Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Determining Area Affected by Corona in Lung Computed Tomography Images by Three-Phase Level Set and Shearlet Transform(Wolters Kluwer Medknow Publications, 2025) Aghazadeh, Nasser; Noras, Parisa; Moghaddasighamchi, SevdaBackground:The COVID-19 pandemic has created a critical global situation, causing widespread challenges and numerous fatalities due to severe respiratory complications. Since lung involvement is a key factor in COVID-19 diagnosis and treatment, accurate identification of infected regions in lung images is essential.Methods:We propose a multiphase segmentation method based on the level set framework to determine lunginvolved areas. The shearlet transform, a high-precision directional multiresolution transform, is employed to guide the gradient flow in the level set formulation. Additionally, the phase stretch transform (PST) is applied to enhance the contrast between infected and healthy regions, improving convergence speed during segmentation.Results:The proposed algorithm was tested on 500 lung images. The method accurately identified infected areas, enabling precise calculation of the percentage of lung involvement. The use of the shearlet transform also allowed clear delineation of ground-glass opacity boundaries.Conclusion:The proposed multiphase level set method, enhanced with shearlet and phase stretch transforms, effectively segments COVID-19-infected lung regions. This approach improves segmentation accuracy and computational efficiency, offering a reliable tool for quantitative lung involvement assessment.Article Citation - WoS: 1Citation - Scopus: 1An Iris Segmentation Scheme Based on Bendlets(Springer London Ltd, 2023) Aghazadeh, Nasser; Abbasi, Mandana; Noras, ParisaDue to the effect of agents such as ambiance, transition channel, and other agents, images are polluted by noise during collection, transition, and compaction, leading to decrease image quality. Noise can decrease the accuracy of the next stages of image processing systems. Therefore, one of the vital stages in the novel processing systems is denoising. This article offers a novel image denoising approach using bendlets. Other multi-scale transformations (such as wavelets, curvelets, and shearlets) cannot recognize properties such as location, direction, and curvature of discontinuities well in piecewise stable images. To solve this problem, bendlets are suggested in this article. Bendlets differ from other multi-scale transformations in that an additional bending parameter is utilized for recognizing the curvature of discontinuities. Bendlets need a fewer number of coefficients to identify curvatures than other multi-scale transformations. Furthermore, they help to make the edges more obvious. The suggested approach is utilized on the UBIRIS.V2 database. It earns better accuracy and stability than other multi-scale transformations.