Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2012  |  Volume : 3  |  Issue : 1  |  Page : 9

Referenceless image quality evaluation for whole slide imaging


1 Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Kanagawa, Japan; Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
2 Department of Pathology, Massachusetts General Hospital, Boston, MA; Department of Pathology, Harvard Medical School, Boston, MA, USA
3 Global Scientific Information and Computing Center, Tokyo Institute of Technology, Tokyo, Japan
4 Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Kanagawa, Japan

Correspondence Address:
Noriaki Hashimoto
Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Kanagawa, Japan; Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.93891

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Objective: The image quality in whole slide imaging (WSI) is one of the most important issues for the practical use of WSI scanners. In this paper, we proposed an image quality evaluation method for scanned slide images in which no reference image is required. Methods: While most of the conventional methods for no-reference evaluation only deal with one image degradation at a time, the proposed method is capable of assessing both blur and noise by using an evaluation index which is calculated using the sharpness and noise information of the images in a given training data set by linear regression analysis. The linear regression coefficients can be determined in two ways depending on the purpose of the evaluation. For objective quality evaluation, the coefficients are determined using a reference image with mean square error as the objective value in the analysis. On the other hand, for subjective quality evaluation, the subjective scores given by human observers are used as the objective values in the analysis. The predictive linear regression models for the objective and subjective image quality evaluations, which were constructed using training images, were then used on test data wherein the calculated objective values are construed as the evaluation indices. Results: The results of our experiments confirmed the effectiveness of the proposed image quality evaluation method in both objective and subjective image quality measurements. Finally, we demonstrated the application of the proposed evaluation method to the WSI image quality assessment and automatic rescanning in the WSI scanner.


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