scholarly journals Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

2008 ◽  
Vol 43 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Michael C. Lee ◽  
Sarah J. Nelson
Keyword(s):  
Magnetic Resonance Imaging ◽  
Pattern Recognition ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Contrast Enhancement ◽  
Resonance Imaging ◽  
Imaging And Spectroscopy

scholarly journals An Efficient Hybrid Clustering and Feature Extraction Techniques for Brain Tumor Classification

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 556-581
Author(s):  
Dr.N. Gomathi ◽  
A. Geetha
Keyword(s):  
Magnetic Resonance Imaging ◽  
Feature Extraction ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Contrast Enhancement ◽  
Resonance Imaging ◽  
Extraction Techniques ◽  
Hybrid Clustering ◽  
Proposed Model ◽  
Skull Stripping

Most aggressive and common disease is Brain tumors and it leads to very short life expectancy in its highest grade. For proper treatment, such tumors needs to be identified in early stages and detecting brain tumors, medical imaging is used as an important tool. Although, for diagnosing such tumors, MRI (Magnetic Resonance Imaging) is used very often and it is assumed as a highly suitable technique. From brain magnetic resonance imaging (MRI) data, edema and tumor inference is a challenging task due to brain tumors blurred boundaries, complex structure and external factors like noise. For alleviating noise sensitivity and enhancing segmentation stability, a hybrid clustering algorithm is proposed in this research work. Certain processes like classification, feature extraction, hybrid clustering and pre-processing are included in this proposed model. For segmentation of brain tumors, proposed a morphological operation. Skull stripping and contrast enhancement are two process performed in pre-processing stage. It is possible to detect high contrast regions under contrast enhancement. In second stage, Enhanced K- means algorithm is combined with Fuzzy C- Means Clustering (FCM), where images are segmented as clusters. Algorithm’s stability can be enhanced using this clustering techniques while minimizing clustering parameter’s sensitivity. Segmented objects are converted into representations using representation and feature extraction techniques. Major attributes and features are described in a better manner using these techniques. The Fast Discrete Curvelet Transform (FDCT) is used for performing feature extraction in this technique for minimizing complexity and enhancing performance. At last, for classification, deep belief network (DBN) is used in this work. And it uses the concept of optimized DBN, for which Improved dragonfly optimisation algorithm (IDOA) is utilized. This proposed model is termed as IDOA-DBN model. When compared with other classification techniques, brain tumors can be detected effectively using proposed model.

scholarly journals Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors

2019 ◽  
Vol 6 (04) ◽  
pp. 1 ◽  
Author(s):  
Hiba Mzoughi ◽  
Ines Njeh ◽  
Mohamed Ben Slima ◽  
Ahmed Ben Hamida ◽  
Chokri Mhiri ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Contrast Enhancement ◽  
Resonance Imaging

Magnetic Resonance Imaging Contrast Enhancement of Brain Tumors at 3 Tesla Versus 1.5 Tesla

2002 ◽  
Vol 37 (3) ◽  
pp. 114-119 ◽  
Author(s):  
IRIS-MELANIE NÖBAUER-HUHMANN ◽  
AHMED BA-SSALAMAH ◽  
VLADIMIR MLYNARIK ◽  
MARKUS BARTH ◽  
ALEXANDER SCHÖGGL ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Contrast Enhancement ◽  
3 Tesla ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Contrast

Magnetic Resonance Imaging and Spectroscopy: New Noninvasive In Vivo Approaches in Toxicology Research

1993 ◽  
Vol 21 (4) ◽  
pp. 411-425
Author(s):  
Manfred Brauer
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Fatty Infiltration ◽  
Chronic Ethanol ◽  
Ethanol Administration ◽  
Resonance Imaging ◽  
Human Patient ◽  
Hypoxic Injury ◽  
Imaging And Spectroscopy

Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) give anatomical and biochemical information about a human patient or animal in a non-invasive manner. This unique quality permits the study of toxicological responses of an organ within an intact animal and in a manner in which many fewer animals are needed than by conventional methods of investigation. The use of MRI and MRS in the study of hepatotoxicants, particularly bromobenzene and ethanol, is reviewed. Bromobenzene causes localised hepatic oedema and bioenergetic deterioration; these changes were followed with time by 1H MRI and 31P MRS, respectively. Phosphocholine levels in the liver were found to increase dramatically during bromobenzene-induced damage, possibly related to an intracellular control mechanism in response to tissue damage. The ability of the bromobenzene-challenged liver to metabolise a fructose load was followed by dynamic 31P MRS. Chronic ethanol administration damages the liver. This toxicological process results in the accumulation of fat in the liver, which was followed by fat-selective 1H MRI. When ethanol is no longer administered to the subject, the fatty infiltration subsides, and this process was followed over 16 days in the same animal using fat-selective 1H MRI. Chronic ethanol renders the liver in situ more susceptible to hypoxic injury and less likely to recover afterwards, as shown by 31P MRS.

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.

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