DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.

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A hybrid segmentation approach for brain tumor extraction and detection

2014 International Conference on Multimedia Computing and Systems (ICMCS) ◽

10.1109/icmcs.2014.6911131 ◽

2014 ◽

Cited By ~ 2

Author(s):

Najlae Idrissi ◽

Fatima Ezzahra Ajmi

Keyword(s):

Brain Tumor ◽

Segmentation Approach ◽

Hybrid Segmentation ◽

Tumor Extraction

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Brain Tumor Detection in MR Imaging Using DW-MTM Filter and Region-Growing Segmentation Approach

Advances in Intelligent Systems and Computing - First International Conference on Artificial Intelligence and Cognitive Computing ◽

10.1007/978-981-13-1580-0_53 ◽

2018 ◽

pp. 549-560 ◽

Cited By ~ 1

Author(s):

Bobbillapati Suneetha ◽

A. Jhansi Rani

Keyword(s):

Brain Tumor ◽

Mr Imaging ◽

Region Growing ◽

Tumor Detection ◽

Segmentation Approach ◽

Region Growing Segmentation

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Gaussian Mixture Model for MRI Image Segmentation to Build a Three-Dimensional Image on Brain Tumor Area

MATEMATIKA ◽

10.11113/matematika.v36.n3.1222 ◽

2020 ◽

Vol 36 (3) ◽

pp. 217-234

Author(s):

Anindya Apriliyanti Pravitasari ◽

Nur Iriawan ◽

Siti Azizah Nurul Solichah ◽

Irhamah Irhamah ◽

Kartika Fithriasari ◽

...

Keyword(s):

Brain Tumor ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Three Dimensional ◽

Region Of Interest ◽

Gaussian Mixture ◽

Magnetic Resonance Imaging Mri ◽

Healthy Part ◽

Mri Image ◽

Tumor Area

A brain tumor is one of the deadly diseases that attack the central and nervoussystem. The treatment of brain tumor, need high accuracy and precision. Brain tumordetection through Magnetic Resonance Imaging (MRI) has two-dimensional output withthree perspectives, namely sagittal, coronal, and axial. These different perspectives needto be seen one by one to determine the location and size of the tumor. Tosolve the problem, this study constructs the three-dimensional visualization perspective ofMRI images. The tumor area in MRI image is segmented as a region of interest (ROI) byemploying the Gaussian Mixture Model (GMM) with Expectation-Maximization as theoptimization technique. These couple segmentation methods have revealed significant gainas a clear boundary of the tumor area to separate from the healthy part of the brain andan estimated tumor volume from sagittal, coronal, and axial perspectives. Furthermore,these findings have been successfully visualized in 3D construction of the tumor positionon the left side of the patient’s head with an estimated volume of 749mm3.

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A Review of Brain Tumor Image Segmentation of MR Images Using Deep Learning Methods

10.21467/proceedings.114.19 ◽

2021 ◽

Author(s):

Amishi Vijay ◽

Jasleen Saini ◽

B.S. Saini

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Automatic Segmentation ◽

Learning Algorithms ◽

Region Of Interest ◽

Tumor Segmentation ◽

Dice Similarity Coefficient ◽

Brain Tumor Segmentation ◽

Learning Techniques ◽

Sensitivity Specificity

A significant analysis is routine for Brain Tumor patients and it depends on accurate segmentation of Region of Interest. In automatic segmentation, field deep learning algorithms are attaining interest after they have performed very well in various ImageNet competitions. This review focuses on state-of-the-art Deep Learning Algorithms which are applied to Brain Tumor Segmentation. First, we review the methods of brain tumor segmentation, next the different deep learning algorithms and their performance measures like sensitivity, specificity and Dice similarity Coefficient (DSC) are discussed and Finally, we discuss and summarize the current deep learning techniques and identify future scope and trends.

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A NOVEL APPROACH FOR BRAIN TUMOR DETECTION USING DW-MTM FILTER AND REGION GROWING SEGMENTATION IN MR IMAGING

10.21506/j.ponte.2018.3.10 ◽

2018 ◽

Vol 74 (3) ◽

Author(s):

Suneetha Bobbillapati ◽

A. Jhansi Rani

Keyword(s):

Brain Tumor ◽

Mr Imaging ◽

Region Growing ◽

Tumor Detection ◽

Novel Approach ◽

Region Growing Segmentation

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Cortical microinfarcts in memory clinic patients are associated with reduced cerebral perfusion

Journal of Cerebral Blood Flow & Metabolism ◽

10.1177/0271678x19877403 ◽

2019 ◽

Vol 40 (9) ◽

pp. 1869-1878 ◽

Cited By ~ 5

Author(s):

Doeschka A Ferro ◽

Henri JJM Mutsaerts ◽

Saima Hilal ◽

Hugo J Kuijf ◽

Esben T Petersen ◽

...

Keyword(s):

Arterial Spin Labeling ◽

Cerebral Perfusion ◽

Region Of Interest ◽

Resonance Imaging ◽

Memory Clinic ◽

Anterior Circulation ◽

Cortical Gray Matter ◽

Arterial Transit Time ◽

Magnetic Resonance Imaging Mri ◽

Continuous Arterial Spin Labeling

Cerebral cortical microinfarcts (CMIs) are small ischemic lesions associated with cognitive impairment and dementia. CMIs are frequently observed in cortical watershed areas suggesting that hypoperfusion contributes to their development. We investigated if presence of CMIs was related to a decrease in cerebral perfusion, globally or specifically in cortex surrounding CMIs. In 181 memory clinic patients (mean age 72 ± 9 years, 51% male), CMI presence was rated on 3-T magnetic resonance imaging (MRI). Cerebral perfusion was assessed from cortical gray matter of the anterior circulation using pseudo-continuous arterial spin labeling parameters cerebral blood flow (CBF) (perfusion in mL blood/100 g tissue/min) and spatial coefficient of variation (CoV) (reflecting arterial transit time (ATT)). Patients with CMIs had a 12% lower CBF (beta = −.20) and 22% higher spatial CoV (beta = .20) (both p < .05) without a specific regional pattern on voxel-based CBF analysis. CBF in a 2 cm region-of-interest around the CMIs did not differ from CBF in a reference zone in the contralateral hemisphere. These findings show that CMIs in memory clinic patients are primarily related to global reductions in cerebral perfusion, thus shedding new light on the etiology of vascular brain injury in dementia.

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Brain Tumor Detection and Classifiaction Using CNN Algorithm and Deep Learning Techniques

2020 International Conference on Advanced Science and Engineering (ICOASE) ◽

10.1109/icoase51841.2020.9436599 ◽

2020 ◽

Author(s):

Sultan B. Fayyadh ◽

Abdullahi A. Ibrahim

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Tumor Detection ◽

Learning Techniques

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Meyer’s Loop Anatomy Demonstrated Using Diffusion Tensor MR Imaging and Fiber Tractography at 3T

Acta Medica Marisiensis ◽

10.2478/amma-2014-0045 ◽

2014 ◽

Vol 60 (5) ◽

pp. 215-222 ◽

Cited By ~ 1

Author(s):

Cristina Goga ◽

Zeynep Firat ◽

Klara Brinzaniuc ◽

Is Florian

Keyword(s):

Diffusion Tensor Imaging ◽

Diffusion Tensor ◽

Region Of Interest ◽

Fiber Tractography ◽

Brain Images ◽

3 Dimensional ◽

Software Release ◽

Magnetic Resonance Imaging Mri ◽

Meyer’S Loop

Abstract Objective: The ultimate anatomy of the Meyer’s loop continues to elude us. Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) may be able to demonstrate, in vivo, the anatomy of the complex network of white matter fibers surrounding the Meyer’s loop and the optic radiations. This study aims at exploring the anatomy of the Meyer’s loop by using DTI and fiber tractography. Methods: Ten healthy subjects underwent magnetic resonance imaging (MRI) with DTI at 3 T. Using a region-of-interest (ROI) based diffusion tensor imaging and fiber tracking software (Release 2.6, Achieva, Philips), sequential ROI were placed to reconstruct visual fibers and neighboring projection fibers involved in the formation of Meyer’s loop. The 3-dimensional (3D) reconstructed fibers were visualized by superimposition on 3-planar MRI brain images to enhance their precise anatomical localization and relationship with other anatomical structures. Results: Several projection fiber including the optic radiation, occipitopontine/parietopontine fibers and posterior thalamic peduncle participated in the formation of Meyer’s loop. Two patterns of angulation of the Meyer’s loop were found. Conclusions: DTI with DTT provides a complimentary, in vivo, method to study the details of the anatomy of the Meyer’s loop.

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