
Author(s):  
M A Oghabian ◽  
A Todd-Pokropek
Keyword(s):  
Sequential Method ◽  
Brain Images

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Crystal Engineering Approach for Fabrication of Inverted Perovskite Solar Cell in Ambient Conditions

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1751
Author(s):  
Inga Ermanova ◽  
Narges Yaghoobi Nia ◽  
Enrico Lamanna ◽  
Elisabetta Di Bartolomeo ◽  
Evgeny Kolesnikov ◽  
...  
Keyword(s):  
Solar Cell ◽  
Crystal Engineering ◽  
High Performance ◽  
Perovskite Solar Cells ◽  
Perovskite Solar Cell ◽  
Layer By Layer ◽  
Ambient Conditions ◽  
Sequential Method ◽  
Engineering Approach ◽  
Hole Transporting

In this paper, we demonstrate the high potentialities of pristine single-cation and mixed cation/anion perovskite solar cells (PSC) fabricated by sequential method deposition in p-i-n planar architecture (ITO/NiOX/Perovskite/PCBM/BCP/Ag) in ambient conditions. We applied the crystal engineering approach for perovskite deposition to control the quality and crystallinity of the light-harvesting film. The formation of a full converted and uniform perovskite absorber layer from poriferous pre-film on a planar hole transporting layer (HTL) is one of the crucial factors for the fabrication of high-performance PSCs. We show that the in-air sequential deposited MAPbI3-based PSCs on planar nickel oxide (NiOX) permitted to obtain a Power Conversion Efficiency (PCE) exceeding 14% while the (FA,MA,Cs)Pb(I,Br)3-based PSC achieved 15.6%. In this paper we also compared the influence of transporting layers on the cell performance by testing material depositions quantity and thickness (for hole transporting layer), and conditions of deposition processes (for electron transporting layer). Moreover, we optimized second step of perovskite deposition by varying the dipping time of substrates into the MA(I,Br) solution. We have shown that the layer by layer deposition of the NiOx is the key point to improve the efficiency for inverted perovskite solar cell out of glove-box using sequential deposition method, increasing the relative efficiency of +26% with respect to reference cells.

scholarly journals Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation

2021 ◽  
pp. 135245852098863
Author(s):  
Frank Dahlke ◽  
Douglas L Arnold ◽  
Piet Aarden ◽  
Habib Ganjgahi ◽  
Dieter A Häring ◽  
...  
Keyword(s):  
Disease Activity ◽  
Brain Volume ◽  
Young Patients ◽  
Clinical Trial Data ◽  
Study Data ◽  
Phase Iii ◽  
Brain Images ◽  
Data Set ◽  
Brain Volume Loss ◽  
Phase Iii Study

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity.

scholarly journals Semantic Segmentation of Cerebellum in 2D Fetal Ultrasound Brain Images using Convolutional Neural Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Vishal Singh ◽  
Pradeeba Sridar ◽  
Jinman Kim ◽  
Ralph Nanan ◽  
N. Poornima ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Fetal Ultrasound ◽  
Brain Images

scholarly journals Meyer’s Loop Anatomy Demonstrated Using Diffusion Tensor MR Imaging and Fiber Tractography at 3T

2014 ◽  
Vol 60 (5) ◽  
pp. 215-222 ◽  
Author(s):  
Cristina Goga ◽  
Zeynep Firat ◽  
Klara Brinzaniuc ◽  
Is Florian
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Fiber Tractography ◽  
Brain Images ◽  
3 Dimensional ◽  
Software Release ◽  
Magnetic Resonance Imaging Mri ◽  
Meyer’S Loop

Abstract Objective: The ultimate anatomy of the Meyer’s loop continues to elude us. Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) may be able to demonstrate, in vivo, the anatomy of the complex network of white matter fibers surrounding the Meyer’s loop and the optic radiations. This study aims at exploring the anatomy of the Meyer’s loop by using DTI and fiber tractography. Methods: Ten healthy subjects underwent magnetic resonance imaging (MRI) with DTI at 3 T. Using a region-of-interest (ROI) based diffusion tensor imaging and fiber tracking software (Release 2.6, Achieva, Philips), sequential ROI were placed to reconstruct visual fibers and neighboring projection fibers involved in the formation of Meyer’s loop. The 3-dimensional (3D) reconstructed fibers were visualized by superimposition on 3-planar MRI brain images to enhance their precise anatomical localization and relationship with other anatomical structures. Results: Several projection fiber including the optic radiation, occipitopontine/parietopontine fibers and posterior thalamic peduncle participated in the formation of Meyer’s loop. Two patterns of angulation of the Meyer’s loop were found. Conclusions: DTI with DTT provides a complimentary, in vivo, method to study the details of the anatomy of the Meyer’s loop.

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