Abstract
In today’s digital world, effectively transferring data from one point to another is an important problem. For this reason, the development of various new compression algorithms and making existing solutions more effective are examined in detail by researchers. In this study, a new method is proposed to improve the performance of JPEG algorithm. The proposed method includes an approach based on chaotic systems. Chaotic systems contain a strong entropy source. This powerful source of entropy has strong practical applications in obtaining statistically robust random values. In this study, a method is proposed to obtain quantization tables effectively by taking advantage of this potential of chaotic systems. The obtained results showed that the compression performance can be increased at the same quality factors. The proposed approach has been tested on the whole slide image (WSI) dataset. Looking at the analysis results, an average of 2.43% higher accuracy was achieved compared to the JPEG algorithm. It is thought that these results can provide an advantage especially in transferring high-dimensional images such as the DICOM standard, where the JPEG algorithm is used in practical applications.
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Ginsburg, S.B., Lee, G., Ali, S., Madabhushi, A.: Feature importance in nonlinear embeddings (FINE): Applications in digital pathology. IEEE Trans. Med. Imag. 35(1), 76–88 (2016)
Hosseini, M., Pratas, D., Pinho, A.: A survey on data compression methods for biological sequences. Information 7(4), 56 (2016)
Ben-Gal, I.I.: On the use of data compression measures to analyze robust designs. IEEE Trans. Reliab. 54(3), 381–388 (2008). https://doi.org/10.1109/TR.2005.853280
Salomon, D.: A Concise Introduction to Data Compression, p. 9781848000728. Springer, Berlin (2008)
Unser, M., Blu, T.: Mathematical properties of the JPEG2000 wavelet filters. IEEE Trans. Image Process. 12(9), 1080–1090 (2003). https://doi.org/10.1109/TIP.2003.812329
Jeong, G.M., et al.: JPEG Quantization Table Design for Photos with Face in Wireless Handset. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) advances in multimedia information processing - PCM 2004 lecture notes in computer science, pp. 681–688. Springer, Berlin, Heidelberg (2005)
Pantanowitz, L.: Twenty years of digital pathology: an overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J. Pathol. Inform. 9(1), 40 (2018)
Ferreira, R., Moon, J., Humphries, J., Sussman, A., Saltz, J., Miller, R., Demarzo, A.: The virtual microscope. Rom. J. Morphol. Embryol. 45, 449–453 (1997)
Mukhopadhyay, S., Feldman, M., Abels, E.: Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter randomized blinded noninferiority study of 1992 cases (pivotal study). Am. J. Surg. Pathol. 42(1), 39–52 (2017). https://doi.org/10.1097/PAS.0000000000000948
Holzinger, A., Goebel, R., Mengel, M., Mueller, H. (eds.): Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges. Springer, Cham (2020)
Hamilton, P.W., Wang, Y., McCullough, S.J.: Virtual microscopy and digital pathology in training and education. APMIS 120(4), 305–315 (2012). https://doi.org/10.1111/j.1600-0463.2011.02869.x
Rewa, R.S., et al.: 3D registration of pre-surgical prostate mri and histopathology images via super-resolution volume reconstruction. Med. Image Anal. 69, 101957 (2021). https://doi.org/10.1016/j.media.2021.101957
Srinidhi, C.L., et al.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)
Wang, C.W., Huang, S.C., Lee, Y.C., Shen, Y.J., Meng, S.I., Gaol, J.L.: Deep learning for bone marrow cell detection and classification on whole-slide images. Med. Image Anal. 75, 102270 (2022)
Zheng, Y., Jiang, Z., Shi, J., Xie, F., Zhang, H., Luo, W., Xue, C.: Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval. Med. Image Anal. 76, 102308 (2022)
Tsuneki, M., Abe, M., Kanavati, F.: A deep learning model for prostate adenocarcinoma classification in needle biopsy whole-slide images using transfer learning. Diagnostics 12(3), 768 (2022)
Liu, T., Su, R., Sun, C., Li, X., Wei, L.: EOCSA: predicting prognosis of Epithelial ovarian cancer with whole slide histopathological images. Expert Syst. Appl. 206, 117643 (2022)
Bhatt, A.R., Ganatra, A., Kotecha, K.: Cervical cancer detection in pap smear whole slide images using convnet with transfer learning and progressive resizing. PeerJ Comput. Sci. 7, e348 (2021)
Arestaa, G., et al.: BACH: grand challenge on breast cancer histology images. Med. Image Anal. 56, 122–139 (2019)
Barker, J., et al.: Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles. Med. Image Anal. 30, 60–71 (2016)
Veta, M., et al.: Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Med. Image Anal. 54, 111–121 (2019)
Wang, X., et al.: A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images. Med. Image Anal. 68, 101914 (2021)
Rijthoven, M., et al.: HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Med. Image Anal. 68, 101890 (2021)
Dov, D., et al.: Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images. Med. Image Anal. 67, 101814 (2021)
Kong, J., et al.: Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation. Pattern Recogn. 42, 1080–1092 (2009)
Hacking, S., et al.: Whole slide imaging and colorectal carcinoma: A validation study for tumor budding and stromal differentiation. Pathol. Res. Pract. 216, 153233 (2020)
Wang, S., et al.: RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification. Med. Image Anal. 58, 101549 (2019)
Xiang, Y., et al.: Autofocus of whole slide imaging based on convolution and recurrent neural networks. Ultramicroscopy 220, 113146 (2021)
Madabhushi, A., George, L.: Image analysis and machine learning in digital pathology: Challenges and opportunities. Med. Image Anal. 33, 170–175 (2016)
Li, B., et al.: Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization. Med. Image Anal. 68, 101938 (2021)
Niazi, M.K.K., et al.: Pathological image compression for big data image analysis: application to hotspot detection in breast cancer. Arti!cial Intell. Med. 95, 82–87 (2019)
Rodrigues, V.F., et al.: Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine. Comput. Methods Programs Biomed. 191, 105403 (2020)
Kalra, S., et al.: Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images. Med. Image Anal. 65, 101757 (2020)
Kalinski, T., et al.: Lossy compression in diagnostic virtual 3-dimensional microscopy—where is the limit? Hum. Pathol. 40, 998–1005 (2009)
Sharma, A., et al.: Balancing image quality and compression factor for special stains whole slide images. Analyt. Cellular Pathol. 35(2), 101–106 (2012)
Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imag. 35(8), 1962–1971 (2016)
Snead, D.R.J., et al.: Validation of digital pathology imaging for primary histopathological diagnosis. Histopathology 68(7), 1063–1072 (2016). https://doi.org/10.1111/his.12879
Gonzalez-Conejero, J., et al.: JPEG2000 encoding of remote sensing multispectral images with nodata regions. IEEE Geosci. Remote Sens. Lett. 7(2), 251–255 (2010)
Cagnazzo, M., et al.: Cost and advantage of object-based image coding with shape-adaptive wavelet transform. J. Image Video Process. 2007(1), 19 (2007)
Y. Dong et al: An Interactive Tool for ROI Extraction and Compression on Whole Slide Images. In: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). 224–227 (2016)
Taubman, D.S., Marcellin, M.W.: JPEG2000 Image Compression Fundamentals, Standards and Practice. Kluwer, Norwell, MA, USA (2001)
Kalinski, T., et al.: Virtual 3D microscopy using multiplane whole slide images in diagnostic pathology. Amer. J. Clin. Pathol. 130(2), 259–264 (2008)
Doyle S., et al.: Evaluation of effects of JPEG2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology. In: Proc. IEEE Int. Symp. Biomed. Imag. Nano Macro (ISBI), pp. 1313–1316 (2010)
Johnson, J.P., et al.: Using a visual discrimination model for the detection of compression artifacts in virtual pathology images. IEEE Trans. Med. Imag. 30(2), 306–314 (2011)
Wallace, G.: The JPEG still picture compression standard. IEEE Trans Consum Electron 20(38), 18–34 (1992)
Barisoni, L., et al.: Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images. Mod. Pathol. 29(7), 671–684 (2016)
Farahani, N., et al.: Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Laborat. Med. Int. 7, 23 (2015)
Hernández-CabroneroM., et al.: Fast MCT optimization for the compression of whole-slide images. In Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 2370–2374 (2016)
Zhang L., et al.: Adaptive color-space transform for HEVC screen content coding. In: Proc. Data Compress. Conf. (DCC) Snowbird, UT, USA, pp. 233–242, (2015)
Niazi M.K.K., et al.: Computer assisted bladder cancer grading: Shapes for color space decomposition. Proc SPIE. 9791: 9791071–9791078 (2016). doi: https://doi.org/10.1117/12.2216967.
Cabronero, M.H., et al.: Mosaic-based color-transform optimization for lossy and lossy-to-lossless compression of pathology whole-slide images. IEEE Trans. Med. Imag. 38(1), 21–32 (2019)
Helin, H., et al.: Optimized JPEG 2000 compression for efficient storage of histopathological whole-slide images. J. Pathol. Inform. 1, 20 (2018)
Bug, D., et al.: Scalable HEVC for Histological Whole-Slide Image Compression. In: Bildverarbeitung Für Die Medizin, pp. 315–321. Springer Fachmedian, Wiesbaden (2020)
Sanchez V., Aulí-Llinàs, F., Vanam, R., Bartrina-Rapesta J.: Rate control for lossless region of interest coding in HEVC intra-coding with applications to digital pathology images. In: 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). pp. 1250–1254 (2015)
Zarella, M.D., Jakubowski, J.: Video compression to support the expansion of whole-slide imaging into cytology. J. Med. Imag. 6(4), 047502 (2019)
Aswolinskiy, W., Tellez, D., Raya, G., van der Woude, L., Looijen-Salamon, M., van der Laak, J., Ciompi, F.: Neural image compression for non-small cell lung cancer subtype classification in H & E stained whole-slide images. Med. Imag. 2021 Digit. Pathol. 11603, 1160304 (2021)
Jiang, Y., Liu, F., Cui, R., Zhang, X., & Zhang, X.: Pathology image compression based on jpeg2000, multi-resolutional human perception and the region of interest predictions. In 2022 data compression conference (DCC). pp. 458–458 (2022)
Kim, H., Yoon, H., Thakur, N., Hwang, G., Lee, E.J., Kim, C., Chong, Y.: Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci. Rep. 11(1), 1–14 (2021)
Yao, H., Wei, H., Qiao, T., Qin, C.: JPEG quantization step estimation with coefficient histogram and spectrum analyses. J. Vis. Commun. Image Represent. 69, 102795 (2020)
Sullivan, G., Wiegand, T.: Rate-distortion optimization for video compression. IEEE Signal Process. Mag. 15(6), 74–90 (1998)
Ramchandran, K., Vetterli, M.: Rate-distortion optimal fast thresholding with complete JPEG/MPEG decoder compatibility. IEEE Trans. Image Proc. 3, 700–704 (1994)
Peterson, H.A., et al.: Quantization of color image components in the DCT domain, human vision, visual processing, and digital display. Proc. SPIE. 1453, 210–222 (1991)
PetersonH.A.: DCT basis function visibility in RGB space, in Society for Information Display Digest of Technical Papers. In: J. Morreale, ed. Society for Information Display, Playa del Rey, CA. (1992)
WatsonA.B.: DCTune: A technique for visual optimization of DCT quantization matrices for individual images. Society for Information Display Digest of Technical Papers XXIV. pp 946–949 (1993)
Yuebing, J.: JPEG image compression using quantization table optimization based on perceptual image quality assessment. In: IEEE Signals, Systems and Computers Conference, New Jersey, NJ, USA, pp. 225–229, (2011)
Kumar, B.V., Karpagam, G.R.: Differential evolution versus genetic algorithm in optimising the quantisation table for JPEG baseline algorithm. Int. J. Adv. Intell. Parad. 7(2), 111–135 (2015)
Strogatz, S.: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Studies in Nonlinearity). Westview Press: Boulder CO, Boulder (2001)
Özer, A.B.: CIDE: chaotically initialized differential evolution. Expert Syst. Appl. 37(6), 4632–4641 (2010)
Muhammad, Z.M.Z., Özkaynak, F.: An image encryption algorithm based on chaotic selection of robust cryptographic primitives. IEEE Access 8, 56581–56589 (2020). https://doi.org/10.1109/ACCESS.2020.2982827
Artuğer, F., Özkaynak, F.: A novel method for performance improvement of chaos-based substitution boxes. Symmetry 12, 571 (2020)
Açikkapi, M.Ş, Özkaynak, F.: A method to determine the most suitable initial conditions of chaotic map in statistical randomness applications. IEEE Access 9, 1482–1494 (2021). https://doi.org/10.1109/ACCESS.2020.3046470
Webpage1: https://portal.gdc.cancer.gov/.
Webpage2: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/.
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Fatih Özkaynak was supported in part by the Scientific and Technological Research Council of Turkey under Grant 122E337.
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Fırat Artuğer: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software,
Fatih Özkaynak: Supervision Visualization, original draft, Writing—review & editing Funding acquisition, Project administration, Resources, Writing—review & editing.
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Artuğer, F., Özkaynak, F. Chaotic quantization based JPEG for effective compression of whole slide images. Vis Comput 39, 5609–5623 (2023). https://doi.org/10.1007/s00371-022-02684-y
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DOI: https://doi.org/10.1007/s00371-022-02684-y