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HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter.

Qingjiao SunHuiyan JiangGanzheng ZhuSiqi LiShang GongBenqiang YangLibo Zhang
Published in: BioMed research international (2016)
Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.
Keyphrases
  • deep learning
  • high frequency
  • convolutional neural network
  • machine learning
  • transcranial magnetic stimulation
  • air pollution