scholarly journals A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Inyoung Bae ◽  
Jong-Hee Chae ◽  
Yeji Han
Keyword(s):  
Magnetic Resonance ◽  
3D Imaging ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Brain Extraction ◽  
Imaging Sequence ◽  
Extraction Algorithm ◽  
Infant Brain ◽  
2D And 3D ◽  
The Brain

AbstractIt is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.

scholarly journals A novel approach for pre-surgical evaluation of facial morphometry in children using magnetic resonance imaging scans of the brain: A feasibility analysis

2020 ◽  
pp. 55-68
Author(s):  
Iroshani Kodikara ◽  
Dhanusha Gamege ◽  
Ganananda Nanayakkara ◽  
Isurani Ilaperuma
Keyword(s):  
Magnetic Resonance ◽  
Three Dimensional ◽  
Facial Dysmorphism ◽  
Brain Images ◽  
Imaging Protocol ◽  
3D Images ◽  
Mri Brain ◽  
Surgical Evaluation ◽  
2D And 3D ◽  
The Brain

Pre-surgical evaluation of facial morphometry is frequently warranted for children with facial dysmorphism. Though many methods utilized previously for such purposes, data is scarce on using magnetic resonance (MRI) brain images for such purposes. The purpose of this study was to appraise the feasibility of utilizing MRI brain scans done in epilepsy imaging protocol to assess facial morphometry. Measurements of the face; orbit, mouth, and nose of children aged 1 to 7 years were obtained using T1 sagittal, T2 axial and three dimensional (3D) MRI images of the brain (n=20). Ability to obtain facial measurements, inter and intra-observer variability calculated. The mean age of the studied children was 4±2 years, of which 40% (n=8) were boys, and 60% (n=12) were girls. Obtaining facial measurements were reliable with high intra-observer (α=0.757 to 0.999) and inter-observer agreements (α=0.823 to 0.997). The landmarks of the cranium, upper face, and upper nose could be identified (100%) in both two dimensional (2D) and 3D images when such landmarks were contained in the imaging field of view (FOV). Landmarks of lower nose, (subalar width = 0%) or mouth (0%) were not contained in the FOV of 2D images, but contained in 3D images (100%). Both 2D and 3D images did not allow assessment of lower face or the mandible as such landmarks were not contained in the FOV.We conclude thatBrain MRIs performed to evaluate cerebral pathology can be executed to assess facial measurements, provided the FOV of the scan is adjusted to include all significant landmarks.

scholarly journals Minipig Model of Huntington´s Disease: 1H Magnetic Resonance Spectroscopy of the Brain

2016 ◽  
pp. 155-163 ◽  
Author(s):  
M. JOZEFOVICOVA ◽  
V. HERYNEK ◽  
F. JIRU ◽  
M. DEZORTOVA ◽  
J. JUHASOVA ◽  
...  
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Neurodegenerative Disorder ◽  
Resonance Spectroscopy ◽  
1H Magnetic Resonance Spectroscopy ◽  
Imaging Sequence ◽  
Single Voxel ◽  
Metabolite Concentrations ◽  
Cognitive Disturbances ◽  
The Brain

Huntington’s disease (HD) is an inherited autosomal neurodegenerative disorder affecting predominantly the brain, characterized by motor dysfunctions, behavioral and cognitive disturbances. The aim of this study was to determine changes in the brain of transgenic minipigs before HD onset using 1H magnetic resonance (MR) spectroscopy. Measurements were performed on a 3 T MR scanner using a single voxel spectroscopy sequence for spectra acquisition in the white matter and chemical shift imaging sequence for measurement in the striatum, hippocampus and thalamus. A decrease of (phospho)creatine (tCr) concentration was found only in the thalamus (p=0.002) of transgenic minipigs, nevertheless we found significant changes in metabolite ratios. Increase of the ratio choline compounds (tCho)/tCr was found in all examined areas: striatum (p=0.010), thalamus (p=0.011) as well as hippocampus (p=0.027). The ratio N-acetylaspartate+N-acetylaspartylglutamate (tNAA)/tCr (p=0.043) and glutamate+glutamine (Glx)/tCr (p=0.039) was elevated in the thalamus, the ratio myo-inositol (Ins)/tCr (p=0.048) was significantly increased in the hippocampus. No significant differences were observed in the metabolite concentrations in the white matter, however we found significant increase of ratios tNAA/tCr (p=0.018) and tCho/tCr (p=0.003) ratios in transgenic boars. We suppose that the majority of the observed changes are predominantly related to changes in energy metabolism caused by decrease of tCr.

scholarly journals Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

Possibilistic Intuitionistic Fuzzy c-Means Clustering Algorithm for MRI Brain Image Segmentation

2015 ◽  
Vol 24 (05) ◽  
pp. 1550016 ◽  
Author(s):  
Hanuman Verma ◽  
R. K. Agrawal
Keyword(s):  
Clustering Algorithm ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Mri Brain Images

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov’s intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.

scholarly journals Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

2020 ◽  
Vol 9 (3) ◽  
pp. 1032-1037
Author(s):  
Nur Hanina Izani Muhammad Zaihani ◽  
Rosniza Roslan ◽  
Zaidah Ibrahim ◽  
Khyrina Airin Fariza Abu Samah
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Gadolinium Contrast ◽  
Brain Images ◽  
Mri Brain ◽  
The Brain ◽  
Mri Brain Images

There are numerous studies on brain imaging applications.  The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor.  Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues.  Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor.  However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance.  They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients.  Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations.  The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS).  The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.

Comparison of Multispectral Image Processing Techniques to Brain MR Image Classification

2012 ◽  
Vol 182-183 ◽  
pp. 1998-2002
Author(s):  
Yen Sheng Chen ◽  
Shao Hsien Chen ◽  
Jeih Jang Liou
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Magnetic Resonance Imaging ◽  
Free Water ◽  
Nerve Fibers ◽  
Medical Image Segmentation ◽  
Multispectral Image ◽  
Fuzzy C Means ◽  
The Brain ◽  
Fuzzy C Means Algorithm

Brain Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scans of the brain. Manual segmentation is extremely time-consuming and tedious. Therefore, fully automated MS detection methods are being developed which can classify large amounts of MR data, and do not suffer from inter observer variability. In this paper we use standard fuzzy c-means algorithm (FCM) for multi-spectral images to segment patient MRI data. Geodesic Active Contours of Caselles level set is another method we implement to do the brain image segmentation jobs. And then we implement anther modified Fuzzy C-Means algorithm, where we call Bias-Corrected FCM as BCFCM, for bias field estimation for the same thing. Experimental results show the success of all these intelligent techniques for brain medical image segmentation.

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