The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

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Machine Learning Techniques for Medical Image Processing

10.1109/ehb52898.2021.9657673 ◽

2021 ◽

Author(s):

Baidaa Mutasher Rashed ◽

Nirvana Popescu

Keyword(s):

Machine Learning ◽

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Machine Learning Techniques ◽

Learning Techniques

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Manifold Learning for Medical Image Registration, Segmentation, and Classification

Machine Learning in Computer-Aided Diagnosis - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-0059-1.ch017 ◽

2012 ◽

pp. 351-372 ◽

Cited By ~ 21

Author(s):

Paul Aljabar ◽

Robin Wolz ◽

Daniel Rueckert

Keyword(s):

Image Analysis ◽

Image Registration ◽

Manifold Learning ◽

Medical Image ◽

Biomarker Discovery ◽

Dimensional Space ◽

Single Point ◽

Machine Learning Techniques ◽

Local Linear Embedding ◽

Learning Techniques

The term manifold learning encompasses a class of machine learning techniques that convert data from a high to lower dimensional representation while respecting the intrinsic geometry of the data. The intuition underlying the use of manifold learning in the context of image analysis is that, while each image may be viewed as a single point in a very high-dimensional space, a set of such points for a population of images may be well represented by a sub-manifold of the space that is likely to be non-linear and of a significantly lower dimension. Recently, manifold learning techniques have begun to be applied to the field of medical image analysis. This chapter will review the most popular manifold learning techniques such as Multi-Dimensional Scaling (MDS), Isomap, Local linear embedding, and Laplacian eigenmaps. It will also demonstrate how these techniques can be used for image registration, segmentation, and biomarker discovery from medical images.

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A Comparative Review of the Role of Deep Learning in Medical Image Processing

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics ◽

10.1201/9781003038450-10 ◽

2021 ◽

pp. 171-193

Author(s):

Erapaneni Gayatri ◽

S. L. Aarthy

Keyword(s):

Image Processing ◽

Deep Learning ◽

Medical Image ◽

Medical Image Processing ◽

Comparative Review

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Establishing an International Reference Image Database for Research and Development in Medical Image Processing

Methods of Information in Medicine ◽

10.1055/s-0038-1633884 ◽

2004 ◽

Vol 43 (04) ◽

pp. 409-412 ◽

Cited By ~ 7

Author(s):

M. Prinz ◽

S. Schneider ◽

O. Sipilä ◽

K. Spinnler ◽

J.-P. Vallée ◽

...

Keyword(s):

Image Processing ◽

Research And Development ◽

Medical Image ◽

Medical Image Processing ◽

Image Database ◽

Reference Image ◽

European Federation ◽

Medical Problems ◽

Comprehensive Reference

Summary Introduction: The lack of comparability of evaluation results is one of the major obstacles of research and development in Medical Image Processing (MIP). The main reason for that is the usage of different image datasets with different quality, size and Gold standard. Objectives: Therefore, one of the goals of the Working Group on Medical Image Processing of the European Federation for Medical Informatics (EFMI WG MIP) is to develop first parts of a Reference Image Database. Methods: Kernel of the concept is to identify highly relevant medical problems with significant potential for improvement by MIP, and then to provide respective reference datasets. The EFMI WG MIP has primarily the role of a specifying group and an information broker, while the provider user relationships are defined by bilateral co-operation or license agreements. Results: An explorative database prototype has been implemented using the MySQL database software on the Web. Templates for provider user agreements have been worked out and already applied for own ‘pre-RID-MIP’ co-operations of the authors. Discussion and Conclusion: First steps towards a comprehensive reference image database have been done. Issues like funding, motivation, management, provision of Gold standards and evaluation guidelines are to be solved. Due to the interest from research groups and industry the efforts will be continued.

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A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/571022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 874-887

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Image Processing ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Medical Image ◽

Data Science ◽

Medical Image Processing ◽

Machine Learning Techniques ◽

Diabetes Diagnosis

The aim of the study is to compare, assess the optimum tools as well as the techniques and advanced features focused on prediction of diabetes diagnosis based on machine learning tactics and diabetic retinopathy using Artificial Intelligence. The literature on data science, Artificial Intelligence (AI) contains important knowledge and understanding of AI entities such as Data science, machine learning, deep learning, Medical image processing, feature extraction, classification techniques, etc. Diabetes diagnosis is a phenomenon that impacts individuals around the globe. Now, with diabetes impacting people from children to the elderly, the out-dated approaches to diabetes diagnosis should be replaced with new, time-saving technologies. There's several studies carried out by researchers to recognise and predict diabetes. Here plenty of classifiers in machine learning can be used, such as KNN, Random Tree, etc.They can save time and get more precise outcome when using these techniques to predict diabetes. Diabetic retinopathy (DR) is a typical disorder of diabetic disease that induces vision-impacting lesions in the retina. It also can turn to visual impairment if it is not addressed early. DR therapy only helps vision. Deep learning has in recent times being one of the most widely used approaches that has accomplished higher outcomes in so many fields, especially in the analysing and identification of medical image classification. In medical image processing, convolutional neural networks (CNN) using transfer learning are commonly used as a deep learning approach and they are incredibly beneficial. Key words: Diab

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A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183863 ◽

2018 ◽

pp. 194-200

Author(s):

J. Magelin Mary ◽

Chitra K. ◽

Y. Arockia Suganthi

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Processing Technique ◽

Input Image ◽

Image Processing Technique ◽

Computational Time ◽

Image Mining ◽

The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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