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Advanced methods for thermal images processing for medical applications


I.S. Kozhevnikova - Ph.D. (Biol.), Senior Research Scientist, Institute for Biomedical Research of Northern (Arctic) Federal University named after M.V. Lomonosov, Arkhangelsk E-mail:, N.A. Ermoshina - Bachelor of Information Systems and Technology of the Baltic State Technical University “VOENMEKH” named after D.F. Ustinov, St.-Petersburg E-mail: M.N. Pankov - Ph.D. (Med.), Associate Professor, Deputy Director, Institute for Biomedical Research of Northern (Arctic) Federal University named after M.V. Lomonosov, Arkhangelsk E-mail:

The systematic review on major achievements in computer aided interpretation of thermogram aims to provide a comprehensive survey of the latest CADx models developments and applications. Thermography can be used to identify and analyze thermal anomalies; it provides useful diagnostic and therapeutic information however the interpretation of thermograms remains a laborious task prone to errors due to visual ambiguity. The temperature pattern of disease especially on early stages can be subtle, nonlinear and invisible. Human eye can capture only a fraction of information because of the limitations of the visual system. Therefore the demand for a proper computer aided thermograms processing and interpretation method is still an open discussion. The CADx development brings additional value helping to overcome subjectivity by providing second opinion and improve reliability of thermography as diagnostic and screening method. The paper briefly outlines the main concepts including some fundamental techniques used in CADx systems and provides the detailed investigation and thorough discussion of reported performance of surveyed systems. Computer algorithms involved in theCADx scheme include four steps: image pre-processing for noise reduction and removal of artifacts, defining regions of interest/segmentation and classification. These algorithms come from the domains of image processing, pattern recognition and machine learning. Research in image processing incorporated with an intelligent system not only provides a solution to identify, classify, and quantify disease patterns from images, but it is likely going to change the way we draw inference from image data. We discuss benefits and drawbacks of methods offering several possible future research directions. In recent years, there have been significant results achieved in CADx models development in terms of accuracy, specifity and senility. These results were possible mainly due to recent sensor improvements, falling costs and advances in images processing techniques and data analysis. Brest cancer diagnosis is the most developed field of research. As the classification accuracy approaches 100%, the proposed methods are able to automatically segment ROIs with the acceptable precision the problems of relatively high rates of false-positive and false-results is still far from being solved. The most promising research areas address such problems as thermal images sequence (dynamic thermography) analysis and interpretation and offers methods to separate cancerous tissue in thermal images, classify benign and malignant cancer. Various attempts in other field of medicine have been made to develop a CADx. However the majority of described systems is fragmented and focuses on one or several aspects within the CADx development cycle. Describing, for example, the feature extractions and selection procedures they leave the segmentation to be performed manually. There is a lack of full cycle models developed and described.


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