Abstract
The popularity of digital histopathology is growing rapidly in the development of computer aided disease diagnosis systems. However, the color variations due to manual cell sectioning and stain concentration make the process challenging in various digital pathological image analysis such as histopathological image segmentation and classification. Hence, the normalization of these variations are needed to obtain the promising results. The proposed research intends to introduce a reliable and robust new complete color normalization method, addressing the problems of color and stain variability. The new complete color normalization involves three phases, namely enhanced fuzzy illuminant normalization, fuzzy-based stain normalization, and modified spectral normalization. The extensive simulations are performed and validated on histopathological images. The presented algorithm outperforms the existing conventional normalization methods by overcoming the certain limitations and challenges. As per the experimental quality metrics and comparative analysis, the proposed algorithm performs efficiently and provides promising results.
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Petushi S, Garcia F U, Haber M M, Katsinis C, Tozeren A (2006) Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Med Imaging 6(1):14
Kayser G, Riede U, Werner M, Hufnagl P, Kayser K (2002) Towards an automated morphological classification of histological images of common lung carcinomas. Elec J Pathol Histol 8:022–03
Schmid K, Angerstein N, Geleff S, Gschwendtner A (2006) Quantitative nuclear texture features analysis confirms who classification 2004 for lung carcinomas. Modern Pathol 19(3):453–459
Greenberg S D (1984) Computer-assisted image analysis cytology. Karger, S Publishers
Yoo T S (2004) Insight into images: principles and practice for segmentation, registration, image analysis. AK Peters/CRC Press
Mittal H, Saraswat M (2019) An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm Evol Comput 45:15–32
Aswathy M, Jagannath M (2017) Detection of breast cancer on digital histopathology images: present status and future possibilities. Inform Med Unlocked 8:74–79
He L, Long L R, Antani S, Thoma G R (2012) Histology image analysis for carcinoma detection and grading. Comput Methods Progr Biomed 107(3):538–556
Mittal H, Saraswat M (2019) An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm Evol Comput 45:15–32
Wang Z, Bovik A C, Sheikh H R, Simoncelli E P (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Chandler D E, Roberson RW (2009) Bioimaging: current concepts in light and electron microscopy. Jones & Bartlett Publishers
Belsare A, Mushrif M (2012) Histopathological image analysis using image processing techniques: an overview. Signal Image Process 3(4):23
Gour M, Jain S, Sunil Kumar T (2020) Residual learning based cnn for breast cancer histopathological image classification. Int J Imaging Syst Technols
Karl J W, Maurer B A (2010) Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation. Landsc Ecol 25(4):591–605
Onder D, Zengin S, Sarioglu S (2014) A review on color normalization and color deconvolution methods in histopathology. Appl Immunohistochem Mol Morphol 22(10):713–719
Saraswat M, Arya K (2014) Automated microscopic image analysis for leukocytes identification: a survey. Micron 65:20–33
Saraswat M, Arya K (2013) Colour normalisation of histopathological images. Comput Methods Biomech Biomed Eng: Imaging Visual 1(4):185–197
Lakshmanan B, Anand S, Jenitha T (2019) Stain removal through color normalization of haematoxylin and eosin images: a review. J Phys: Conf Ser 1362(1):012108. IOP Publishing
Ruderman D L, Cronin T W, Chiao C -C (1998) Statistics of cone responses to natural images: implications for visual coding. JOSA A 15(8):2036–2045
Bejnordi B E, Litjens G, Timofeeva N, Otte-Höller I, Homeyer A, Karssemeijer N, van der Laak J A (2015) Stain specific standardization of whole-slide histopathological images. IEEE Trans Med Imaging 35(2):404–415
Vahadane A, Peng T, Albarqouni S, Baust M, Steiger K, Schlitter A M, Sethi A, Esposito I, Navab N (2015) Structure-preserved color normalization for histological images. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE, pp 1012–1015
Dhal K G, Ray S, Das S, Biswas A, Ghosh S (2019) Hue-preserving and gamut problem-free histopathology image enhancement. Iran J Sci Technol Trans Electr Eng 43(3):645–672
BenTaieb A, Hamarneh G (2017) Adversarial stain transfer for histopathology image analysis. IEEE Trans Med Imaging 37(3):792–802
Shaban MT, Baur C, Navab N, Albarqouni S (2019) Staingan: stain style transfer for digital histological images. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, pp 953–956
Zheng Y, Jiang Z, Zhang H, Xie F, Shi J, Xue C (2019) Adaptive color deconvolution for histological WSI normalization. Comput Methods Progr Biomed 170:107–120
Maji P, Mahapatra S (2019) Rough-fuzzy circular clustering for color normalization of histological images. Fundam Inform 164(1):103–117
Salvi M, Michielli N, Molinari F (2020) Stain color adaptive normalization (SCAN) algorithm: separation and standardization of histological stains in digital pathology. Comput Methods Progr Biomed 105506
Zanjani F G, Zinger S, Bejnordi B E, van der Laak J A, de With PH (2018) Stain normalization of histopathology images using generative adversarial networks. In: IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, p 2018
Gavrilovic M, Azar J C, Lindblad J, Wählby C, Bengtsson E, Busch C, Carlbom I B (2013) Blind color decomposition of histological images. IEEE Trans Med Imaging 32(6):983– 994
Gonzales R, Woods R, Eddins S (2002) Digital image processing. Prentice Hall, New Jersey
Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graph Appl 21(5):34–41
Ruifrok A C, Johnston D A, et al. (2001) Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 23(4):291–299
Khan A M, Rajpoot N, Treanor D, Magee D (2014) A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 61 (6):1729–1738
Macenko M, Niethammer M, Marron J S, Borland D, Woosley J T, Guan X, Schmitt C, Thomas NE (2009) A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 1107–1110
Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter A M, Esposito I, Navab N (2016) Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging 35 (8):1962– 1971
Bukenya F (2020) A hybrid approach for stain normalisation in digital histopathological images. Multimed Tools Appl 79(3):2339–2362
Maji P, Mahapatra S (2019) Circular clustering in fuzzy approximation spaces for color normalization of histological images. IEEE Trans Med Imaging 39(5):1735–1745
Li X, Plataniotis K N (2015) A complete color normalization approach to histopathology images using color cues computed from saturation-weighted statistics. IEEE Trans Biomed Eng 62(7):1862–1873
Athira M, Aswathy M, Rahman N (2016) A complete color normalization approach and classification of breast cancer cell 5(8):53–58
Roy S, Lal S, Kini J R (2019) Novel color normalization method for hematoxylin & eosin stained histopathology images. IEEE Access 7:28982–28998
Plataniotis K N, Venetsanopoulos A N (2013) Color image processing and applications. Springer Science & Business Media
Dubey Y K, Mushrif M M (2016) Fcm clustering algorithms for segmentation of brain mr images. Adv Fuzzy Syst 36(2):413– 426
Çetin M, Dokur Z, Ölmez T (2019) Fuzzy local information c-means algorithm for histopathological image segmentation. In: 2019 Scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT). IEEE, pp 1–6
Hanbury A (2003) Circular statistics applied to colour images. In: 8th Computer vision winter workshop, vol 91(1–2). Citeseer, pp 53–71
Tosta TA A, de Faria PR, Neves L A, do Nascimento M Z (2019) Color normalization of faded h&e-stained histological images using spectral matching. Comput Biol Med 111:103344
Monga P V (2020) Information processing and algorithms laboratory. Accessed on 2020-04-10
Spanhol F A, Oliveira L S, Petitjean C, Heutte L (2015) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462
Kahya M A, Al-Hayani W, Algamal ZY (2017) Classification of breast cancer histopathology images based on adaptive sparse support vector machine. J Appl Math Bioinform 7(1):49
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Vijh, S., Saraswat, M. & Kumar, S. A new complete color normalization method for H&E stained histopatholgical images. Appl Intell 51, 7735–7748 (2021). https://doi.org/10.1007/s10489-021-02231-7
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DOI: https://doi.org/10.1007/s10489-021-02231-7