Evaluation of Ischemic Stroke Region From CT/MR Images Using Hybrid Image Processing Techniques

2018 ◽  
pp. 194-219 ◽  
Author(s):  
Rajinikanth V. ◽  
Suresh Chandra Satapathy ◽  
Nilanjan Dey ◽  
Hong Lin
Keyword(s):  
Image Processing ◽  
Ischemic Stroke ◽  
Hybrid Approach ◽  
Rapid Onset ◽  
Processing Technique ◽  
Experimental Result ◽  
Mr Images ◽  
Brain Abnormality ◽  
Social Group Optimization ◽  
The Brain

An ischemic stroke (IS) naturally originates with rapid onset neurological shortfall, which can be verified by analyzing the internal regions of brain. Computed tomography (CT) and magnetic resonance image (MRI) are the commonly used non-invasive medical examination techniques used to record the brain abnormalities for clinical study. In order to have a pre-opinion regarding the brain abnormality in clinical level, it is essential to use a suitable image processing tool to appraise the digital CT/MR images. In this chapter, a hybrid image processing technique based on the social group optimization assisted Tsallis entropy and watershed segmentation (WS) is proposed to examine ischemic stroke region from digital CT/MR images. For the experimental study, the digital CT/MRI datasets like Radiopedia, BRATS-2013, and ISLES-2015 are considered. Experimental result of this study confirms that, proposed hybrid approach offers superior results on the considered image datasets.

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>

scholarly journals A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3063 ◽  
Author(s):  
WonSeok Yang ◽  
Jun-Yong Hong ◽  
Jeong-Youn Kim ◽  
Seung-ho Paik ◽  
Seung Hyun Lee ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Image Quality ◽  
Statistical Evaluation ◽  
Processing Technique ◽  
Singular Value ◽  
Image Processing Technique ◽  
Stroke Patients ◽  
Value Decomposition ◽  
The Brain

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.

scholarly journals Image processing-based mTICI grading after endovascular treatment for acute ischemic stroke

2021 ◽  
Vol 7 (2) ◽  
pp. 235-238
Author(s):  
Muhannad Sabieleish ◽  
Maximilian Thormann ◽  
Jonathan Metzler ◽  
Axel Boese ◽  
Michael Friebe ◽  
...  
Keyword(s):  
Image Processing ◽  
Ischemic Stroke ◽  
Endovascular Treatment ◽  
Mechanical Thrombectomy ◽  
Processing Technique ◽  
Automated System ◽  
Image Processing Technique ◽  
Pixel Intensity ◽  
Mca Occlusion ◽  
Starting Point

Abstract Introduction : The grade of reperfusion after endovascular treatment of ischemic stroke e.g. mechanical thrombectomy is determined based on the mTICI score. The mTICI score shows significant interrater variability; it is usually biased towards better reperfusion results if selfassessed by the operator. We therefore developed a semiautomated image processing technique for assessing and evaluating the degree of reperfusion independently, resulting in a more objective mTICI score. Methods: Fifty angiography datasets of patients who were treated with mechanical thrombectomy for middle cerebral artery (MCA) occlusion were selected from our database. Image datasets were standardized by adjustment of field of view and orientation. Based on pixel intensity features, the internal carotid artery (ICA) curve was detected automatically and used as a starting point for identifying the target downstream territory (TDT) of the MCA on the DSA series. Furthermore, a grid with predefined dimensions was used to divide the TDT into checkzones and be classified as perfused or unperfused. Results: The algorithm detected the TDT and classified each zone of the grid as perfused or unperfused. Lastly, the percentage of the perfused area in the TDT was calculated for each patient and compared to the grading of experienced clinical users. Conclusion: A semi-automatic image-processing workflow was developed to evaluate perfusion rate based on angiographic images. The approach can be used for the objective calculation of the mTICI score. The semi-automatic grading is currently feasible for MCA occlusion but can be extended for other brain territories. The work shows a starting point for a machine learning approach to achieve a fully automated system that can evaluate and give an accurate mTICI score to become a common AI-based grading standard in the coming near future.

scholarly journals Edge detection for detection of brain tumour in CT images

2018 ◽  
Vol 7 (2.25) ◽  
pp. 95
Author(s):  
T R. Thamizhvani ◽  
A Josephin Arockia Dhivya ◽  
S Akshaya ◽  
K Dhanalakshmi ◽  
R Chandrasekaran ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Edge Detection ◽  
Brain Tumour ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Standard Data ◽  
Computed Tomography Images ◽  
Edge Operator ◽  
The Brain

Brain tumour can be defined as the continuous and uncontrolled growth of the cells in the regions of brain. Analysis and detection of brain tumours from the computed tomography images can be performed by various image processing algorithms. Edge detection is special type of image processing technique, which uses operators for functioning. The Computed Tomography images are obtained from the standard data-base which undergoes pre-processing technique. Contrast adjustment is performed to enhance the region of brain tumour. Edge operators of different types are applied to the images for identification of the boundary of the brain tumour region. Appropriate edge operator for de-termination of the boundary is defined by comparing the image quality and accuracy parameters. These parameters illustrate that canny oper-ator is described to be more definite for the detection and analysis of the boundary and region of brain tumour in Computed Tomography images.   

Evaluation of Surface Roughness of Machined Fiber Reinforced Composites Plastics Using Image Processing

2014 ◽  
Vol 573 ◽  
pp. 627-631
Author(s):  
G. Dilli Babu ◽  
K. Sivaji Babu ◽  
B. Uma Maheswar Gowd
Keyword(s):  
Image Processing ◽  
Surface Roughness ◽  
High Volume ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Experimental Result ◽  
Fiber Reinforced ◽  
Reinforced Plastics ◽  
Fiber Reinforced Plastics

The measurement of surface roughness of the machined Fiber Reinforced Plastics is very important to assess the quality of a composite, which is normally carried out using taly-surf stylus instruments. This method of measuring is accepted widely by all the researchers. But, this process is not suitable for high volume applications as it is time consuming and cumbersome. With rising demand of industrial automation in manufacturing process, image processing technique plays an important role in inspection and process monitoring. In this paper, a new parameter for determining surface roughness of machined fiber reinforced specimens was proposed using image processing technique. The experimental result indicates that the surface roughness of machined composites could be predicted with a reasonable accuracy using image processing technique.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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