scholarly journals Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

2013 ◽  
Author(s):  
Qaiser Mahmood ◽  
Mohammad Alipoor ◽  
Artur Chodorowski ◽  
Andrew Mehnert1 ◽  
Mikael Persson
Keyword(s):  
Mean Shift ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Brain Segmentation ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
The Brain ◽  
Mr Brain ◽  
Dice Index

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the adaptive mean shift wherein the adaptive bandwidth of the kernel for each feature point is estimated using our proposed Bayesian approach [1]. BAMS is designed to segment the brain into three tissues; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The performance of the algorithm is evaluated relative to the manual segmentation (ground truth). The results of our proposed algorithm show the average Dice index 0.8377±0.036 for the WM, 0.7637±0.038 for the GM and 0.6835 ±0.023 for the CSF.

scholarly journals A fully automatic unsupervised segmentation framework for the brain tissues in MR images

2014 ◽  
Author(s):  
Qaiser Mahmood ◽  
Artur Chodorowski ◽  
Babak Ehteshami Bejnordi ◽  
Mikael Persson
Keyword(s):  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
Segmentation Framework ◽  
The Brain ◽  
Brain Tissues

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 Brain Tumor Detection and Segmentation using Histogram and Optimization Algorithm

2019 ◽  
Vol 8 (10S) ◽  
pp. 125-129
Keyword(s):  
Brain Tumor ◽  
Optimization Algorithm ◽  
Ground Truth ◽  
Tumor Detection ◽  
High Accuracy ◽  
Mathematical Foundation ◽  
Mr Images ◽  
Brain Image ◽  
Segmentation Technique ◽  
The Brain

In this research, an automated and customized neoplasm segmentation methodology is given and valid against ground truth applying simulated T1-weighted resonance pictures in twenty five subjects. a replacement intensity-based segmentation technique known as bar graph primarily based gravitational optimization algorithm is developed to segment the brain image into discriminative sections (segments) with high accuracy. whereas the mathematical foundation of this rule is given in details, the appliance of the projected rule within the segmentation of single T1-weighted pictures (T1-w) modality of healthy and lesion MR images is additionally given. The results show that the neoplasm lesion is divided from the detected lesion slice with eighty nine.6% accuracy..

scholarly journals Fully automatic brain segmentation using model-guided level sets and skeleton-based models

2013 ◽  
Author(s):  
Chunliang Wang ◽  
Chun Wang ◽  
Örjan Smedby
Keyword(s):  
Level Set ◽  
Gray Matter ◽  
Brain Segmentation ◽  
Segmentation Method ◽  
Implicit Model ◽  
Segmentation Methods ◽  
Cortical Gray Matter ◽  
Fully Automatic ◽  
Level Set Framework ◽  
The Brain

A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

An Efficient Computational Approach for the Detection of MR Brain Tissues in the Presence of Noise and Intensity Inhomogeneity

2017 ◽  
Vol 33 ◽  
pp. 65-79 ◽  
Author(s):  
G. Sandhya ◽  
Kande Giri Babu ◽  
T. Satya Savithri
Keyword(s):  
Energy Minimization ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Brain Images ◽  
Brain Image ◽  
Anisotropic Diffusion Filter ◽  
The Brain ◽  
Mr Brain ◽  
Brain Tissues

The automatic detection of brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) from the MR images of the brain using segmentation is of immense interest for the early detection and diagnosing various brain-related diseases. MR imaging technology is one of the best and most reliable ways of studying the brain. Segmentation of MR images is a challenging task due to various artifacts such as noise, intensity inhomogeneity, partial volume effects and elemental texture of the image. This work proposes a region based, efficient and modern energy minimization process called as Anisotropic Multiplicative Intrinsic Component Optimization (AMICO) for the brain image segmentation in the presence of noise and intensity inhomogeneity to separate different tissues. This algorithm uses an efficient Anisotropic diffusion filter to decrease the noise. The denoised image gets segmented after the correction of intensity inhomogeneity by the MICO algorithm. The algorithm decomposes the MR brain image as two multiplicative intrinsic components, called as the component of the true image which represents the physical properties of the brain tissue and the component of bias field that is related to intensity inhomogeneity. By optimizing the values of these two components using an efficient energy minimization technique, correction of intensity inhomogeneity and segmentation of the tissues can be achieved simultaneously. Performance evaluation and the comparison with some existing methods have validated the remarkable performance of AMICO in terms of efficiency of segmentation of brain images in the presence of noise and intensity inhomogeneity.

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 learning-based automatic segmentation algorithm of brain MR images based on 7T

2020 ◽  
Vol 16 ◽  
Author(s):  
Minghui Deng ◽  
Zhenhao Jin ◽  
Ran Yu ◽  
Qingshuang Zeng
Keyword(s):  
Automatic Segmentation ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Image Intensity ◽  
Automated Method ◽  
Brain Mr Images ◽  
Intensity Contrast

Background: The learning-based algorithms provide an ability to automatically estimate and refine GM, WM and CSF. The ground truth manually achieved from the 3T MR image may not be accurate and reliable with poor image intensity contrast. It will seriously influence the classification performance because the supervised learning-based algorithms extremely rely on the ground truth. Recently, the 7T MR images brings about the excellent image intensity contrast, while Structured Random Forest (SRF) performs the pixel-level classification and achieves structural and contextual information in images. Materials and Methods: In this paper, a automatic segmentation algorithm is proposed based on ground truth achieved by the corresponding 7T subjects for segmenting the 3T&1.5T brain tissues using SRF classifiers. Through taking advantage of the 7T brain MR images, we can achieve the highly accuracy and reliable ground truth and then implement the training of SRF classifiers. Our proposed algorithm effectively integrates the T1-weighed images along with the probability maps to train the SRF classifiers for brain tissue segmentation. Results: Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 95.14%±0.9%, 90.17%±1.83%, and 81.96%± 4.32% for WM, GM, and CSF. With the experiment results, the proposed algorithm can achieve better performances than other automatic segmentation methods. Further experiments are performed on the 200 3T&1.5T brain MR images of ADNI dataset and our proposed method shows promised performances. Conclusions: The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.

Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

2020 ◽  
Vol 77 (4) ◽  
pp. 1609-1622
Author(s):  
Franziska Mathies ◽  
Catharina Lange ◽  
Anja Mäurer ◽  
Ivayla Apostolova ◽  
Susanne Klutmann ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Fdg Pet ◽  
False Negative ◽  
Ground Truth ◽  
Etiological Diagnosis ◽  
Geriatric Unit ◽  
Positron Emission ◽  
The Brain ◽  
Hospitalized Geriatric Patients

Background: Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. Objective: This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. Methods: The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. Results: FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). Conclusion: Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

Combined MR brain segmentation

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Rafał Kartaszyński ◽  
Paweł Mikołajczak
Keyword(s):  
Brain Segmentation ◽  
Mr Brain

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.

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