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Technology for High-Sensitivity Analysis of Medical Diagnostic Images
S R Abulkhanov1, O V Slesarev2, Yu S Strelkov3
1Associate Professor, Department of Engine Manufacturing Technologies, Samara National Research University, 34 Moskovskoye Shosse, Samara, 443086, Russia; Senior Researcher, Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS) - Branch of the Federal Scientific Research Center "Crystallography and Photonics" of the Russian Academy of Sciences, 151 Molodogvardeyskaya St., Samara, 443001, Russia.
Abstract:
Control and analysis of small, inaccessible to human vision changes in medical images make it possible to focus on diagnostically important radiological signs important for the correct diagnosis. The aim of the study was to develop information technology facilitating the early diagnosis of diseases using medical images.
Materials And Methods:
To control changes in the image, we used its transformation based on solving a particular case of the knapsack problem. The proposed transformation is highly sensitive to any changes in the image and provides the possibility to record deviations visually with high accuracy. Medical images were obtained using cone beam computed tomography.
Results:
Practical evaluation of the information technology on tomograms showed the following: the transformed images of healthy bone tissue fragments from different parts of the jaw have similar shapes and nearly the same range of brightness. The transformed image of bone tissue after treatment has a shape close to that of the transformed image of healthy bone tissue. The transformed image of the affected bone tissue has a shape and brightness range differing from the shape and color of the transformed images of healthy bone tissue and bone tissue after treatment. However, transformation of medical images obtained with the Planmeca ProMax 3D Classic device (Finland) allows recording changes that account for less than 0.0001% of the entire image.
Conclusion:
The proposed method allows human vision to capture changes as small as nearly one pixel in the transformed image, which is impossible with the original medical image. Increasing the color contrast of the transformed medical image makes it possible to reveal the structure of the analyzed medical image fragment. The proposed image transformation method can be used for early diagnosis of diseases and in other fields of medicine.
