scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals A Professional Estimate on the Segmentation of Brain Cancer in MR Images using M-FCM

2020 ◽  
Vol 9 (1) ◽  
pp. 2425-2430
Keyword(s):  
Brain Cancer ◽  
Computer Software ◽  
Ground Truth ◽  
Magnetic Resonance Images ◽  
Morphological Operations ◽  
Mr Images ◽  
Segmentation Accuracy ◽  
Software Code ◽  
Brain Mr Images ◽  
The Brain

Brain imaging innovations have been forever made for a significant part in analyzing and focusing the unused sees of the brain life systems and functions. A computer software code is designed for the detection of cancer in brain magnetic resonance images. Image segmentation, morphological operations and feature extraction are some of the image processing methods developed for the brain cancer detection in MR images concerning the cancer influenced sufferers. In the proposed research, a Modified morphological-based Fuzzy-C-Means (MFCM) algorithm is proposed to segment the cancer region in the brain MR images. M-FCM algorithm is used to perform the segmentation process significantly through the idealize choice of a cluster, based on the updated membership function. Quantitative analysis between ground truth and segmented cancer is presented in terms of segmentation accuracy and segmentation sensitivity

THE SEGMENTATION OF BRAIN MR IMAGES USING REFORMATIVE EXPECTATION-MAXIMIZATION ALGORITHM

2010 ◽  
Vol 10 (02) ◽  
pp. 289-297
Author(s):  
JIE WU ◽  
JIABI CHEN ◽  
XUELONG ZHANG ◽  
JINGHAI CHEN
Keyword(s):  
Expectation Maximization ◽  
Brain Mri ◽  
Expectation Maximization Algorithm ◽  
Test Results ◽  
Mr Images ◽  
Mri Segmentation ◽  
Em Method ◽  
Brain Mr Images ◽  
The Brain

We propose a reformative Expectation-Maximization algorithm for brain MRI segmentation. The method extends the traditional EM method to a power transformed version. To test the algorithm we compare it with the method used in SPM software. The test results show that the method performs well in brain MR images segmentation, the brain MR images can be segmented into distinct tissue types.

Normal cerebral ventricular volume growth in childhood

2020 ◽  
Vol 26 (5) ◽  
pp. 517-524
Author(s):  
Noah S. Cutler ◽  
Sudharsan Srinivasan ◽  
Bryan L. Aaron ◽  
Sharath Kumar Anand ◽  
Michael S. Kang ◽  
...  
Keyword(s):  
Brain Mri ◽  
Volume Growth ◽  
Ventricular Volume ◽  
Growth Patterns ◽  
Diagnostic Process ◽  
Growth Charts ◽  
Normal Brain ◽  
Mr Images ◽  
Ventricular Volumes ◽  
Brain Mr Images

OBJECTIVENormal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.METHODSThe authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.RESULTSVentricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0–1 year, 6-month intervals for ages 1–3 years, and 12-month intervals for ages 3–18 years. Additional percentile values were calculated for boys only and girls only.CONCLUSIONSThe authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.

Cerebral Contour Extraction with Particle Method in Neonatal MR Images

2011 ◽  
Vol 15 (3) ◽  
pp. 362-369
Author(s):  
Syoji Kobashi ◽  
◽  
Daisuke Yokomichi ◽  
Yuki Wakata ◽  
Kumiko Ando ◽  
...  
Keyword(s):  
Ground Truth ◽  
Particle Method ◽  
Magnetic Resonance Images ◽  
Mean Square ◽  
Contour Extraction ◽  
Mr Images ◽  
Basic Work ◽  
Brain Mr Images ◽  
Method Of Extraction ◽  
The Brain

Cerebral surface extraction from neonatal MR images is the basic work of quantifying the deformation of the cerebrum. Although there are many conventional methods of segmenting the cerebral region, only the rough area is given by counting the number of surface voxels in the segmented region. This article proposes a new method of extraction that is based on the particle method. The method introduces three kinds of particles that correspond to cerebrospinal fluid, gray matter, and white matter; it converts the brain MR images into the set of particles. The proposed method was applied to neonatal magnetic resonance images, and the experimental results showed that the cerebral contour was extracted with a root-mean-square-error of 0.51 mm compared with the ground truth contour given by a physician.

scholarly journals A Review on Some Classification Methods of Brain Tumor MRI Datasets

2020 ◽  
pp. 421-424
Author(s):  
Nirmal Mungale ◽  
Snehal Kene ◽  
Amol Chaudhary
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Mr Images ◽  
Classification Techniques ◽  
Abnormal Growth ◽  
Life Threatening ◽  
Magnetic Resonance Imaging Mri ◽  
Recent Classification ◽  
The Brain

Brain tumor is a life-threatening disease. Brain tumor is formed by the abnormal growth of cells inside and around the brain. Identification of the size and type of tumor is necessary for deciding the course of treatment of the patient. Magnetic Resonance Imaging (MRI) is one of the methods for detection of tumor in the brain. The classification of MR Images is a difficult task due to variety and complexity of brain tumors. Various classification techniques have been identified for brain MRI tumor images. This paper reviews some of these recent classification techniques.

scholarly journals Classification of Brain Tumors Using Hybridized Convolutional Neural Network in Brain MRI images

2022 ◽  
Vol 16 ◽  
pp. 561-570
Author(s):  
V Shwetha ◽  
C. H. Renu Madhavi ◽  
Kumar M. Nagendra
Keyword(s):  
Brain Mri ◽  
Morphological Operations ◽  
Hybrid Technique ◽  
Body Images ◽  
Anisotropic Diffusion Filter ◽  
Medical Disorders ◽  
Improved Performance ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Mri Image

In this research article, we have proposed a novel technique to operate on the Magnetic Resonance Imaging (MRI) data images which can be classified as image classification, segmentation and image denoising. With the efficient utilization of MRI images the medical experts are able to identify the medical disorders such as tumors which are correspondent to the brain. The prime agenda of the study is to organize brain into healthy and brain with tumor in brain with the test MRI data as considered. The MRI based technique is an methodology to study brain tumor based information for the better detailing of the internal body images when compared to other technique such as Computed Tomography (CT).Initially the MRI image is denoised using Anisotropic diffusion filter, then MRI image is segmented using Morphological operations, to classify the images for the disorder CNN based hybrid technique is incorporated, which is associated with five different set of layers with the pairing of pooling and convolution layers for the comparatively improved performance than other existing technique. The considered data base for the designed model is a publicly available and tested KAGGLE database for the brain MRI images which has resulted in the accuracy of 88.1%.

scholarly journals 3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

2021 ◽  
Author(s):  
Zilong Zeng ◽  
Tengda Zhao ◽  
Lianglong Sun ◽  
Yihe Zhang ◽  
Mingrui Xia ◽  
...  
Keyword(s):  
Brain Mri ◽  
Ground Truth ◽  
Validation Dataset ◽  
Grand Challenge ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Convolutional Network ◽  
Infant Brain ◽  
Brain Mr Images ◽  
Segmentation Models

Precise segmentation of infant brain MR images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation model for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in limited samples of baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infant. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the largest improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

scholarly journals Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems and Support Vector Machines

2021 ◽  
Vol 15 (2) ◽  
pp. 97-110
Author(s):  
Amina Merzoug ◽  
Nacéra Benamrane ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Multiple Sclerosis ◽  
Support Vector Machines ◽  
Brain Mri ◽  
Artificial Immune Systems ◽  
Support Vector ◽  
Mr Images ◽  
Artificial Immune ◽  
Immune Systems ◽  
Vector Machines ◽  
Brain Mr Images

This paper presents a segmentation method to detect multiple sclerosis (MS) lesions in brain MRI based on the artificial immune systems (AIS) and a support vector machines (SVM). In the first step, AIS is used to segment the three main brain tissues white matter, gray matter, and cerebrospinal fluid. Then the features were extracted and SVM is applied to detect the multiple sclerosis lesions based on SMO training algorithm. The experiments conducted on 3D brain MR images produce satisfying results.

A Modified Fuzzy C-Means Algorithm Brain MR Images Segmentation with Bias Field Compensation

2013 ◽  
Vol 756-759 ◽  
pp. 1349-1355 ◽  
Author(s):  
Xiao Li Liu ◽  
Yu Ting Guo ◽  
Jun Kong ◽  
Jian Zhong Wang
Keyword(s):  
Brain Mri ◽  
Bias Field ◽  
Local Information ◽  
Mr Images ◽  
Fcm Algorithm ◽  
Different Types ◽  
Field Estimation ◽  
Non Local ◽  
Brain Mr Images ◽  
Fuzzy C Means Algorithm

Segmentation of brain magnetic resonance (MR) images is always required as a preprocessing stage in many brain analysis tasks. Nevertheless, the bias field (BF, also called intensity in-homogeneities) and noise in the MRI images always make the accurate segmentation difficult. In this paper, we present a modified FCM algorithm for bias field estimation and segmentation of brain MRI. Our method is formulated by modifying the objective function of the standard FCM algorithm. It aims to compensate for bias field and incorporate both the local and non-local information into the distance function to restrain the noise of the image. We have conducted extensive experimental and have compared our method with different types of FCM extension methods using simulated MRI images. The results show that our proposed method can deal with the bias field and noise effectively and outperforms other methods.

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