Inverse Problems, Image Analysis, and Medical Imaging

2002 ◽  
Keyword(s):  
Image Analysis ◽  
Medical Imaging ◽  
Inverse Problems

scholarly journals Deep learning-based solvability of underdetermined inverse problems in medical imaging

2021 ◽  
Vol 69 ◽  
pp. 101967
Author(s):  
Chang Min Hyun ◽  
Seong Hyeon Baek ◽  
Mingyu Lee ◽  
Sung Min Lee ◽  
Jin Keun Seo
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Inverse Problems

Intelligent Computing in Medical Imaging

2021 ◽  
pp. 592-608
Author(s):  
Shouvik Chakraborty ◽  
Sankhadeep Chatterjee ◽  
Amira S. Ashour ◽  
Kalyani Mali ◽  
Nilanjan Dey
Keyword(s):  
Image Analysis ◽  
Medical Imaging ◽  
Biomedical Imaging ◽  
Clinical Analysis ◽  
Biological Species ◽  
Imaging Modalities ◽  
Biomedical Image Analysis ◽  
Accurate Analysis ◽  
Intelligent Computing ◽  
Biomedical Image

Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.

Principles and Advanced Methods in Medical Imaging and Image Analysis

2009 ◽  
Vol 36 (4) ◽  
pp. 1460-1460
Author(s):  
E. Russell Ritenour
Keyword(s):  
Image Analysis ◽  
Medical Imaging

scholarly journals Survey on deep learning for pulmonary medical imaging

2019 ◽  
Vol 14 (4) ◽  
pp. 450-469 ◽  
Author(s):  
Jiechao Ma ◽  
Yang Song ◽  
Xi Tian ◽  
Yiting Hua ◽  
Rongguo Zhang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Language Processing ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Rapid Evolution ◽  
Comprehensive Overview ◽  
Feature Representations ◽  
Learning Techniques

AbstractAs a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

scholarly journals Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions

2019 ◽  
Vol 1 (1) ◽  
pp. e180031 ◽  
Author(s):  
Luciano M. Prevedello ◽  
Safwan S. Halabi ◽  
George Shih ◽  
Carol C. Wu ◽  
Marc D. Kohli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Imaging ◽  
Artificial Intelligence Research
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