A Blind Noise Estimation and Removal in Histopathological Images

Shiksha Singh, Rajesh Kumar

2021

Abstract

With the advancement in technology for digital pathology, a huge chunk of the visual dataset is prepared for medical experts for disease diagnosis and grading. The introduction of noise in various image modalities in the medical field can distress the result of diagnosis which could lead to inappropriate disease grading and hence delay in treatment. In this, a blind noise estimation and removal technique is proposed for histopathology images. The model uses the wavelet transformed image and block selection approach with a block size of eight for noise estimation. The noise estimated in the model is Gaussian, Poisson, and speckle. The proposed approach is verified on images of Break his dataset with all four-magnification scale. The performance of the proposed approach is shown through parameter Signal-to-noise ratio (SNR), mean square error (MSE), root mean square error (RMSE) and peak signal to noise ratio (PSNR)

Paper Citation

in Harvard Style

Singh S. and Kumar R. (2021). A Blind Noise Estimation and Removal in Histopathological Images. In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE, ISBN 978-989-758-544-9, pages 67-72. DOI: 10.5220/0010562700003161

in Bibtex Style

@conference{icacse21,
author={Shiksha Singh and Rajesh Kumar},
title={A Blind Noise Estimation and Removal in Histopathological Images},
booktitle={Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,},
year={2021},
pages={67-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010562700003161},
isbn={978-989-758-544-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,
TI - A Blind Noise Estimation and Removal in Histopathological Images
SN - 978-989-758-544-9
AU - Singh S.
AU - Kumar R.
PY - 2021
SP - 67
EP - 72
DO - 10.5220/0010562700003161