Abstract
In automatic cancer detection, nuclei segmentation is a very essential step which enables the classification task simpler and computationally more efficient. However, automatic nuclei detection is fraught with the problems of inter-class variability of nuclei size and shapes. In this research article, a novel unsupervised edge detection technique, is proposed for segmenting the nuclei regions in liver cancer Hematoxylin and Eosin (H&E) stained histopathology images. In this novel edge detection technique, the notion of computing local standard deviation is incorporated, instead of computing gradients. Since, local standard deviation value is correlated with the edge information of image, this novel method can extract the nuclei edges efficiently, even at multiscale. The edge-detected image is further converted into a binary image by employing Ostu (IEEE Trans Syst Man Cybern 9(1):62–66, 1979)’s thresholding operation. Subsequently, an adaptive morphological filter is also employed in order to refine the final segmented image. The proposed nuclei segmentation method is also tested on a well-recognized multi-organ dataset, in order to check its effectiveness over wide variety of dataset. The visual results of both datasets indicate that the proposed segmentation method overcomes the limitations of existing unsupervised methods, moreover, its performance is comparable with the same of recent deep neural models like DIST, HoverNet, etc. Furthermore, three quality metrics are computed in order to measure the performance of several nuclei segmentation methods quantitatively. The mean value of quality metrics reveals that proposed segmentation method indeed outperformed other existing nuclei segmentation methods.
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Aghdasi F, Ward RK (1996) Reduction of boundary artifacts in image restoration. IEEE Trans Image Process 5(4):611–618
Ali S, Madabhushi A (2012) An integrated region, boundary, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Trans Med Imaging 31(7):1448–1460
Al-Kofahi Y, Lassoued W, Lee W, Roysam B (2010) Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 57(4):841–852
Basu M (2002) Gaussian-based edge-detection methods-a survey. IEEE Trans Syst Man Cybern Part C (appl Rev) 32(3):252–260
Bhateja V, Nigam M, Bhadauria AS, Arya A, Zhang EYD (2019) Human visual system based optimized mathematical morphology approach for enhancement of brain MR images. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01386-z
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277
Cheuk W, Chan JK, Shek TW, Chang JH, Tsou MH, Yuen NW, Ng WF, Chan AC, Prat J (2001) Inflammatory pseudotumor-like follicular dendritic cell tumor: a distinctive low-grade malignant intra-abdominal neoplasm with consistent Epstein-Barr virus association. Am J Surg Pathol 25(6):721–731
De Natale FG, Boato G (2017) Detecting morphological filtering of binary images. IEEE Trans Inf Forensics Secur 12(5):1207–1217
Fukuma K, Prasath VS, Kawanaka H, Aronow BJ, Takase H (2016) A study on nuclei segmentation, feature extraction and disease stage classification for human brain histopathological images. Procedia Comput Sci 96:1202–1210
Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C, Tosolini M, Camus M, Berger A, Wind P, Zinzindohoué F (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313(5795):1960–1964
Gonzalez RC, Woods RE, Eddins SL (2004) Digital image processing using MATLAB. Pearson Education India, Delhi
Graham S, Vu QD, Raza SEA, Azam A, Tsang YW, Kwak JT, Rajpoot N (2019a) Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal 58:101563
Graham S, Chen H, Gamper J, Dou Q, Heng PA, Snead D, Tsang YW, Rajpoot N (2019b) MILD-Net: minimal information loss dilated network for gland instance segmentation in colon histology images. Med Image Anal 52:199–211
Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, An YB (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171
Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804
Hou L, Nguyen V, Kanevsky AB, Samaras D, Kurc TM, Zhao T, Gupta RR, Gao Y, Chen W, Foran D, Saltz JH (2019) Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pattern Recogn 86:188–200
Huang PW, Lai YH (2010) Effective segmentation and classification for HCC biopsy images. Pattern Recogn 43(4):1550–1563
Huang DY, Wang CH (2009) Optimal multi-level thresholding using a two-stage Otsu optimization approach. Pattern Recogn Lett 30(3):275–284
Irshad H, Veillard A, Roux L, Racoceanu D (2013) Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential. IEEE Rev Biomed Eng 7:97–114
Ishikawa M, Murakami Y, Ahi ST, Yamaguchi M, Kobayashi N, Kiyuna T, Yamashita Y, Saito A, Abe T, Hashiguchi A, Sakamoto M (2016) Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens. J Med Imaging 3(2):027502
Jung C, Kim C (2010) Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng 57(10):2600–2604
Khan AM, 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
Kong H, Akakin HC, Sarma SE (2013) A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE Trans Cybern 43(6):1719–1733
Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A (2017) A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imaging 36(7):1550–1560
Liu X, Chen S, Zou M, Chai Z (2000) Edge-detection based on the local variance in angiographic images. J Electron 17(4):338–344
Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175
Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc Lond Ser B Biol Sci 207(1167):187–217
McCann MT (2015) Tools for automated histology image analysis. Doctoral dissertation, Carnegie Mellon University
McCann MT, Ozolek JA, Castro CA, Parvin B, Kovacevic J (2014) Automated histology analysis: opportunities for signal processing. IEEE Signal Process Mag 32(1):78–87
Moga AN, Gabbouj M (1998) Parallel marker-based image segmentation with watershed transformation. J Parallel Distrib Comput 51(1):27–45
Naylor P, Laé M, Reyal F, Walter T (2019) Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging 38(2):448–459
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Phoulady HA, Goldgof DB, Hall LO, Mouton PR (2016) Nucleus segmentation in histology images with hierarchical multilevel thresholding. In: Medical imaging 2016: digital pathology, vol 9791. In International society for optics and photonics, p 979111
Plissiti ME, Nikou C (2012) Overlapping cell nuclei segmentation using a spatially adaptive active physical model. IEEE Trans Image Process 21(11):4568–4580
Rabinovich A, Agarwal S, Laris C, Price J, Belongie S (2003) Unsupervised color decomposition of histologically stained tissue samples. Adv Neural Inf Process Syst 16:667–674
Raza SEA, Cheung L, Shaban M, Graham S, Epstein D, Pelengaris S, Khan M, Rajpoot NM (2019) Micro-Net: a unified model for segmentation of various objects in microscopy images. Med Image Anal 52:160–173
Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graphics Appl 21(5):34–41
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention (MICCAI), Springer, New York, pp 234–241
Roy S, Kumar Jain A, Lal S, Kini J (2018) A study about color normalization methods for histopathology images. Micron 114:42–61
Roy S, Lal S, Kini J (2019) Novel color normalization method for Hematoxylin & Eosin stained histopathology images. IEEE Access 7:28982–28998
Ruderman DL, Cronin TW, Chiao CC (1998) Statistics of cone responses to natural images: implications for visual coding. JOSA A 15(8):2036–2045
Ruifrok AC, Johnston DA (2001) Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 23(4):291–299
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
Shih FY, Cheng S (2005) Automatic seeded region growing for color image segmentation. Image vis Comput 23(10):877–886
Song Y, Zhang L, Chen S, Ni D, Lei B, Wang T (2015) Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning. IEEE Trans Biomed Eng 62(10):2421–2433
Tellez D, Litjens G, Bándi P, Bulten W, Bokhorst JM, Ciompi F, van der Laak J (2019) Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med Image Anal 58:101544
Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter AM, Esposito I, Navab N (2016) Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging 35(8):1962–1971
Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Comput Archit Lett 13(06):583–598
Wang Z, Bovik AC (2009) Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98–117
Xu H, Lu C, Berendt R, Jha N, Mandal M (2016) Automatic nuclei detection based on generalized laplacian of gaussian filters. IEEE J Biomed Health Inform 21(3):826–837
Yi F, Huang J, Yang L, Xie Y, Xiao G (2017) Automatic extraction of cell nuclei from H&E-stained histopathological images. J Med Imaging 4(2):027502
Zanjani FG, Zinger S, Bejnordi BE, van der Laak JA, de With PH (2018) Stain normalization of histopathology images using generative adversarial networks. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), IEEE, pp 573–577
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This research work was supported in part by the Science Engineering and Research Board (SERB), Department of Science and Technology (DST), Govt. of India under Grant ECR/2017/000689.
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Roy, S., Das, D., Lal, S. et al. Novel edge detection method for nuclei segmentation of liver cancer histopathology images. J Ambient Intell Human Comput 14, 479–496 (2023). https://doi.org/10.1007/s12652-021-03308-4
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DOI: https://doi.org/10.1007/s12652-021-03308-4