Intelligent Computing in Medical Imaging

Abstract

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.

An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images

“…The upper limit can be computed with the help of an error controlling threshold ∂ and is defined in equation (15). A group of pixels i.e., a superpixel j with total number of pixels j cnt in the region j R of the superpixel is represented by a representative value j v , calculated using equation (16).…”

“…There are several approaches reported in the literature that tries to detect the presence of the COVID-19 virus from chest CT scans and/or X-ray images. In most of the recent works [3] – [14] , the deep learning approach is adopted to automatically classify the images [15] , [16] . In [17] , a deep learning-based model is proposed to automatically segment and classify the COVID-19 CT scan images.…”

SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation

“…Machine learning belongs to the artificial intelligence domain and has many applications in different domains. It is an emerging field of research and continuous development effort can be observed in the literature [1] , [2] , [3] . One of the earlier applications of machine learning is noticed in the checker games in 1959 and it replaces the humans to play the game [4] .…”

A Deep Learning Model to Classify and Detect Brain Abnormalities in Portable Microwave Based Imaging System

“…At present, different modern imaging modalities: PET(positron emission tomography), X-ray screening, MRI (magnetic resonance imaging), CT (computed tomography) scans, and ultrasound screening are used to identify brain tumors in medical applications [2][3][4]. The key disadvantages of the conventional medical imaging modalities are increasing cancerous risk due to high dose radiation, lower susceptibility, high ionizing with brain cells, costly, and danger for the old patient and pregnant women [2,[5][6][7][8][9][10]. In the last decades, microwave imaging (EMI) research has been growing in medical applications due to its abundant properties: non-ionizing radiation, inexpensive, non-invasive, and very safe for the human body compared to conventional medical imaging modalities [11][12][13][14][15][16][17].…”