MEDICAL IMAGING APPARATUS, ULTRASONIC IMAGING APPARATUS, MAGNETIC RESONANCE IMAGING APPARATUS, MEDICAL IMAGE PROCESSING APPARATUS, AND MEDICAL IMAGE PROCESSING METHOD

2013 ◽  
Vol 133 (5) ◽  
pp. 3220 ◽  
Author(s):  
Kota Aoyagi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Medical Image ◽  
Ultrasonic Imaging ◽  
Medical Image Processing ◽  
Processing Method ◽  
Resonance Imaging ◽  
Image Processing Method

scholarly journals Medical Images Segmentation Based on Unsupervised Algorithms: A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 71-80
Author(s):  
Revella E. A. Armya Armya ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Automatic Process ◽  
X Rays ◽  
Resonance Imaging ◽  
Unsupervised Algorithms

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

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.

Magnetic Resonance Imaging Multispectral Tissue Classification

Physiology ◽  
1988 ◽  
Vol 3 (4) ◽  
pp. 148-154
Author(s):  
MW Vannier ◽  
CM Speidel ◽  
DL Rickman
Keyword(s):  
Magnetic Resonance Imaging ◽  
Image Processing ◽  
Magnetic Resonance ◽  
Statistical Evaluation ◽  
Multispectral Image ◽  
Processing Technology ◽  
Resonance Imaging ◽  
Analysis Application ◽  
Software And Hardware ◽  
Multispectral Analysis

The application of NASA multispectral image processing technology for analysis of magnetic resonance imaging scans has been studied. Software and hardware capability has been developed, and a statistical evaluation of multispectral analysis application to magnetic resonance imaging scans of the head has been performed.

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