Improving Cell Image Segmentation by Using Isotropic Undecimated Wavelet Transform
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Cell 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.
Açıklama
Anahtar Kelimeler
Computer architecture, Microprocessors, Image segmentation, Feature extraction, Accuracy, Wavelet transforms, Image edge detection, Semantics, Medical diagnostic imaging, Image color analysis, Blood cells, convolution neural networks, cell segmentation, isotropic undecimated wavelet transform
Kaynak
IEEE Access
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
12
