New Evidences on Automatic Tumor Segmentation in Magnetic Resonance Brain Images

2009 ◽  
pp. 1190-1193 ◽  
Author(s):  
L. L. Caldeira ◽  
P. Almeida ◽  
J. Seabra
Keyword(s):  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Brain Images

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.

scholarly journals Brain Tumor Detection from MRI using Adaptive Thresholding and Histogram based Techniques

2020 ◽  
Vol 21 (1) ◽  
pp. 3-10
Author(s):  
E Murali ◽  
K Meena
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Stage ◽  
Tumor Location ◽  
Magnetic Resonance Images ◽  
Ensemble Classification ◽  
Adaptive Thresholding ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images

This paper depicts a computerized framework that can distinguish brain tumor and investigate the diverse highlights of the tumor. Brain tumor segmentation means to isolated the unique tumor tissues, for example, active cells, edema and necrotic center from ordinary mind tissues of WM, GM, and CSF. However, manual segmentation in magnetic resonance data is a timeconsuming task. We present a method of automatic tumor segmentation in magnetic resonance images which consists of several steps. The recommended framework is helped by image processing based technique that gives improved precision rate of the cerebrum tumor location along with the computation of tumor measure. In this paper, the location of brain tumor from MRI isrecognized utilizing adaptive thresholding with a level set and a morphological procedure with histogram. Automatic brain tumor stage is performed by using ensemble classification. Such phase classifies brain images into tumor and non-tumors using Feed Forwarded Artificial neural network based classifier. For test investigation, continuous MRI images gathered from 200 people are utilized. The rate of fruitful discovery through the proposed procedure is 97.32 percentage accurate.

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.

scholarly journals Multimodal Magnetic Resonance Image Brain Tumor Segmentation Based on ACU-Net Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14608-14618
Author(s):  
Ling Tan ◽  
Wenjie Ma ◽  
Jingming Xia ◽  
Sajib Sarker
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm

2017 ◽  
Vol 79 ◽  
pp. 164-180 ◽  
Author(s):  
Diego Oliva ◽  
Salvador Hinojosa ◽  
Erik Cuevas ◽  
Gonzalo Pajares ◽  
Omar Avalos ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Search Algorithm ◽  
Cross Entropy ◽  
Brain Images

scholarly journals Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning

2021 ◽  
Vol 19 ◽  
pp. 39-44
Author(s):  
Roque Rodríguez Outeiral ◽  
Paula Bos ◽  
Abrahim Al-Mamgani ◽  
Bas Jasperse ◽  
Rita Simões ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Primary Tumor ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Resonance Imaging

Brain abnormality detection using template matching

2018 ◽  
Vol 14 (4) ◽  
Author(s):  
G.B. Praveen ◽  
Anita Agrawal ◽  
Shrey Pareek ◽  
Amalin Prince
Keyword(s):  
Magnetic Resonance ◽  
Template Matching ◽  
Imaging Modality ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Processing Unit ◽  
Brain Disorders ◽  
Brain Tumor Segmentation ◽  
Central Processing ◽  
Wide Range

Abstract Magnetic resonance imaging (MRI) is a widely used imaging modality to evaluate brain disorders. MRI generates huge volumes of data, which consist of a sequence of scans taken at different instances of time. As the presence of brain disorders has to be evaluated on all magnetic resonance (MR) sequences, manual brain disorder detection becomes a tedious process and is prone to inter- and intra-rater errors. A technique for detecting abnormalities in brain MRI using template matching is proposed. Bias filed correction is performed on volumetric scans using N4ITK filter, followed by volumetric registration. Normalized cross-correlation template matching is used for image registration taking into account, the rotation and scaling operations. A template of abnormality is selected which is then matched in the volumetric scans, if found, the corresponding image is retrieved. Post-processing of the retrieved images is performed by the thresholding operation; the coordinates and area of the abnormality are reported. The experiments are carried out on the glioma dataset obtained from Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). Glioma dataset consisted of MR scans of 30 real glioma patients and 50 simulated glioma patients. NVIDIA Compute Unified Device Architecture framework is employed in this paper, and it is found that the detection speed using graphics processing unit is almost four times faster than using only central processing unit. The average Dice and Jaccard coefficients for a wide range of trials are found to be 0.91 and 0.83, respectively.

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