- Medical Imaging Modalities

2015 ◽  
pp. 22-45
Keyword(s):  
Medical Imaging ◽  
Imaging Modalities

Electrical Impedance Tomography (EIT)

2014 ◽  
pp. 235-273 ◽  
Author(s):  
Tushar Kanti Bera ◽  
J. Nagaraju
Keyword(s):  
Medical Imaging ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Optical Tomography ◽  
Body Part ◽  
Imaging Modalities ◽  
X Rays ◽  
Science And Engineering ◽  
Impedance Tomography ◽  
Anatomy And Physiology

Looking into the human body is very essential not only for studying the anatomy and physiology, but also for diagnosing a disease or illness. Doctors always try to visualize an organ or body part in order to study its physiological and anatomical status for understanding and/or treating its illness. This necessity introduced the diagnostic tool called medical imaging. The era of medical imaging started in 1895, when Roentgen discovered the magical powerful invisible rays called X-rays. Gradually the medical imaging introduced X-Ray CT, Gamma Camera, PET, SPECT, MRI, USG. Recently medical imaging field is enriched with comparatively newer tomographic imaging modalities like Electrical Impedance Tomography (EIT), Diffuse Optical Tomography (DOT), Optical Coherence Tomography (OCT), and Photoacaustic Tomography (PAT). The EIT has been extensively researched in different fields of science and engineering due to its several advantages. This chapter will present a brief review on the available medical imaging modalities and focus on the need of an alternating method. EIT will be discussed with its physical and mathematical aspects, potentials, and challenges.

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.

scholarly journals Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

2019 ◽  
Vol 8 (4) ◽  
pp. 462 ◽  
Author(s):  
Muhammad Owais ◽  
Muhammad Arsalan ◽  
Jiho Choi ◽  
Kang Ryoung Park
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Images ◽  
Diagnosis And Treatment ◽  
Imaging Modalities ◽  
Imaging Data ◽  
Medical Database ◽  
Medical Image Retrieval

Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).

Recent Advances in Ceramic Scintillators

2007 ◽  
Vol 1038 ◽  
Author(s):  
Edgar Van Loef ◽  
Yimin Wang ◽  
Jarek Glodo ◽  
Charles Brecher ◽  
Alex Lempicki ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Medical Imaging ◽  
Atomic Number ◽  
Gamma Ray ◽  
Light Yield ◽  
Emission Tomography ◽  
Imaging Modalities ◽  
Practical Application ◽  
Positron Emission

AbstractA review is presented of recent ceramic scintillator R&D. Attention is focussed on Ce doped gamma-ray scintillators for medical imaging applications. Ceramic scintillators discussed in detail include SrHfO3:Ce and Lu2Hf2O7:Ce. These materials combine a high density and high atomic number with fast emission and a good light yield and may find practical application in medical imaging modalities such as Positron Emission Tomography and Computed Tomography.

Optical coherence tomography among medical imaging modalities: potential and limitations

2007 ◽  
Author(s):  
Natalia Gladkova ◽  
Elena Zagaynova ◽  
Natalia Shakhova ◽  
Alexander M. Sergeev ◽  
Valentin Gelikonov ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Medical Imaging ◽  
Imaging Modalities ◽  
Optical Coherence
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