Hybrid Approach Towards Removal of Streak Artifacts and Region Identification in Brain Radiology

2020 ◽  
Vol 10 (3) ◽  
pp. 604-609
Author(s):  
A. Bhagyalakshmi ◽  
V. Vijaya Chamundeeswari
Keyword(s):  
Hybrid Approach ◽  
Single Image ◽  
Beam Hardening ◽  
Brain Image ◽  
Streak Artifact ◽  
Streak Artifacts ◽  
Under Sampling ◽  
Original Region ◽  
Ct Brain ◽  
The Brain

A streak artifact is an anomaly seen in the CT image of the brain. It is like a dark bands or streaks appearing in an image that is not present in the original region. The appearance of these artifacts leads to misinterpretation of medical diagnosis. Under sampling, photon starvation, motion and beam hardening are some of the reasons for occurrence of streak artifacts. These type of artifacts commonly occur in the posterior fossa of the brain. This paper mainly focuses on region shape and texture identification and removal of streak artifacts exists in the brain image. Initially morphological operators and fuzzy k-means algorithms are used to identify the different regions in the CT brain image. Each region is treated as a single image and features are extracted for each image. The feature descriptors are constructed using region properties and statistical features. The extracted features are compared with threshold brain image to identify the dark bands of streak artifacts.

The Brain Mechanism of Developmental Dyslexia:Evidences From The Brain Image*

2009 ◽  
Vol 36 (11) ◽  
pp. 1394-1401
Author(s):  
Yang YANG ◽  
Hong-Yan BI ◽  
Jiu-Ju WANG
Keyword(s):  
Brain Image ◽  
Brain Mechanism ◽  
The Brain

The Dioptrique

2017 ◽  
Author(s):  
Walter Ott
Keyword(s):  
Sensory Experience ◽  
The Other ◽  
Brain Image ◽  
Brain Motion ◽  
Natural Geometry ◽  
The Common ◽  
The Brain

Descartes’s treatment of perception in the Optics, though published before the Meditations, contains a distinct account of sensory experience. The end of the chapter suggests some reasons for this oddity, but that the two accounts are distinct is difficult to deny. Descartes in the present work topples the brain image from its throne. In its place, we have two mechanisms, one purely causal, the other inferential. Where the proper sensibles are concerned, the ordination of nature suffices to explain why a given sensation is triggered on the occasion of a given brain motion. The same is true with regard to the common sensibles. But on top of this purely causal story, Descartes re-introduces his doctrine of natural geometry.

The Meditations

2017 ◽  
Author(s):  
Walter Ott
Keyword(s):  
Perceptual Experience ◽  
Developmental Reading ◽  
Basic Structure ◽  
Brain Image ◽  
The Real ◽  
The Third ◽  
Real Work ◽  
Sensible Qualities ◽  
The Brain

Despite its difference in aspiration, the Meditations preserves the basic structure of perceptual experience outlined in Descartes’s earliest works. The chapter explores Descartes’s notion of an idea and uses a developmental reading to clear up the mystery surrounding material falsity. In the third Meditation, our protagonist does not yet know enough about extension in order to be able to tell whether her idea of cold is an idea of a real feature of bodies or merely the idea of a sensation. By the time she reaches the end of her reflections, she has learned that sensible qualities are at most sensations. As in his earliest stages, Descartes believes that the real work of perceiving the geometrical qualities of bodies is done by the brain image, which he persists in calling an ‘idea,’ at least when it is the object of mental awareness.

scholarly journals Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)

2021 ◽  
Vol 11 (1) ◽  
pp. 380-390
Author(s):  
Pradipta Kumar Mishra ◽  
Suresh Chandra Satapathy ◽  
Minakhi Rout
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Gray Wolf ◽  
Deep Convolutional Neural Networks ◽  
Brain Tumor Segmentation ◽  
Swarm Optimization ◽  
Brain Image ◽  
Mri Brain ◽  
Whale Optimization ◽  
The Brain

Abstract Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models i.e., GA-DCNN, PSO-DCNN, GWO-DCNN.

Reducing Execution Time in CT Angiography and Dynamic CT Brain Image Registration Through Code Optimisation

2018 ◽  
Author(s):  
Ashrani Aizzuddin Abd. Rahni ◽  
Gunawathi Gunasekaran ◽  
Israna Hossain Arka ◽  
Kalaivani Chellappan ◽  
Shahizon Azura Mukari ◽  
...  
Keyword(s):  
Image Registration ◽  
Ct Angiography ◽  
Execution Time ◽  
Dynamic Ct ◽  
Brain Image ◽  
Ct Brain

Construction of an average CT brain image for brain infarct pattern comparison

2002 ◽  
pp. 425-429
Author(s):  
Cynthia Jongen ◽  
Josien P. W. Pluim ◽  
Max A. Viergever ◽  
Wiro J. Niessen
Keyword(s):  
Brain Infarct ◽  
Brain Image ◽  
Pattern Comparison ◽  
Ct Brain

MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

2021 ◽  
pp. 2150034
Author(s):  
Sandhya Gudise ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Population Based ◽  
Spinal Fluid ◽  
Brain Image ◽  
Tlbo Algorithm ◽  
Wide Range ◽  
Skull Stripping ◽  
The Brain ◽  
Mr Brain

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.

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.

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2017 ◽  
pp. 115-130
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

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