Abstract
In medical diagnostic analysis, pathological microscopic image is often regarded as a gold standard, and hence the study of pathological microscopic image is of great necessity. High quality microscopic pathological images enable doctors to arrive at correct diagnosis. The pathological microscopic image is an important cornerstone for modernization and computerization of medical procedures. The quality of pathological microscopic images may be degraded due to a variety of reasons. It is difficult to acquire key information, so research for quality assessment of pathological microscopic image is quite necessary. In this paper, we perform a study on subjective quality assessment of pathological microscopic images and investigate whether the existing objective quality measures can be applied to the pathological microscopic images. Concretely, we establish a new pathological microscopic image quality database (PMIQD) which includes 425 pathological microscopic images with different quality degrees. The mean opinion scores rated by nonexperts and experts are calculated afterwards. Besides, we investigate the prediction performance of the existing popular image quality assessment (IQA) algorithms on PMIQD, including 8 no-reference (NR) methods. Experimental results demonstrate that the present objective models do not work well. IQA for pathological microscopic image needs to be developed for predicting the quality rated by nonexperts and experts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Snead, D.R.J., et al.: Validation of digital pathology imaging for primary histopathological diagnosis. Histopathology 68(7), 1063–1072 (2016)
Sheikh, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Zhang, L., Shen, Y., Li, H.: VSI: a visual saliency-induced index for perceptual image quality assessment. IEEE Trans. Image Process. 23(10), 4270–4281 (2014)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
Sheikh, H.R.: LIVE image quality assessment database release 2 (2005). http://live.ece.utexas.edu/research/quality
Larson, E.C., Chandler, D.M.: Categorical image quality (CSIQ) database (2010)
Ponomarenko, N., et al.: Image database TID2013: peculiarities, results and perspectives. Sig. Process. Image Commun. 30, 57–77 (2015)
Recommendation ITU-R BT.500-13: Methodology for the subjective assessment of the quality of television pictures. Technical report, International Telecommunication Union (2012)
Zhu, W., Zhai, G., Menghan, H., Liu, J., Yang, X.: Arrow’s impossibility theorem inspired subjective image quality assessment approach. Sig. Process. 145, 193–201 (2018)
Karbowski, M., Youle, R.J.: Dynamics of mitochondrial morphology in healthy cells and during apoptosis. Cell Death Differ. 10(8), 870 (2003)
Shrestha, P., Kneepkens, R., Vrijnsen, J., Vossen, D., Abels, E., Hulsken, B.: A quantitative approach to evaluate image quality of whole slide imaging scanners. J. Pathol. Inform. 7, 56 (2016)
Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., Kot, A.C.: No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Cybern. 46(1), 39–50 (2015)
Narvekar, N.D., Karam, L.J.: A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection. In: 2009 International Workshop on Quality of Multimedia Experience, pp. 87–91. IEEE (2009)
Vu, P.V., Chandler, D.M.: A fast wavelet-based algorithm for global and local image sharpness estimation. IEEE Sig. Process. Lett. 19(7), 423–426 (2012)
Vu, C.T., Phan, T.D., Chandler, D.M.: A spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Process. 21(3), 934–945 (2011)
Winkelman, K.-H.: Method and apparatus for the automatic analysis of density range, color cast, and gradation of image originals on the basis of image values transformed from a first color space into a second color space, 16 September 1997. US Patent 5,668,890 (1997)
P ITU-T Recommendation: Subjective video quality assessment methods for multimedia applications. International Telecommunication Union (1999)
Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3440–3451 (2006)
Mantiuk, R.K., Tomaszewska, A., Mantiuk, R.: Comparison of four subjective methods for image quality assessment. In: Computer Graphics Forum, vol. 31, pp. 2478–2491. Wiley Online Library (2012)
Kumar, B., Singh, S.P., Mohan, A., Anand, A.: Performance of quality metrics for compressed medical images through mean opinion score prediction. J. Med. Imaging Health Inform. 2(2), 188–194 (2012)
Streijl, R.C., Winkler, S., Hands, D.S.: Mean opinion score (MOS) revisited: methods and applications, limitations and alternatives. Multimed. Syst. 22(2), 213–227 (2016)
Pelgrom, M.J.M., Duinmaijer, A.C.J., Welbers, A.P.G.: Matching properties of MOS transistors. IEEE J. Solid-State Circ. 24(5), 1433–1439 (1989)
Sheikh, H.R., Bovik, A.C.: Image information and visual quality. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. iii–709. IEEE (2004)
Saad, M.A., Bovik, A.C., Charrier, C.: Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Trans. Image Process. 21(8), 3339–3352 (2012)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Gu, K., et al.: FISBLIM: a five-step blind metric for quality assessment of multiply distorted images. In: SiPS 2013 Proceedings, pp. 241–246. IEEE (2013)
Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20(3), 209–212 (2012)
Xue, W., Zhang, L., Mou, X.: Learning without human scores for blind image quality assessment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 995–1002 (2013)
Ke, G., Zhai, G., Yang, X., Zhang, W.: Hybrid no-reference quality metric for singly and multiply distorted images. IEEE Trans. Broadcast. 60(3), 555–567 (2014)
Moorthy, A.K., Bovik, A.C.: Visual importance pooling for image quality assessment. IEEE J. Sel. Top. Sig. Process. 3(2), 193–201 (2009)
Chen, M.-J., Su, C.-C., Kwon, D.-K., Cormack, L.K., Bovik, A.C.: Full-reference quality assessment of stereopairs accounting for rivalry. Sig. Process. Image Commun. 28(9), 1143–1155 (2013)
Gu, K., Zhai, G., Yang, X., Zhang, W., Liu, M.: Subjective and objective quality assessment for images with contrast change. In: 2013 IEEE International Conference on Image Processing, pp. 383–387. IEEE (2013)
Dhanachandra, N., Manglem, K., Chanu, Y.J.: Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015)
Ameisen, D., et al.: Automatic image quality assessment in digital pathology: from idea to implementation. In: IWBBIO, pp. 148–157 (2014)
Acknowledgment
This work is sponsored by the Shanghai Sailing Program (No. 19YF1414100), the National Natural Science Foundation of China (No. 61831015, No. 61901172), the STCSM (No. 18DZ2270700), and the China Postdoctoral Science Foundation funded project (No. 2016M600315).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xu, S., Hu, M., Yu, W., Feng, J., Li, Q. (2020). PMIQD 2019: A Pathological Microscopic Image Quality Database with Nonexpert and Expert Scores. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_25
Download citation
DOI: https://doi.org/10.1007/978-981-15-3341-9_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3340-2
Online ISBN: 978-981-15-3341-9
eBook Packages: Computer ScienceComputer Science (R0)