scholarly journals Image Segmentation of Brain MR Images Using Otsu’s based Hybrid WCMFO Algorithm

2020 ◽  
Vol 64 (2) ◽  
pp. 681-700 ◽  
Author(s):  
A. Renugambal ◽  
K. Selva Bhuvaneswari
Keyword(s):  
Image Segmentation ◽  
Mr Images ◽  
Brain Mr Images

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 487-496
Author(s):  
Li Liu ◽  
Chi Hua ◽  
Zixuan Cheng ◽  
Yunfeng Ji
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Medical Imaging ◽  
Automatic Segmentation ◽  
Learning Ability ◽  
Mr Images ◽  
Feature Maps ◽  
Intelligent Diagnosis ◽  
Brain Mr Images ◽  
Self Organizing

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

An Approach with Neural Network to Detection of MRI Anatomy

1994 ◽  
Vol 7 (1) ◽  
pp. 47-52
Author(s):  
R. De Blasi ◽  
A. Blonda ◽  
G. Pasquariello ◽  
D. Milella ◽  
F. Dicuonzo ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Back Propagation ◽  
Learning Rule ◽  
Object Classification ◽  
Modular System ◽  
Training Phase ◽  
Self Organizing Map ◽  
Mr Images ◽  
Medical Expert ◽  
Brain Mr Images

In this paper an artificial modular system applied to object classification in brain MR images is presented. It consists of two modules based on neural architectures joined in sequence to perform first an image segmentation and then an object classification. For these two steps a Self Organizing Map and a Multilayer Perceptron trained with the Back-Propagation learning rule have been used. The objective of the system is the automatic recognition of the anatomic structures in MR images of the cerebral section passing through the orbits and the visual pathways. To reach this goal we have submitted the two networks to a training phase realized by an unsupervised process for the image segmentation and by a supervised process for regions labelling. This last step has been based on topographic relations supplied by a medical expert. The system has been useful to discriminate 20 different classes of anatomic objects over the considered section. Preliminary results are presented.

scholarly journals Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 71-85
Author(s):  
Sumit Kumar ◽  
Garima Vig ◽  
Sapna Varshney ◽  
Priti Bansal
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Hybrid Algorithm ◽  
Tumor Detection ◽  
Metaheuristic Algorithms ◽  
Mr Images ◽  
Medical Practitioners ◽  
Whale Optimization ◽  
Brain Mr Images ◽  
Image Pixels

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.

A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images

2020 ◽  
Vol 110 ◽  
pp. 101980
Author(s):  
Mohamed T. Bennai ◽  
Zahia Guessoum ◽  
Smaine Mazouzi ◽  
Stéphane Cormier ◽  
Mohamed Mezghiche
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Multi Agent ◽  
Brain Mr Images

scholarly journals A Hybrid Technique for Medical Image Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Alamgir Nyma ◽  
Myeongsu Kang ◽  
Yung-Keun Kwon ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Impulsive Noise ◽  
Median Filter ◽  
Input Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Mr Images ◽  
Data Set ◽  
Brain Mr Images

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

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.

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