Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Department: search.filters.department.İzmir Institute of TechnologyPublisher: search.filters.publisher.Wolters Kluwer Medknow Publications

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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, Sevda; 04.02. Department of Mathematics; 04. Faculty of Science; 01. Izmir Institute of Technology
    Background: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: 1
    Citation - Scopus: 1
    Comparison of Magnetic Seed and Rfid Methods in the Localization of Non-Palpable Breast Lesions
    (Wolters Kluwer Medknow Publications, 2024) Sezgin, Efe; Sanli, Deniz E. Tekcan; Golshan, Mehra; Sezgin, Efe; Celik, Varol; Aydogan, Fatih; 03.08. Department of Food Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Background: Many methods have been developed for localizing non-palpable breast lesions. This study investigated the success rate and surgical results of the magnetic seed (Magseed) and radiofrequency identification (RFID) method, which are relatively new compared to standard wire-guided localizations. Materials and Methods: 20 simulation (10 Magseed, 10 RFID) models were created using turkey breasts and raisins. Raisins containing magnetic seed and RFID tags were placed on the turkey breast. Sentimag (R) probe was used for the Magseed group, and Faxitron LOCalizer (TM) System device was used in the RFID group. Both methods were evaluated in terms of accuracy in detecting breast lesion localization, operation times, excised tissue weights, total resection volume, surgical margin negativity, and re-excision rates. Results: Lesion localization success in both techniques was 100%. While procedure times were statistically significantly shorter in the Magseed group, incision lengths were shorter in the RFID group (P = 0.013, P = 0.007, respectively). No statistically significant difference was found between the groups for the weight of the removed parts, total resection volume, and surgical margin distance (P > 0.05). Conclusion: In this feasibility study, it was concluded that neither the RFID nor Magseed methods had a significant advantage over each other, in terms of localization detection and surgical margin negativity, and both methods could be used successfully for localization.