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Chaotic quantization based JPEG for effective compression of whole slide images

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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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References

  1. 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)

    Google Scholar 

  2. Hosseini, M., Pratas, D., Pinho, A.: A survey on data compression methods for biological sequences. Information 7(4), 56 (2016)

    Google Scholar 

  3. 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

    Article  Google Scholar 

  4. Salomon, D.: A Concise Introduction to Data Compression, p. 9781848000728. Springer, Berlin (2008)

    Google Scholar 

  5. 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

    Article  MathSciNet  Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. Srinidhi, C.L., et al.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Google Scholar 

  19. Arestaa, G., et al.: BACH: grand challenge on breast cancer histology images. Med. Image Anal. 56, 122–139 (2019)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. Veta, M., et al.: Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge. Med. Image Anal. 54, 111–121 (2019)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. Rijthoven, M., et al.: HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Med. Image Anal. 68, 101890 (2021)

    Google Scholar 

  24. Dov, D., et al.: Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images. Med. Image Anal. 67, 101814 (2021)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. 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)

    Google Scholar 

  27. Wang, S., et al.: RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification. Med. Image Anal. 58, 101549 (2019)

    Google Scholar 

  28. Xiang, Y., et al.: Autofocus of whole slide imaging based on convolution and recurrent neural networks. Ultramicroscopy 220, 113146 (2021)

    Google Scholar 

  29. Madabhushi, A., George, L.: Image analysis and machine learning in digital pathology: Challenges and opportunities. Med. Image Anal. 33, 170–175 (2016)

    Google Scholar 

  30. 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)

    Google Scholar 

  31. 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)

    Google Scholar 

  32. Rodrigues, V.F., et al.: Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine. Comput. Methods Programs Biomed. 191, 105403 (2020)

    Google Scholar 

  33. Kalra, S., et al.: Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images. Med. Image Anal. 65, 101757 (2020)

    Google Scholar 

  34. Kalinski, T., et al.: Lossy compression in diagnostic virtual 3-dimensional microscopy—where is the limit? Hum. Pathol. 40, 998–1005 (2009)

    Google Scholar 

  35. Sharma, A., et al.: Balancing image quality and compression factor for special stains whole slide images. Analyt. Cellular Pathol. 35(2), 101–106 (2012)

    Google Scholar 

  36. Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imag. 35(8), 1962–1971 (2016)

    Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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)

    Google Scholar 

  39. 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)

    Google Scholar 

  40. 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)

  41. Taubman, D.S., Marcellin, M.W.: JPEG2000 Image Compression Fundamentals, Standards and Practice. Kluwer, Norwell, MA, USA (2001)

    Google Scholar 

  42. Kalinski, T., et al.: Virtual 3D microscopy using multiplane whole slide images in diagnostic pathology. Amer. J. Clin. Pathol. 130(2), 259–264 (2008)

    Google Scholar 

  43. 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)

  44. 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)

    Google Scholar 

  45. Wallace, G.: The JPEG still picture compression standard. IEEE Trans Consum Electron 20(38), 18–34 (1992)

    Google Scholar 

  46. 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)

    Google Scholar 

  47. Farahani, N., et al.: Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Laborat. Med. Int. 7, 23 (2015)

    Google Scholar 

  48. 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)

  49. 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)

  50. 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.

  51. 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)

    Google Scholar 

  52. Helin, H., et al.: Optimized JPEG 2000 compression for efficient storage of histopathological whole-slide images. J. Pathol. Inform. 1, 20 (2018)

    Google Scholar 

  53. Bug, D., et al.: Scalable HEVC for Histological Whole-Slide Image Compression. In: Bildverarbeitung Für Die Medizin, pp. 315–321. Springer Fachmedian, Wiesbaden (2020)

    Google Scholar 

  54. 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)

  55. Zarella, M.D., Jakubowski, J.: Video compression to support the expansion of whole-slide imaging into cytology. J. Med. Imag. 6(4), 047502 (2019)

    Google Scholar 

  56. 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)

    Google Scholar 

  57. 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)

  58. 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)

    Google Scholar 

  59. 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)

    Google Scholar 

  60. Sullivan, G., Wiegand, T.: Rate-distortion optimization for video compression. IEEE Signal Process. Mag. 15(6), 74–90 (1998)

    Google Scholar 

  61. Ramchandran, K., Vetterli, M.: Rate-distortion optimal fast thresholding with complete JPEG/MPEG decoder compatibility. IEEE Trans. Image Proc. 3, 700–704 (1994)

    Google Scholar 

  62. 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)

    Google Scholar 

  63. 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)

  64. 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)

  65. 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)

  66. 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)

    Google Scholar 

  67. Strogatz, S.: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Studies in Nonlinearity). Westview Press: Boulder CO, Boulder (2001)

    Google Scholar 

  68. Özer, A.B.: CIDE: chaotically initialized differential evolution. Expert Syst. Appl. 37(6), 4632–4641 (2010)

    Google Scholar 

  69. 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

    Article  Google Scholar 

  70. Artuğer, F., Özkaynak, F.: A novel method for performance improvement of chaos-based substitution boxes. Symmetry 12, 571 (2020)

    Google Scholar 

  71. 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

    Article  Google Scholar 

  72. Webpage1: https://portal.gdc.cancer.gov/.

  73. Webpage2: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/.

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Acknowledgements

Fatih Özkaynak was supported in part by the Scientific and Technological Research Council of Turkey under Grant 122E337.

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  1. Department of Computer Engineering, Munzur University, 62200, Tunceli, Turkey

    Fırat Artuğer

  2. Department of Software Engineering, Fırat University, 23119, Elazig, Turkey

    Fatih Özkaynak

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  1. Fırat Artuğer
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  2. Fatih Özkaynak
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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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Correspondence to Fırat Artuğer.

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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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