scholarly journals Region Of Interest Based Image Classification: A Study in MRI Brain Scan Categorization

10.5772/50019 ◽  
2012 ◽  
Author(s):  
Ashraf Elsayed ◽  
Frans Coenen ◽  
Marta Garca-Fiana ◽  
Vanessa Sluming
Keyword(s):  
Image Classification ◽  
Region Of Interest ◽  
Mri Brain ◽  
Brain Scan

Region of Interest Based MRI Brain Image Compression Using Peano Space Filling Curve

2016 ◽  
Vol 11 (2) ◽  
pp. 114-120 ◽  
Author(s):  
C. Peter Devadoss ◽  
Balasubramanian Sankaragomathi ◽  
Thirugnanasambantham Monica
Keyword(s):  
Image Compression ◽  
Region Of Interest ◽  
Space Filling ◽  
Brain Image ◽  
Space Filling Curve ◽  
Mri Brain ◽  
Filling Curve ◽  
Mri Brain Image

Clinical and etiological evaluation of secondary seizures by computed tomography and electroencephalogram

2016 ◽  
Vol 5 (10) ◽  
pp. 4982
Author(s):  
Archana Aher* ◽  
Satish Gore
Keyword(s):  
Meta Analysis ◽  
Temporal Region ◽  
Government Medical College ◽  
Occipital Region ◽  
Parietal Region ◽  
Medical College ◽  
Mri Brain ◽  
Brain Scan ◽  
Ct Brain ◽  
Clonic Convulsions

This study was conducted to determine the clinical evaluation and various etiological factors of secondary seizures in patients admitted to Government Medical College, Nagpur. We evaluated 58 patients of secondary seizures from Dec 2011 to Oct 2013. Secondary seizures were defined as case of seizure with CT (brain) or MRI (brain) abnormality1. Out of 58 cases 35 were males and 23 were females. Mean age of study subjects was 34.85. The commonest presenting feature was generalized tonic clonic convulsions (42 patients) followed by focal seizures (16 patients).  Todd’s palsy was observed in 4 cases. Aura was present in 24 cases. According to CT brain scan the aetiology was – neurocysticercosis (34.48%), post stroke (27.59%), tuberculoma (24.14%). Space occupying lesions(SOLs) were present in 8 patients, out of whom 4 had brain tumour, 2 patients had brain abscess, 1 had hydatid cyst and 1 had metastasis. Majority of lesions were located in frontal region (58.62%), followed by in parietal region (44.83%), in temporal region (25.86%) and in occipital region (13.79 % patients). In our study neurocysticercosis was found to be the commonest cause of secondary seizures. As in a meta-analysis it was found that cysticidal drugs result in better outcome in patients of neurocysticecosis, we recommend that the patients of secondary seizures should be identified for the aetiology and treated at the earliest2.

Automated alignment of MRI brain scan by anatomic landmarks

2009 ◽  
Author(s):  
Li Zhang ◽  
Qing Xu ◽  
Chong Chen ◽  
Carol L. Novak
Keyword(s):  
Anatomic Landmarks ◽  
Mri Brain ◽  
Brain Scan ◽  
Automated Alignment

scholarly journals CT/MRI brain scan findings: Significant or not?

2016 ◽  
Vol 1 (2) ◽  
pp. 25-25
Author(s):  
Cesar M. Limjoco
Keyword(s):  
Mri Brain ◽  
Brain Scan

scholarly journals MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1033
Author(s):  
Ali M. Hasan ◽  
Hamid A. Jalab ◽  
Rabha W. Ibrahim ◽  
Farid Meziane ◽  
Ala’a R. AL-Shamasneh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Short Term Memory ◽  
Local Binary Patterns ◽  
Quantum Entropy ◽  
Brain Scans ◽  
Quantum Calculus ◽  
Mri Brain ◽  
Brain Scan ◽  
The Brain

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.

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