scholarly journals Enhanced N-Cut and Watershed based Model for Brain MRI Segmentation

2020 ◽  
Vol 9 (5) ◽  
pp. 346-351
Keyword(s):  
Medical Image ◽  
Clinical Investigation ◽  
Brain Mri ◽  
Hybrid Approach ◽  
Mr Images ◽  
Mri Segmentation ◽  
Normalized Cut ◽  
Mr Image ◽  
Watershed Transform ◽  
Cut Method

Segmentation of an image is most important and essential task in medical image processing, specifically while analyzing magnetic resonance (MR) image of brain clinically. during the clinical investigation of brain MRI images. Lot of research has been carried out for MRI segmentation but still it is challenging task. Hybrid approach which uses enhanced normalized cut and watershed transform to segment brain MRI images is developed in this paper. Watershed transform is used for the initial partitioning of the MRI, which creates primitive regions. In the next stage these primitive regions resembled for graph depiction and then the normalized cut method is used for segmenting an image. Variety of simulated and actual MR images are being segmented by using proposed algorithm to test its efficiency, in addition to it segmentation results are also compared with the other available techniques of brain MRI segmentation.

Research on Improved Methods of Watershed Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 583-586
Author(s):  
De Yong Wang ◽  
Ji Fan
Keyword(s):  
Watershed Algorithm ◽  
Normalized Cut ◽  
Watershed Transform ◽  
Cut Method ◽  
Weight Value ◽  
Area And Perimeter ◽  
The Common ◽  
Main Formula ◽  
Improved Methods ◽  
Image Segmentation Algorithm

In this paper an improved image segmentation algorithm based on watershed transform is presented. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameter αand β. Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

scholarly journals Accurate Automatic Glioma Segmentation in Brain MRI images Based on CapsNet

2021 ◽  
Author(s):  
Maryam Jalili Aziz ◽  
Mohammad Amin Amiri Tehrani Zadeh ◽  
Parastoo Farnia ◽  
Meysam Alimohammadi ◽  
Bahador Makki Abadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Mri ◽  
Spatial Relationship ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
Mr Image ◽  
Network Training ◽  
First Time ◽  
Entire Dataset ◽  
Different Parts

Glioma is a highly invasive type of brain tumor that appears in different parts of brain with various sizes, shapes, and blurred borders. Therefore, it is a challenging task to identify the exact boundaries of the tumor in an MR image. In recent years, deep learning based CNNs methods have gained popularity in the field of image processing and have been utilized for accurate image segmentation in medical applications. However, the inherent limitations of CNNs warrants the need for tens of thousands of images in the training phase, while the collection and annotation of such large number of images poses a great challenge. Here, for the first time, we have optimized a network based on the capsule neural network called SegCaps, to achieve accurate glioma segmentation in MR images. We have compared our results with a similar experiment conducted using commonly utilized U-Net. Both experiments are performed on the BraTS2020 challenging dataset. For U-Net, network training is performed on the entire dataset, while a subset containing only 20% of the whole dataset is used for the SegCaps. To evaluate the results of our proposed method, Dice Similarity Coefficient (DSC) is used. SegCaps and U-Net reached DSC of 87.96% and 85.56% on glioma tumor core segmentation, respectively. The SegCaps uses convolutional layers as the basic components and has the intrinsic capability to generalize novel viewpoints. The network learns the spatial relationship between features using dynamic routing of capsules. These capabilities of the capsule neural network have led to the 3% improvement of results in glioma segmentation with fewer data while it contains 95.4% less parameters than U-Net.

A New Normalized-Cut Image Segmentation Algorithm Based on Watershed Transform

2012 ◽  
Vol 235 ◽  
pp. 45-48 ◽  
Author(s):  
Hai Tao Liu ◽  
Yin Long Wang ◽  
Hui Fen Yao
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Normalized Cut ◽  
Watershed Transform ◽  
Cut Method ◽  
Weight Value ◽  
Area And Perimeter ◽  
The Common ◽  
Main Formula ◽  
Image Segmentation Algorithm

In this paper an improved image segmentation algorithm based on watershed transform is presented. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameterαandβ. Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

An Image Segmentation Algorithm Based on Watershed Transform

2015 ◽  
Vol 740 ◽  
pp. 608-611
Author(s):  
Yin Long Wang ◽  
Qian Jin Li ◽  
Zhi Xiang Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Normalized Cut ◽  
Watershed Transform ◽  
Cut Method ◽  
Weight Value ◽  
Area And Perimeter ◽  
The Common ◽  
Main Formula ◽  
Image Segmentation Algorithm

An improved image segmentation algorithm based on watershed transform is presented In this paper. Firstly the normalized cut method and watershed transform are explained and analyzed. Secondly the idea of the improved algorithm and the main formula are explained. We consider the area and perimeter when we merge adjacent regions. We define a new weight value and discuss the value of the parameter α and β. Finally the experiment result is presented. The new algorithm reduces the nodes and the computational demand of the common normalized cut technique.

VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

2021 ◽  
Vol 12 (1) ◽  
pp. 94-110
Author(s):  
Mariem Miledi ◽  
Souhail Dhouib
Keyword(s):  
Medical Image ◽  
Variable Neighborhood Search ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Computational Time ◽  
Neighborhood Search ◽  
Mri Segmentation ◽  
Number Of Classes ◽  
Otsu’S Function

Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

scholarly journals Deep CNN based MR image denoising for tumor segmentation using watershed transform

2018 ◽  
Vol 7 (2.3) ◽  
pp. 37 ◽  
Author(s):  
Ghazanfar Latif ◽  
D N.F. Awang Iskandar ◽  
Jaafar Alghazo ◽  
Mohsin Butt ◽  
Adil H. Khan
Keyword(s):  
Image Denoising ◽  
Clinical Investigation ◽  
Imaging Techniques ◽  
Tumor Segmentation ◽  
Chemical Information ◽  
Mr Image ◽  
Denoising Method ◽  
Watershed Transform ◽  
Deep Cnn ◽  
Magnetic Resonance Imaging Mri

Magnetic Resonance Imaging (MRI) is considered one of the most effective imaging techniques used in the medical field for both clinical investigation and diagnosis. This is due to the fact that MRI provides many critical features of the tissue including both physiological and chemical information. Rician noise affects MR images during acquisition thereby reducing the quality of the image and complicating the accurate diagnosis. In this paper, we propose a novel technique for MR image denoising using Deep Convolutional Neural Network (Deep CNN) and anisotropic diffusion (AD) which we will refer to as Deep CNN-AD. Watershed transform is then used to segment the tumorous portion of the denoised image.   The proposed method is tested on the BraTS MRI datasets. The proposed denoising method produced better results compared to previous methods. As denoising process affect the segmentation process therefore better denoised images by proposed technique produced more accurate segmentation with an average Specificity of 99.85% and dice coefficient of 90.46% thus indicating better performance of proposed technique.

THE SEGMENTATION OF BRAIN MR IMAGES USING REFORMATIVE EXPECTATION-MAXIMIZATION ALGORITHM

2010 ◽  
Vol 10 (02) ◽  
pp. 289-297
Author(s):  
JIE WU ◽  
JIABI CHEN ◽  
XUELONG ZHANG ◽  
JINGHAI CHEN
Keyword(s):  
Expectation Maximization ◽  
Brain Mri ◽  
Expectation Maximization Algorithm ◽  
Test Results ◽  
Mr Images ◽  
Mri Segmentation ◽  
Em Method ◽  
Brain Mr Images ◽  
The Brain

We propose a reformative Expectation-Maximization algorithm for brain MRI segmentation. The method extends the traditional EM method to a power transformed version. To test the algorithm we compare it with the method used in SPM software. The test results show that the method performs well in brain MR images segmentation, the brain MR images can be segmented into distinct tissue types.

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.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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