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.

scholarly journals Noise Adaptive FCM algorithm for Segmentation of MRI Brain Images

2018 ◽  
Vol 8 (04) ◽  
Author(s):  
Harmanpreet Singh ◽  
Ramandeep Kaur
Keyword(s):  
Spatial Information ◽  
Brain Mri ◽  
Local Information ◽  
Brain Images ◽  
Fcm Algorithm ◽  
Non Local ◽  
Robust To Noise ◽  
Local Spatial Information ◽  
Mri Brain Images ◽  
Crucial Parameter

Image segmentation is an important task in many image processing applications. Fuzzy C means algorithm has been widely used for the segmentation. There are many versions of the traditional FCM algorithm which uses the local spatial information to increase the robustness of this algorithm in presence of noise, but all these algorithms do not successfully segment the images contaminated by heavy noise. In order to solve this problem, non-local spatial information present in the image is utilized. The filtering parameter ‘h’ in the non-local information is a crucial parameter which needs to be appropriately determined, irrespective of using a single constant value of ‘h’; we can determine its value using the standard deviation of noise present in the image. The adaptive non-local information determined is termed as noise adaptive non-local spatial information. This adaptive non-local information is used in the FCM algorithm for the segmentation of MRI images. In this paper Noise adaptive FCM algorithm (NAFCM) using adaptive non-local information is proposed. Therefore the proposed algorithm utilizes adaptive non-local information making it robust in presence of noise as well as preserving the details present in the image. The efficiency of the proposed algorithm is demonstrated by validation studies on synthetic as well as simulated brain MRI images. The results of the proposed algorithm show that the proposed algorithm is robust to noise and as compared to other state of the art algorithms.

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.

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 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.

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