Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier
Magnetic resonance imaging (MRI) is effectively used for accurate diagnosis of acute
ischemic stroke. This paper presents an automated method based on computer aided …
ischemic stroke. This paper presents an automated method based on computer aided …
Application of machine learning techniques for characterization of ischemic stroke with MRI images: a review
Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its
manual interpretation by experts is arduous and time-consuming. Thus, there is a need for …
manual interpretation by experts is arduous and time-consuming. Thus, there is a need for …
A fully automated algorithm under modified FCM framework for improved brain MR image segmentation
Automated brain magnetic resonance image (MRI) segmentation is a complex problem
especially if accompanied by quality depreciating factors such as intensity inhomogeneity …
especially if accompanied by quality depreciating factors such as intensity inhomogeneity …
Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis
D Garcia-Lorenzo, S Prima, DL Arnold… - IEEE transactions on …, 2011 - ieeexplore.ieee.org
We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in
magnetic resonance images. The method performs tissue classification using a model of …
magnetic resonance images. The method performs tissue classification using a model of …
RP-Net: a 3D convolutional neural network for brain segmentation from magnetic resonance imaging
Quantitative analysis of brain volume is quite significant for the diagnosis of brain diseases.
Accurate segmentation of essential brain tissues from 3D medical images is fundamental to …
Accurate segmentation of essential brain tissues from 3D medical images is fundamental to …
Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images
Background Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have
generated increasing interest as an imaging marker of small vessel diseases, with relevance …
generated increasing interest as an imaging marker of small vessel diseases, with relevance …
A deep dense residual network with reduced parameters for volumetric brain tissue segmentation from MR images
Deep convolutional neural networks (DCNN) have proven to be the state-of-the-art methods
for brain tissue segmentation; however, their complex architectures, and the large number of …
for brain tissue segmentation; however, their complex architectures, and the large number of …
[HTML][HTML] Multiple sclerosis lesion detection in multimodal MRI using simple clustering-based segmentation and classification
Background Multiple sclerosis (MS) is an immune-mediated inflammatory disease that
attacks myelinated axons in the central nervous system, destroying myelin and axons to …
attacks myelinated axons in the central nervous system, destroying myelin and axons to …
Automated identification of brain tumors from single MR images based on segmentation with refined patient-specific priors
Brain tumors can have different shapes or locations, making their identification very
challenging. In functional MRI, it is not unusual that patients have only one anatomical …
challenging. In functional MRI, it is not unusual that patients have only one anatomical …
Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury
Purpose: To develop and compare an automated detection system for ischemic lesions in a
neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO‐H) from T2 …
neonatal model of bilateral carotid artery occlusion with hypoxia (BCAO‐H) from T2 …