Multi-class stain separation using independent component analysis

Trahearn, Nicholas; Snead, David; Cree, Ian A.; Rajpoot, Nasir M.

doi:10.1117/12.2081933

Cited by 18 publications

(14 citation statements)

References 8 publications

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“…The H&E images were generated using a stain vector of a real image used to train the crypt segmentation method. The stain vector was determined using the method proposed by [ 28 ]. The results for the Dice coefficient on both pixel-level and object-level are shown in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

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A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

2016

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BackgroundThere have been great advancements in the field of digital pathology. The surge in development of analytical methods for such data makes it crucial to develop benchmark synthetic datasets for objectively validating and comparing these methods. In addition, developing a spatial model of the tumour microenvironment can aid our understanding of the underpinning laws of tumour heterogeneity.ResultsWe propose a model of the healthy and cancerous colonic crypt microenvironment. Our model is designed to generate synthetic histology image data with parameters that allow control over cancer grade, cellularity, cell overlap ratio, image resolution, and objective level.ConclusionsTo the best of our knowledge, ours is the first model to simulate histology image data at sub-cellular level for healthy and cancerous colon tissue, where the cells have different compartments and are organised to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. Qualitative and quantitative validation has been performed on the model results demonstrating good similarity to the real data. The simulated data could be used to validate techniques such as image restoration, cell and crypt segmentation, and cancer grading.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1126-2) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

“…10 ) using a user-defined colour deconvolotion matrix. In the results for this paper we used the colour deconvolotion matrix suggested by Ruifrok and Johnston [ 27 ] and a matrix obtained from an image using the stain separation method proposed by Trahearn et al [ 28 ] as follows: …”

Section: Methodsmentioning

confidence: 99%

A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

2016

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“…During stain normalisation, SVD is used for automated stain matrix estimation to address colour variation problems [57]. The SVD method works by calculating the plane from the two vectors corresponding to the two largest singular values of the SVD decomposition of the OD(optical density) transformed pixels and then later projecting OD transformed pixels onto the plane [84,57,1]. SVD easily adapts to variations in the local statistics of an image [95,64,27,3,38].…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

“…Stain normalisation techniques employ non-negative matrix factorisation (NMF) [86,41,85], singular value decomposition (SVD) [57], principal component analysis (PCA) [45], [33], independent component analysis (ICA) [84], statistical based algorithm ICA [1,51], and machine learning [97,78,35,98]. However, these methods are only applicable to certain histology images.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for stain normalisation in digital histopathological images

Bukenya

2019

Multimed Tools Appl

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Stain in-homogeneity adversely affects segmentation and quantification of tissues in histology images. Stain normalisation techniques have been used to standardise the appearance of images. However, most the available stain normalisation techniques only work on a particular kind of stain images. In addition, some of these techniques fail to utilise both the spatial and textural information in histology images, leading to image tissue distortion. In this paper, a hybrid approach has been developed, based on an octree colour quantisation algorithm combined with the Beer-Lambert law, a modified blind source separation algorithm, and a modified colour transfer approach. The hybrid method consists of two stages the stain separation stage and colour transfer stage. An octree colour quantisation algorithm combined with Beer-Lambert law, and a modified blind source separation algorithm are used during the stain separation stage to computationally estimate the amount of stain in an histology image based on its chromatic and luminous response. A modified colour transfer algorithm is used during the colour transfer stage to minimise the effect of varying staining and illumination. The hybrid method addresses the colour variation problem in both H&DAB (Haemotoxylin and Diaminobenzidine) and H&E (Haemotoxylin and Eosin) stain images. The stain normalisation method is validated against ground truth data. It is widely known that the Beer-Lambert law applies to only stains (such as haematoxylin, eosin) that absorb light. We demonstrate that the Beer-Lambert law applies is applicable to images containing a DAB stain. Better stain normalisation results are obtained in both H&E and H&DAB images.

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“…Attention is devoted to the color transformation process, which should overcome the problematic and undesirable color variation due to differences in color responses of slide scanners, raw materials and manufacturing techniques of stain vendors, as well as staining protocols across different pathology labs [ 17 ]. While some systems transform the RGB color space into more perceptual color spaces, such as CIE-Lab [ 18 – 22 ], Luv [ 23 – 25 ], Ycbcr [ 26 ], or 1D/2D color spaces [ 18 , 27 , 28 ], others perform illumination and color normalization through white shading correction methods [ 29 , 30 ], background subtraction techniques, (adaptive) histogram equalization [ 14 , 31 , 32 ], Gamma correction methods [ 33 ], Reinhard’s method [ 34 ], (improved) color deconvolution [ 35 , 36 ], Non-negative Matrix Factorization (NMF) and Independent Component Analysis (ICA) [ 17 , 37 – 40 ], decorrelation stretching techniques [ 14 , 32 , 41 ], anisotropic diffusion [ 22 ]. After these preprocessing steps, the labeled structures of interest are detected by morphological binary or gray level operators [ 28 , 42 – 45 ], automatic thresholding techniques [ 20 , 28 , 33 , 43 ], clustering techniques [ 46 , 47 ], the Fast Radial Symmetry Transform (FRST) [ 16 , 48 ], Gaussian Mixture Models [ 20 , 22 , 49 ], and edge detectors such as the Canny edge detector, Laplacian of Gaussian filters [ 50 ] or Difference of Gaussian filters [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

et al. 2018

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BackgroundIn the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension.SoftwareThe software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers’ relative location). Additionally, it computes novel measures of markers’ co-existence in tissue volumes depending on their density.ConclusionsApplications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.

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Multi-class stain separation using independent component analysis

Cited by 18 publications

References 8 publications

A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

A model of the spatial tumour heterogeneity in colorectal adenocarcinoma tissue

A hybrid approach for stain normalisation in digital histopathological images

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

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