zbMATH — the first resource for mathematics

Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network. (English) Zbl 1459.92054
Summary: Given the explosive growth of the amount of medical image data being produced and transferred over networks every day, employing lossy compression and other irreversible image operations is inevitable. As expected, irreversible image coding may decrease image fidelity by introducing undesired artifacts, which may lead to an invalid diagnosis. The purpose of this study is to propose a no-reference model of assessing the quality of a degraded medical image resulting from irreversible coding, based on pattern recognition with the use of a convolutional neural network (CNN). This deep neural network consists of six convolutional layers followed by two fully connected ones for the final image classification. Such network geometry is a common choice for image classification problems nowadays. We aim to construct a model that is specialized for medical images and could serve as a predictor of image quality for algorithm performance analysis. This technique uses a CNN to classify shapes of randomly chosen grayscale intensities. The shapes and grayscale shadings were chosen with the intention to mimic structures and edges appearing in a medical image. Using the accuracy of a classifier, we attempt to quantitatively measure how the information content in an image deteriorates after applying irreversible operations and how this loss of information affects the ability/inability of the neural network to recognize the shapes. The technique may be used to study the performance of irreversible image coding techniques. Two irreversible operations are employed for image degradation: compression and interpolation. We show the difference of image quality resulting from JPEG and JPEG2000 compression algorithms followed by scaling using several interpolation techniques. The main result of this work is the development of a model to quantitatively measure image quality based on pattern recognition using a deep neural network. The presented model of quantitative assessment of medical image quality may be helpful in determining the thresholds for irreversible image post-processing algorithms parameters (i.e. quality factor in JPEG) in order to avoid misdiagnosis. Further investigation of this problem will involve a connection of the introduced method with specific pathologies and various medical image modalities.
92C55 Biomedical imaging and signal processing
Full Text: DOI
[1] Detyna, J.; Jelen, L.; Jelen, M., Role of image processing in the cancer diagnosis., Bio Algorithms Med Syst, 7, 4, 5-9 (2011)
[2] Jelen, L.; Lipiński, A.; Detyna, J.; Jelen, M., Bio-Algorithms and Med-Systems, Journal Edited by Jagiellonian University Medical College, 7, 2, 47-53 (2011)
[3] Abdar, M.; Ksiazek, W.; Acharya, U. R.; Tan, R.-S.; Makarenkov, V.; Plawiak, P., A new machine learning technique for an accurate diagnosis of coronary artery disease, Comput Methods Prog Biomed, 179, 104992 (2019)
[4] Pławiak, P., Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals, Swarm Evol Comput, 39, 192-208 (2018)
[5] Pławiak, P.; Acharya, U. R., Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals, Neural Comput Appl, 32, 15, 11137-11161 (2020)
[6] Tuncer, T.; Ertam, F.; Dogan, S.; Aydemir, E.; Pławiak, P., Ensemble residual network-based gender and activity recognition method with signals, J Supercomput, 76, 3, 2119-2138 (2020)
[7] Hammad, M.; Pławiak, P.; Wang, K.; Acharya, U. R., Resnet-attention model for human authentication using ECG signals, Expert Syst, e12547 (2020)
[8] Pławiak, P.; Abdar, M.; Acharya, U. R., Application of new deep genetic cascade ensemble of SVM classifiers to predict the australian credit scoring, Appl Soft Comput, 84, 105740 (2019)
[9] Pławiak, P.; Abdar, M.; Pławiak, J.; Makarenkov, V.; Acharya, U. R., Dghnl: a new deep genetic hierarchical network of learners for prediction of credit scoring, Inf Sci, 516, 401-418 (2020)
[10] Cosman, P. C.; Gray, R. M.; Olshen, R. A., Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy, Proc IEEE, 82, 6, 919-932 (1994)
[11] European Society of Radiology, Usability of irreversible image compression in radiological imaging, Insights Imaging, 2, 2, 103-115 (2011)
[12] Gao, X.; Lu, W.; Tao, D.; Liu, W., Image quality assessment and human visual system, Proc SPIE - Int SocOpt Eng, 7744 (2010)
[13] 978-3
[14] Marmolin, H., Subjective MSE measures, IEEE Trans Syst Man Cybern, 16, 3, 486-489 (1986)
[15] Wang, Z.; Bovik, A. C.; Sheikh, H. R.; Simoncelli, E. P., Image quality assessment: from error visibility to structural similarity, IEEE Trans Image Process, 13, 4, 600-612 (2004)
[16] Wang, Z.; Li, Q., Information content weighting for perceptual image quality assessment, IEEE Trans Image Process, 20, 5, 1185-1198 (2011) · Zbl 1372.94266
[17] Kowalik-Urbaniak, I. A.; Brunet, D.; Wang, J.; Vrscay, E.; Z. Wang; Koff, D., The quest for ‘diagnostically lossless’ medical image compression: a comparative study of objective quality metrics for compressed medical images, Medical imaging 2014: image perception, observer performance, and technology assessment, 9037 (2014)
[18] George, A.; Livingston, S. J., A survey on full reference image quality assessment algorithms, IJRET, 2, 12, 303-307 (2013)
[19] Lehmann, T. M.; Gonner, C.; Spitzer, K., Survey: interpolation methods in medical image processing, IEEE Trans Med Imaging, 18, 11, 1049-1075 (1999)
[20] Meijering, E. H.; Niessen, W. J.; Viergever, M. A., Quantitative evaluation of convolution-based methods for medical image interpolation, Med Image Anal, 5, 2, 111-126 (2001)
[21] Naït-Ali, A.; Cavaro-Ménard, C., Compression of biomedical images and signals (2008), Wiley Online Library
[22] Chow, L. S.; Paramesran, R., Review of medical image quality assessment, Biomed Signal Process Control, 27, 145-154 (2016)
[23] Shahid, M.; Rossholm, A.; Lövström, B.; Zepernick, H.-J., No-reference image and video quality assessment: a classification and review of recent approaches, EURASIP J Image Video Process, 2014, 1, 40 (2014)
[24] Ferzli, R.; Karam, L. J., A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB), IEEE Trans Image Process, 18, 4, 717-728 (2009) · Zbl 1371.94127
[25] Narvekar, N. D.; Karam, L. J., A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection, 2009 International workshop on quality of multimedia experience, 87-91 (2009), IEEE
[26] Varadarajan, S.; Karam, L. J., An improved perception-based no-reference objective image sharpness metric using iterative edge refinement, 2008 15th IEEE international conference on image processing, 401-404 (2008), IEEE
[27] Sadaka, N. G.; Karam, L. J.; Ferzli, R.; Abousleman, G. P., A no-reference perceptual image sharpness metric based on saliency-weighted foveal pooling, 2008 15th IEEE international conference on image processing, 369-372 (2008), IEEE
[28] Chen, J.; Zhang, Y.; Liang, L.; Ma, S.; Wang, R.; Gao, W., A no-reference blocking artifacts metric using selective gradient and plainness measures, Pacific-rim conference on multimedia, 894-897 (2008), Springer
[29] Ye, P.; Doermann, D., No-reference image quality assessment using visual codebooks, IEEE Trans Image Process, 21, 7, 3129-3138 (2012) · Zbl 1373.94464
[30] Oszust, M.; Piórkowski, A.; Obuchowicz, R., No-reference image quality assessment of magnetic resonance images with high-boost filtering and local features, Magn Reson Med, 1648-1660 (2020)
[31] Oszust, M., No-reference image quality assessment with local gradient orientations, Symmetry, 11, 1, 95 (2019)
[32] Cheng J.Y., Chen F., Alley M.T., Pauly J.M., Vasanawala S.S.. Highly scalable image reconstruction using deep neural networks with bandpass filtering. arXiv:180503300 2018.
[33] Talebi, H.; Milanfar, P., Nima: Neural image assessment, IEEE Trans Image Process, 27, 8, 3998-4011 (2018) · Zbl 1409.94573
[34] Mardani, M.; Gong, E.; Cheng, J. Y.; Vasanawala, S. S.; Zaharchuk, G.; Xing, L., Deep generative adversarial neural networks for compressive sensing MRI, IEEE Trans Med Imaging, 38, 1, 167-179 (2018)
[35] Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Fei-Fei, L., Imagenet: a large-scale hierarchical image database, 2009 IEEE conference on computer vision and pattern recognition, 248-255 (2009), IEEE
[36] Ma J., Nakarmi U., Kin C.Y.S., Sandino C., Cheng J.Y., Syed A.B., et al. Diagnostic image quality assessment and classification in medical imaging: opportunities and challenges. 2019. arXiv:1912.02907.
[37] Erickson, B. J., Irreversible compression of medical images, J Digit Imaging, 15, 1, 5-14 (2002)
[38] Persons, K.; Palisson, P.; Manduca, A.; Erickson, B. J.; Savcenko, V., An analytical look at the effects of compression on medical images, J Digit Imaging, 10, 1, 60-66 (1997)
[39] Meijering, E. H.W., Spline interpolation in medical imaging: comparison with other convolution-based approaches, European signal processing conference (EUSIPCO), 4, 1989-1996 (2000)
[40] Thévenaz, P.; Blu, T.; Unser, M., Interpolation revisited [medical images application], IEEE Trans Med Imaging, 19, 7, 739-758 (2000)
[41] Clark A., et al. Pillow. URL https://pillowreadthedocsio/en/stable (Visited on 7/24/20) 2010.
[42] LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W., Backpropagation applied to handwritten zip code recognition, Neural Comput, 1, 4, 541-551 (1989)
[43] Krizhevsky, A.; Sutskever, I.; Hinton, G. E., Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 1097-1105 (2012)
[44] Simonyan K., Zisserman A.. Very deep convolutional networks for large-scale image recognition. arXiv:14091556 2014.
[45] Zhao, Z.-Q.; Zheng, P.; Xu, S.-t.; Wu, X., Object detection with deep learning: a review, IEEE Trans Neural Netw Learn Syst, 30, 11, 3212-3232 (2019)
[46] Chollet, F., Keras: deep learning library for theano and tensorflow, 7, 8, T1 (2015)
[47] Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J., Tensorflow: a system for large-scale machine learning, 12th USENIX symposium on operating systems design and implementation (OSDI 16), 265-283 (2016)
[48] Kavukcuoglu, K.; Ranzato, M.; Fergus, R.; LeCun, Y., Learning invariant features through topographic filter maps, 2009 IEEE conference on computer vision and pattern recognition, 1605-1612 (2009), IEEE
[49] Nair, V.; Hinton, G. E., Rectified linear units improve restricted Boltzmann machines, ICML (2010)
[50] Ramachandran P., Zoph B., Le Q.V. Searching for activation functions. arXiv:171005941 2017.
[51] Kingma D.P., Ba J.. Adam: a method for stochastic optimization. arXiv:14126980 2014.
[52] Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y., Deep learning, 1 (2016), MIT Press Cambridge · Zbl 1373.68009
[53] Bisong, E., Google colaboratory, Building machine learning and deep learning models on google cloud platform, 59-64 (2019), Springer
[54] Strintzis, M. G., A review of compression methods for medical images in PACS, Int J Med Inform, 52, 1-3, 159-165 (1998)
[55] Koff, D.; Bak, P.; Brownrigg, P.; Hosseinzadeh, D.; Khademi, A.; Kiss, A., Pan-canadian evaluation of irreversible compression ratios for development of national guidelines, J Digit Imaging, 22, 6, 569 (2009)
[56] Kowalik-Urbaniak, I.; Vrscay, E. R.; Wang, Z.; Cavaro-Menard, C.; Koff, D.; Wallace, B., The impact of skull bone intensity on the quality of compressed ct neuro images, Medical imaging 2012: advanced PACS-based imaging informatics and therapeutic applications, 8319, 83190L (2012), International Society for Optics and Photonics
[57] Kowalik-Urbaniak, I.; Brunet, D.; Wang, J.; Koff, D.; Smolarski-Koff, N.; Vrscay, E. R., The quest for ‘diagnostically lossless’ medical image compression: a comparative study of objective quality metrics for compressed medical images, Medical imaging 2014: image perception, observer performance, and technology assessment, 9037, 903717 (2014), International Society for Optics and Photonics
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.