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

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.

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

Journal of Biomimetics Biomaterials and Biomedical Engineering ◽

10.4028/www.scientific.net/jbbbe.33.65 ◽

2017 ◽

Vol 33 ◽

pp. 65-79 ◽

Cited By ~ 2

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.

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Characterization of Ischemic Stroke in CT Images using Image Processing

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i9.405 ◽

2017 ◽

Vol 7 (9) ◽

pp. 18

Author(s):

Amal Alzain ◽

Suhaib Alameen ◽

Rani Elmaki ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Ischemic Stroke ◽

White Matter ◽

Classification Accuracy ◽

Ct Images ◽

Gray Level ◽

Level Variation ◽

Interactive Data ◽

The Brain ◽

Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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

Journal of Digital Imaging ◽

10.1007/s10278-020-00400-7 ◽

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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Fully Automatic Cardiac Segmentation And Quantification For Pulmonary Hypertension Analysis Using Mice Cine Mr Images

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi48211.2021.9433855 ◽

2021 ◽

Author(s):

Blanca Zufiria ◽

Maialen Stephens ◽

Maria Jesus Sanchez ◽

Jesus Ruiz-Cabello ◽

Karen Lopez-Linares ◽

...

Keyword(s):

Pulmonary Hypertension ◽

Mr Images ◽

Cardiac Segmentation ◽

Cine Mr ◽

Fully Automatic

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Magnetic Resonace–Based Attenuation Correction for Micro–Single-Photon Emission Computed Tomography

Molecular Imaging ◽

10.2310/7290.2011.00036 ◽

2012 ◽

Vol 11 (2) ◽

pp. 7290.2011.00036 ◽

Cited By ~ 4

Author(s):

Vincent Keereman ◽

Yves Fierens ◽

Christian Vanhove ◽

Tony Lahoutte ◽

Stefaan Vandenberghe

Keyword(s):

Attenuation Correction ◽

Single Photon ◽

Photon Emission ◽

The Body ◽

Emission Computed Tomography ◽

Micro Ct ◽

Mr Images ◽

Brain Scans ◽

The Brain ◽

Photon Emission Computed Tomography

Attenuation correction is necessary for quantification in micro–single-photon emission computed tomography (micro-SPECT). In general, this is done based on micro–computed tomographic (micro-CT) images. Derivation of the attenuation map from magnetic resonance (MR) images is difficult because bone and lung are invisible in conventional MR images and hence indistinguishable from air. An ultrashort echo time (UTE) sequence yields signal in bone and lungs. Micro-SPECT, micro-CT, and MR images of 18 rats were acquired. Different tracers were used: hexamethylpropyleneamine oxime (brain), dimercaptosuccinic acid (kidney), colloids (liver and spleen), and macroaggregated albumin (lung). The micro-SPECT images were reconstructed without attenuation correction, with micro-CT-based attenuation maps, and with three MR-based attenuation maps: uniform, non-UTE-MR based (air, soft tissue), and UTE-MR based (air, lung, soft tissue, bone). The average difference with the micro-CT-based reconstruction was calculated. The UTE-MR-based attenuation correction performed best, with average errors ≤ 8% in the brain scans and ≤ 3% in the body scans. It yields nonsignificant differences for the body scans. The uniform map yields errors of ≤ 6% in the body scans. No attenuation correction yields errors ≥ 15% in the brain scans and ≥ 25% in the body scans. Attenuation correction should always be performed for quantification. The feasibility of MR-based attenuation correction was shown. When accurate quantification is necessary, a UTE-MR-based attenuation correction should be used.

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A fingerprint of amyloid plaques in a bitransgenic animal model of Alzheimer's disease obtained by statistical unmixing analysis of hyperspectral Raman data

The Analyst ◽

10.1039/c9an01631g ◽

2019 ◽

Vol 144 (23) ◽

pp. 7049-7056 ◽

Cited By ~ 4

Author(s):

Emerson A. Fonseca ◽

Lucas Lafetá ◽

Renan Cunha ◽

Hudson Miranda ◽

João Campos ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Animal Model ◽

Amyloid Plaques ◽

Model Of Alzheimer’S Disease ◽

The Brain ◽

Brain Tissues

We have found different Raman signatures of AB fibrils and in brain tissues from unmixed analysis, providing a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers.

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Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation

Journal of Magnetic Resonance Imaging ◽

10.1002/(sici)1522-2586(200003)11:3<267::aid-jmri5>3.0.co;2-8 ◽

2000 ◽

Vol 11 (3) ◽

pp. 267-278 ◽

Cited By ~ 37

Author(s):

Vincent Barra ◽

Jean-Yves Boire

Keyword(s):

Mr Images ◽

Tissue Segmentation ◽

Wavelet Representation ◽

Possibilistic Clustering ◽

The Brain

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Brain shift: an error factor during implantation of deep brain stimulation electrodes

Journal of Neurosurgery ◽

10.3171/jns-07/11/0989 ◽

2007 ◽

Vol 107 (5) ◽

pp. 989-997 ◽

Cited By ~ 102

Author(s):

Yasushi Miyagi ◽

Fumio Shima ◽

Tomio Sasaki

Keyword(s):

Deep Brain Stimulation ◽

Brain Stimulation ◽

Brain Shift ◽

Mr Images ◽

Implanted Electrodes ◽

Error Factor ◽

Bilateral Surgery ◽

Second Procedure ◽

The Brain ◽

Deep Brain

Object The goal of this study was to focus on the tendency of brain shift during stereotactic neurosurgery and the shift's impact on the unilateral and bilateral implantation of electrodes for deep brain stimulation (DBS). Methods Eight unilateral and 10 bilateral DBS electrodes at 10 nuclei ventrales intermedii and 18 subthalamic nuclei were implanted in patients at Kaizuka Hospital with the aid of magnetic resonance (MR) imaging–guided and microelectrode-guided methods. Brain shift was assessed as changes in the 3D coordinates of the anterior and posterior commissures (AC and PC) with MR images before and immediately after the implantation surgery. The positions of the implanted electrodes, based on the midcommissural point and AC–PC line, were measured both on x-ray films (virtual position) during surgery and the postoperative MR images (actual position) obtained on the 7th day postoperatively. Results Contralateral and posterior shift of the AC and PC were the characteristics of unilateral and bilateral procedures, respectively. The authors suggest the following. 1) The first unilateral procedure elicits a unilateral air invasion, resulting in a contralateral brain shift. 2) During the second procedure in the bilateral surgery, the contralateral shift is reset to the midline and, at the same time, the anteroposterior support by the contralateral hemisphere against gravity is lost due to a bilateral air invasion, resulting in a significant posterior (caudal) shift. Conclusions To note the tendency of the brain to shift is very important for accurate implantation of a DBS electrode or high frequency thermocoagulation, as well as for the prediction of therapeutic and adverse effects of stereotactic surgery.

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