Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region‐growing and quasi‐Monte Carlo‐expectation maximization algorithm

2019 ◽  
Vol 30 (1) ◽  
pp. 104-111 ◽  
Author(s):  
Belkacem Hachemi ◽  
Zouaoui Chama ◽  
Fatiha Alim‐Ferhat ◽  
El‐Sedik Lamini ◽  
Abdelkader Abderrahmane ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Monte Carlo ◽  
Brain Tumor ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Region Growing ◽  
Resonance Imaging ◽  
Quasi Monte Carlo ◽  
Fully Automatic ◽  
Monte Carlo Expectation Maximization

scholarly journals A quantile variant of the expectation–maximization algorithm and its application to parameter estimation with interval data

2018 ◽  
Vol 12 (3) ◽  
pp. 253-272 ◽  
Author(s):  
Chanseok Park
Keyword(s):  
Monte Carlo ◽  
Expectation Maximization ◽  
Likelihood Function ◽  
Expectation Maximization Algorithm ◽  
Parametric Models ◽  
Maximum Likelihood Estimates ◽  
Computational Technique ◽  
Stable Convergence ◽  
Explicit Integration ◽  
Monte Carlo Expectation Maximization

The expectation–maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed. The expectation–maximization is best suited for situations where the expectation in each E-step and the maximization in each M-step are straightforward. A difficulty with the implementation of the expectation–maximization algorithm is that each E-step requires the integration of the log-likelihood function in closed form. The explicit integration can be avoided by using what is known as the Monte Carlo expectation–maximization algorithm. The Monte Carlo expectation–maximization uses a random sample to estimate the integral at each E-step. But the problem with the Monte Carlo expectation–maximization is that it often converges to the integral quite slowly and the convergence behavior can also be unstable, which causes computational burden. In this paper, we propose what we refer to as the quantile variant of the expectation–maximization algorithm. We prove that the proposed method has an accuracy of [Formula: see text], while the Monte Carlo expectation–maximization method has an accuracy of [Formula: see text]. Thus, the proposed method possesses faster and more stable convergence properties when compared with the Monte Carlo expectation–maximization algorithm. The improved performance is illustrated through the numerical studies. Several practical examples illustrating its use in interval-censored data problems are also provided.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

Multiple myeloma manifesting as an intraventricular brain tumor

2009 ◽  
Vol 110 (4) ◽  
pp. 737-739 ◽  
Author(s):  
Joo-Hun David Eum ◽  
Astrid Jeibmann ◽  
Werner Wiesmann ◽  
Werner Paulus ◽  
Heinrich Ebel
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Myeloma ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Lateral Ventricle ◽  
Histopathological Examination ◽  
Brain Parenchyma ◽  
Resonance Imaging

Primary intracerebral manifestation of multiple myeloma is rare and usually arises from the meninges or brain parenchyma. The authors present a case of multiple myeloma primarily manifesting within the lateral ventricle. A 67-year-old man was admitted with headache accompanied by slowly progressing right hemiparesis. Magnetic resonance imaging showed a large homogeneous contrast-enhancing intraventricular midline mass and hydrocephalus. The tumor was completely resected, and histopathological examination revealed plasmacytoma. After postoperative radio- and chemotherapy, vertebral osteolysis was detected as a secondary manifestation of multiple myeloma.

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