Segmentation of Ischemic Stroke Lesion in Brain MRI Based on Social Group Optimization and Fuzzy-Tsallis Entropy

2018 ◽  
Vol 43 (8) ◽  
pp. 4365-4378 ◽  
Author(s):  
V. Rajinikanth ◽  
Suresh Chandra Satapathy
Keyword(s):  
Ischemic Stroke ◽  
Social Group ◽  
Brain Mri ◽  
Tsallis Entropy ◽  
Stroke Lesion ◽  
Social Group Optimization

Stroke Lesion Segmentation and Analysis using Entropy/Otsu’s Function – A Study with Social Group Optimization

2019 ◽  
Vol 14 (4) ◽  
pp. 305-313 ◽  
Author(s):  
Suresh Chandra Satapathy ◽  
Steven Lawrence Fernandes ◽  
Hong Lin
Keyword(s):  
Social Group ◽  
Research Work ◽  
Lesion Segmentation ◽  
Permanent Disability ◽  
Brain Abnormality ◽  
Inspection Process ◽  
Stroke Lesion ◽  
Social Group Optimization ◽  
The Brain ◽  
Otsu’S Function

Background: Stroke is one of the major causes for the momentary/permanent disability in the human community. Usually, stroke will originate in the brain section because of the neurological deficit and this kind of brain abnormality can be predicted by scrutinizing the periphery of brain region. Magnetic Resonance Image (MRI) is the extensively considered imaging procedure to record the interior sections of the brain to support visual inspection process. Objective: In the proposed work, a semi-automated examination procedure is proposed to inspect the province and the severity of the stroke lesion using the MRI. associations while known disease-lncRNA associations are required only. Method: Recently discovered heuristic approach called the Social Group Optimization (SGO) algorithm is considered to pre-process the test image based on a chosen image multi-thresholding procedure. Later, a chosen segmentation procedure is considered in the post-processing section to mine the stroke lesion from the pre-processed image. Results: In this paper, the pre-processing work is executed with the well known thresholding approaches, such as Shannon’s entropy, Kapur’s entropy and Otsu’s function. Similarly, the postprocessing task is executed using most successful procedures, such as level set, active contour and watershed algorithm. Conclusion: The proposed procedure is experimentally inspected using the benchmark brain stroke database known as Ischemic Stroke Lesion Segmentation (ISLES 2015) challenge database. The results of this experimental work authenticates that, Shannon’s approach along with the LS segmentation offers superior average values compared with the other approaches considered in this research work.</P>

U-Net Supported Segmentation of Ischemic-Stroke-Lesion from Brain MRI Slices

2021 ◽  
Author(s):  
Seifedine Kadry ◽  
Robertas Damasevicius ◽  
David Taniar ◽  
Venkatesan Rajinikanth ◽  
Isah A. Lawal
Keyword(s):  
Ischemic Stroke ◽  
Brain Mri ◽  
Stroke Lesion

scholarly journals Stroke Lesion Detection and Analysis in MRI Images Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shujun Zhang ◽  
Shuhao Xu ◽  
Liwei Tan ◽  
Hongyan Wang ◽  
Jianli Meng
Keyword(s):  
Ischemic Stroke ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Brain Mri ◽  
Lesion Detection ◽  
Learning Ability ◽  
Massive Data ◽  
Stroke Patients ◽  
Stroke Lesion ◽  
Assisted Diagnosis

Stroke is a kind of cerebrovascular disease that heavily damages people’s life and health. The quantitative analysis of brain MRI images plays an important role in the diagnosis and treatment of stroke. Deep neural networks with massive data learning ability supply a powerful tool for lesion detection. In order to study the property of the stroke lesions and complete intelligent automatic detection, we collaborated with two authoritative hospitals and collected 5,668 brain MRI images of 300 ischemic stroke patients. All the lesion regions in the images were accurately labeled by professional doctors to ensure the authority and effectiveness of the data. Three categories of deep learning object detection networks including Faster R-CNN, YOLOV3, and SSD are applied to implement automatic lesion detection with the best precision of 89.77%. Meanwhile, statistical analysis of the locations, shapes of the lesions, and possible related diseases is conducted with valid conclusions. The research contributes to the intelligent assisted diagnosis and prevention and treatment of ischemic stroke.

scholarly journals Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

2019 ◽  
Vol 39 (3) ◽  
pp. 843-856 ◽  
Author(s):  
Nilanjan Dey ◽  
V. Rajinikanth ◽  
Fuqian Shi ◽  
João Manuel R.S. Tavares ◽  
Luminita Moraru ◽  
...  
Keyword(s):  
Social Group ◽  
Brain Mri ◽  
Evaluation Tool ◽  
Diffusion Weighted ◽  
Social Group Optimization

scholarly journals Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2080
Author(s):  
Venkatesan Rajinikanth ◽  
Shabnam Mohamed Aslam ◽  
Seifedine Kadry
Keyword(s):  
Feature Extraction ◽  
Ischemic Stroke ◽  
Deep Learning ◽  
Brain Mri ◽  
Brain Abnormality ◽  
Deep Feature ◽  
Feature Based ◽  
Stroke Lesion ◽  
Binary Classifiers ◽  
Feature Based Classification

Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%).

scholarly journals Pure Motor Stroke Secondary to Cerebral Infarction of Recurrent Artery of Heubner after Mild Head Trauma: A Case Report

2016 ◽  
Vol 4 (1) ◽  
pp. 139-141
Author(s):  
Ali Yilmaz ◽  
Zahir Kizilay ◽  
Ayca Ozkul ◽  
Bayram Çirak
Keyword(s):  
Ischemic Stroke ◽  
Head Trauma ◽  
Distal Part ◽  
Brain Mri ◽  
Artery Occlusion ◽  
Acute Ischemia ◽  
Post Traumatic ◽  
The Brain ◽  
Perforating Arteries ◽  
Pure Motor

BACKGROUND: The recurrent Heubner's artery is the distal part of the medial striate artery. Occlusion of the recurrent artery of Heubner, classically contralateral hemiparesis with fasciobrachiocrural predominance, is attributed to the occlusion of the recurrent artery of Heubner and is widely known as a stroke syndrome in adults. However, isolated occlusion of the deep perforating arteries following mild head trauma also occurs extremely rarely in childhood.CASE REPORT: Here we report the case of an 11-year-old boy with pure motor stroke. The brain MRI showed an acute ischemia in the recurrent artery of Heubner supply area following mild head trauma. His fasciobrachial hemiparesis and dysarthria were thought to be secondary to the stretching of deep perforating arteries leading to occlusion of the recurrent artery of Heubner.CONCLUSION: Post-traumatic pure motor ischemic stroke can be secondary to stretching of the deep perforating arteries especially in childhood.

Sign in / Sign up

Export Citation Format

Share Document