Improving Cell Image Segmentation by Using Isotropic Undecimated Wavelet Transform

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dc.contributor.authorToptas, Murat
dc.contributor.authorToptas, Buket
dc.contributor.authorHanbay, Davut
dc.date.accessioned2026-04-04T13:33:24Z
dc.date.available2026-04-04T13:33:24Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractCell images play a vital role in biological research and medical diagnoses, as they provide valuable information about the structure and function of cells. Specifically, accurate segmentation of cell images is critically important for the detection of abnormal cells and the early diagnosis of various diseases. This paper introduces a transformative approach that integrates the Isotropic Undecimated Wavelet Transform into the input layer of established deep learning architectures such as U-Net, SegNet, and FCN, thereby enhancing their ability to accurately delineate cell boundaries without the need for data augmentation or intervention in the depth of network architectures. The proposed method significantly enhances the contrast between cells and the background, which is crucial for reliable segmentation. Extensive experiments conducted on two datasets demonstrate that the preprocessing with Isotropic Undecimated Wavelet Transform significantly boosts the performance of these architectures. On Dataset1, the U-Net model enhanced with Isotropic Undecimated Wavelet Transform achieved a global accuracy of 0.988, a mean Intersection over Union of 0.972, and a mean Dice coefficient of 0.971, outperforming all other metrics. On Dataset2, the SegNet model enhanced with Isotropic Undecimated Wavelet Transform achieved up to a global accuracy of 0.976, a mean Intersection over Union of 0.905, and a mean Dice coefficient of 0.959, showcasing the best performance across all metrics. The method's consistent success in improving segmentation across different datasets and architectures has been empirically validated through experimental studies.
dc.identifier.doi10.1109/ACCESS.2024.3487481
dc.identifier.endpage159912
dc.identifier.issn2169-3536
dc.identifier.orcid0000-0001-6978-5289
dc.identifier.scopus2-s2.0-85209176490
dc.identifier.scopusqualityQ1
dc.identifier.startpage159902
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3487481
dc.identifier.urihttps://hdl.handle.net/11616/109141
dc.identifier.volume12
dc.identifier.wosWOS:001349760000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectComputer architecture
dc.subjectMicroprocessors
dc.subjectImage segmentation
dc.subjectFeature extraction
dc.subjectAccuracy
dc.subjectWavelet transforms
dc.subjectImage edge detection
dc.subjectSemantics
dc.subjectMedical diagnostic imaging
dc.subjectImage color analysis
dc.subjectBlood cells
dc.subjectconvolution neural networks
dc.subjectcell segmentation
dc.subjectisotropic undecimated wavelet transform
dc.titleImproving Cell Image Segmentation by Using Isotropic Undecimated Wavelet Transform
dc.typeArticle

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