Autors: Georgieva, V. M., Gardeva V.
Title: Adaptive algorithm for CT images enhancement to improve the diagnosis of lung diseases
Keywords: CT images enhancement , diagnosis of lung diseases

Abstract: Image processing methods are used to support decision-making processes of radiologists for improving treatment for lung disease. These methods involve using chest CT images to diagnose and detect abnormal cases in lung. Improving the human visual perception of pulmonary CT images is favorable for the doctor to make a more accurate clinical diagnosis. Thus, improving pulmonary CT as a preprocessing procedure can increase accuracy and efficiency in actual medical applications. In this paper we propose an adaptive algorithm for improving the visual quality of CT images. It consists of contrast enhancement based of combination of Retinex theory and Contrast limited adaptive histogram equalization (CLAHE). A complex adaptive filter based on Wiener and median filtration is available to reduce existing noise. In order to simplify processing we use a tensor presentation of the images. The evaluation of the efficiency of the proposed algorithm is made by objective parameters such as PSNR, SNR,

References

    Issue

    48th International Conference Applications of Mathematics in Engineering and Economics (AMEE 2022), vol. 2939, issue 1, pp. 020003-1–020003-7, 2023, Bulgaria, AIP Conf. Proc., DOI 10.1063/5.0178718

    Вид: пленарен доклад в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus