1 publications with the keyword "histogram equalization"

Automated curvilinear image segmentation is a crucial step to characterize and quantify the morphology of blood vessels across scale. We propose a dual pipeline RF_OFB+U-NET that fuses U-Net deep learning features with a low level image feature filter bank using the random forests classifier for vessel segmentation. We modify the U-Net CNN architecture to provide a foreground vessel regression likelihood map that is used to segment both arteriole and venule blood vessels in mice dura mater tissues. The hybrid approach combining both hand-crafted and learned features was tested on 60 epifluores-cence microscopy images and improved the segmentation of thin vessel structures by nearly 5% using the Dice similarity coefficient compared to U-Net.

@inproceedings{2019a,
  author = "Y. M. Kassim and O. V. Glinskii and V. V. Glinsky and V. H. Huxley and G. Guidoboni and K. Palaniappan",
  title = "Deep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation",
  year = 2019,
  journal = "2019 IEEE International Conference on Image Processing (ICIP)",
  publisher = "IEEE",
  pages = "1445-1449",
  month = "Aug",
  keywords = "semantic vessel segmentation, deep learning, histogram equalization, random forests, u-net",
  doi = "10.1109/ICIP.2019.8803084",
  url = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C26&q=Deep+U-Net+Regression+and+Hand-Crafted+Feature+Fusion+for+Accurate+Blood+Vessel+Segmentation&btnG="
}

Y. M. Kassim, O. V. Glinskii, V. V. Glinsky, V. H. Huxley, G. Guidoboni, and K. Palaniappan. Deep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation. 2019 IEEE International Conference on Image Processing (ICIP), IEEE, pages 1445-1449, August 2019.