Wyniki wyszukiwania - BazTech

1

Comparative Analysis and Fusion of MRI and PET Images based on Wavelets for Clinical Diagnosis

Sebastian Jinu, King Gnana

International Journal of Electronics and Telecommunications

|

2022

|

Vol. 68. No. 4

867--873

EN

Nowadays, Medical imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT), and Computed Tomography (CT) play a crucial role in clinical diagnosis and treatment planning. The images obtained from each of these modalities contain complementary information of the organ imaged. Image fusion algorithms are employed to bring all of this disparate information together into a single image, allowing doctors to diagnose disorders quickly. This paper proposes a novel technique for the fusion of MRI and PET images based on YUV color space and wavelet transform. Quality assessment based on entropy showed that the method can achieve promising results for medical image fusion. The paper has done a comparative analysis of the fusion of MRI and PET images using different wavelet families at various decomposition levels for the detection of brain tumors as well as Alzheimer’s disease. The quality assessment and visual analysis showed that the Dmey wavelet at decomposition level 3 is optimum for the fusion of MRI and PET images. This paper also compared the results of several fusion rules such as average, maximum, and minimum, finding that the maximum fusion rule outperformed the other two.

2

Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE

Hashemzehi Raheleh, Mahdavi Seyyed Javad Seyyed, Kheirabadi Maryam, Kamel Seyed Reza

Biocybernetics and Biomedical Engineering

|

2020

|

Vol. 40, no. 3

1225--1232

EN

A brain tumor is an abnormal growth of cells inside the skull. Malignant brain tumors are among the most dreadful types of cancer with direct consequences such as cognitive decline and poor quality of life. Analyzing magnetic resonance imaging (MRI) is a popular technique for brain tumor detection. In this paper, we use these images to train our new hybrid paradigm which consists of a neural autoregressive distribution estimation (NADE) and a convolutional neural network (CNN). We subsequently test this model with 3064 T1-weight-ed contrast-enhanced images with three types of brain tumors. The results demonstrate that the hybrid CNN-NADE has a high classification performance as regards the availability of medical images are limited.

3

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

Dey Nilanjan, Rajinikanth V., Shi Fuqian, Tavares João Manuel R.S., Moraru Luminita, Arvind Karthik K., Lin Hong, Kamalanand K., Emmanuel C.

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 3

843--856

EN

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance- Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation.

4

Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms

Kabir Anaraki Amin, Ayati Moosa, Kazemi Foad

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 1

63--74

EN

Gliomas are the most common type of primary brain tumors in adults and their early detection is of great importance. In this paper, a method based on convolutional neural networks (CNNs) and genetic algorithm (GA) is proposed in order to noninvasively classify different grades of Glioma using magnetic resonance imaging (MRI). In the proposed method, the architecture (structure) of the CNN is evolved using GA, unlike existing methods of selecting a deep neural network architecture which are usually based on trial and error or by adopting predefined common structures. Furthermore, to decrease the variance of prediction error, bagging as an ensemble algorithm is utilized on the best model evolved by the GA. To briefly mention the results, in one case study, 90.9 percent accuracy for classifying three Glioma grades was obtained. In another case study, Glioma, Meningioma, and Pituitary tumor types were classified with 94.2 percent accuracy. The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images. Due to the flexible nature of the method, it can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.

5

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Savareh B. A., Emami H., Hajiabadi M., Ghafoori M., Azimi S. M.

Polish Journal of Medical Physics and Engineering

|

2018

|

Vol. 24, Iss. 1

48--53

EN

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

6

Mathematical modeling of fluid flow in brain tumor

Riahi D. N., Roy R.

Journal of Theoretical and Applied Mechanics

|

2014

|

Vol. 52 nr 1

271--279

EN

We consider the problem of fluid flow in a brain tumor. We develop a mathematical model for the one-dimensional fluid flow in a spherical tumor where the spatial variations of the interstitial velocity, interstitial pressure and the drug concentration within the tumor are only with respect to the radial distance from the center of the tumor. The interstitial ve- locity in the radial direction and the interstitial pressure are determined analytically, while the radial variations of two investigated drug concentrations were determined numerically. We calculated these quantities in the tumor, in a corresponding normal tissue and for the concentrations also in the cavity that can exist after the tumor is removed. We determine, in particular, the way the interstitial pressure and velocity vary, which agrees with the expe- riments, as well as the way one drug concentration changes in the presence or absence of a second drug concentration within the tumor. We find that the amount of drug delivery in the tumor can be enhanced in the presence of another drug in the tumor, while the ratio of the amount of one drug in the tumor to its amount in the normal tissue can be reduced in the presence of the second drug in the tumor and the tissue.

7

The Cellular Phone-Induced Electromagnetic Radiation as the Risk Factor in Brain Cancer – a Survey of Recent Research

Stankiewicz W., Krawczyk A., Kieliszek J.

Przegląd Elektrotechniczny

|

2013

|

R. 89, nr 2b

25-27

EN

This paper summarizes our current knowledge about the potential influence of exposition to electromagnetic field generated by cellular phone technology and other everyday-use appliances, on the risk of onset of brain tumors. The results of several large-scale international epidemiologic studies are compared and analyzed in detail. No results were found to clearly prove the significant effect of cellular phones, base stations, microwave ovens etc. in enhancing the risk of brain tumor incidence.

PL

W artykule podsumowano bieżącą wiedzę na temat możliwego wpływu pola elektromagnetycznego generowanego przez terminale telefonii mobilnej na powstawanie guzów mózgu. Wyniki międzynarodowych badań epidemiologicznych zostały szczegółowo porównane i przedyskutowane. Badania te wykazały, że nie ma istotnego wpływu telefonów komórkowych na podniesienie ryzyka powstawania gózów mózgu.

8

Texture Analysis for 3D Classification of Brain Tumor Tissues

Arunadevi B., Deepa S.

Przegląd Elektrotechniczny

|

2013

|

R. 89, nr 4

338-342

EN

This paper investigates on extending and comparing the Gray level co-occurrence matrices (GLCM) and 3D Gabor filters in volumetric texture analysis of brain tumor tissue classification. The extracted features are sub-selected by genetic algorithm for dimensionality reduction and fed into Extreme Learning Machine Classifier. The organizational prototype of image voxels distinctive to the underlying substrates in a tissue is been evaluated and validated on public and clinical dataset revealing 3D GLCM more appropriate towards brain tumor tissue classification.

PL

W artykule zbadano i porównano algorytmy klasyfikacji tkanki guza mózgu – GLCM i filtry Gabora 3D. Właściwości ekstrakcji były selekcjonowane przy użyciu algorytmu genetycznego i klasyfikatora ELM.

9

Application of region growing method to brain tumor segmentation - preliminary results

Piekar E., Szwarc P., Sobotnicki A., Momot M.

Journal of Medical Informatics & Technologies

|

2013

|

Vol. 22

153--160

EN

In this article image have been subject to segmentation using Matlab software, i.e. T1 in normal conditions, perfusion images and images after administering a contrast agent. The tumor in images made in normal conditions was difficult to identify. The images obtained after administering the contrast agent confirmed that the homogeneity criterion has been appropriately selected. In perfusion images the pixels of the background were added to the tumor. When the parameters were changed i.e. pixel counter or neighborhood type the method became more efficient; the tumor boundaries were outlined more precisely. The region growing method enables precise tumor detection; however, the selection of an appropriate homogeneity criterion is a prerequisite for correct segmentation.

10

Detecting Pathologies with Homology Algorithms in Magnetic Resonance Images of Brain

Żelawski M.

Machine Graphics and Vision

|

2009

|

Vol. 18, No. 3

253-266

EN

This paper describes a method for detecting the presence of pathological changes in two-dimensional brain images from magnetic resonance examination. The proposed idea is based on homology theory, which makes it easily extendable to three-dimensional brain images in particular it may be applied to the computer tomography data.