Equalisation of Archival Microscopic Images from Immunohistochemically Stained Tissue Sections

Neuman, Urszula; Korzyńska, Anna; López, Carlos; Lejeune, Marylène; Roszkowiak, Łukasz; Bosch, Ramón

doi:10.1016/s0208-5216(13)70056-1

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…The idea is to find criteria for automatic adjustment of parameters to fit to processed image. We should consider developing appropriate preprocessing phase that could equalize images [20] before they are segmented as it may improve results and make it easier to adjust parameter values. As a result of this investigation we believe that there are three ways to improve segmentation of tested methods.…”

Section: Discussionmentioning

confidence: 99%

Short survey: adaptive threshold methods used to segment immunonegative cells from simulated images of follicular lymphoma stained with 3,3'-Diaminobenzidine&Haematoxylin

Roszkowiak

Korzyńska

Pijanowska

2015

Annals of Computer Science and Information Systems

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Abstract-We perform a short survey of image thresholding methods for very specific task, and assess their performance comparison. We analyse performance of adaptive thresholding methods concerning segmentation of immunonegative cells of follicular lymphoma tissue samples stained with 3,3'-Diaminobenzidine&Haematoxylin. We use artificial images based on experimental images that greatly simulates real samples and simplifies process of evaluation. We chose 8 methods of adaptive threshold segmentation, with different approach. They were applied to 6 different monochromatic images derived from original RGB images, by splitting layers, conversion to Lab colour space and colour deconvolution. Evaluation of the results was performed with basic statistical measures as sensitivity and specificity along with Jaccard's coefficient. We identify the thresholding algorithms with superior performance. Collected results will be used to design the new better method based on this approach.

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Section: Discussionmentioning

confidence: 99%

Short survey: adaptive threshold methods used to segment immunonegative cells from simulated images of follicular lymphoma stained with 3,3'-Diaminobenzidine&Haematoxylin

Roszkowiak

Korzyńska

Pijanowska

2015

Annals of Computer Science and Information Systems

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“…For example, cell analysis is another important application field of the CBMIA approaches, referring to cell migration analysis (Boucher et al 1998), multiple cell detection (Yao et al 2005;Pasquale and Stander 2009), blood cell classification (Tuzel et al 2007), cell segmentation (Korzynska et al 2007), semen cell quality analysis (Witkowski 2013), cell nucleus detection (Nogueira and Teofilo 2014;John et al 2016), stem cell analysis (Huang et al 2016;Zhang et al 2016;Li et al 2017), antibody analysis (Soda et al 2009;Neuman et al 2013), pathological tissue analysis (Tasoulis et al 2014;Jothi and Rajam 2017) and so on. Material analysis is also an important application domain of the CBMIA methods, including food microstructure analysis (Ding and Gunasekaran 1998), collagen fiber analysis (Elbischger et al 2004), metallography image analysis (Grzegorzek 2010), membranes porosity evaluation (Chwojnowski et al 2012), cement quality analysis (Wang et al 2014) and so on.…”

Section: Potential Application Fields Of Cbmia Methodologymentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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“…TMA allows a more efficient way to analyse tissue sections, especially when they should undergo further processing, such as staining. It is possible to use immunohistochemistry (Neuman et al, 2013; Korzynska et al, 2010; Lopez et al, 2014), in situ hybridization (Garcia-Rojo, Bueno & Slodkowska, 2010), or fluorescence in situ hybridization (Du & Dua, 2010) on all cores simultaneously. This way, it lowers the intralaboratory variability of different staining concentrations between slices.…”

Section: Introductionmentioning

confidence: 99%

PATMA: parser of archival tissue microarray

Roszkowiak

López

2016

PeerJ

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Tissue microarrays are commonly used in modern pathology for cancer tissue evaluation, as it is a very potent technique. Tissue microarray slides are often scanned to perform computer-aided histopathological analysis of the tissue cores. For processing the image, splitting the whole virtual slide into images of individual cores is required. The only way to distinguish cores corresponding to specimens in the tissue microarray is through their arrangement. Unfortunately, distinguishing the correct order of cores is not a trivial task as they are not labelled directly on the slide. The main aim of this study was to create a procedure capable of automatically finding and extracting cores from archival images of the tissue microarrays. This software supports the work of scientists who want to perform further image processing on single cores. The proposed method is an efficient and fast procedure, working in fully automatic or semi-automatic mode. A total of 89% of punches were correctly extracted with automatic selection. With an addition of manual correction, it is possible to fully prepare the whole slide image for extraction in 2 min per tissue microarray. The proposed technique requires minimum skill and time to parse big array of cores from tissue microarray whole slide image into individual core images.

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Equalisation of Archival Microscopic Images from Immunohistochemically Stained Tissue Sections

Cited by 14 publications

References 21 publications

Short survey: adaptive threshold methods used to segment immunonegative cells from simulated images of follicular lymphoma stained with 3,3'-Diaminobenzidine&Haematoxylin

Short survey: adaptive threshold methods used to segment immunonegative cells from simulated images of follicular lymphoma stained with 3,3'-Diaminobenzidine&Haematoxylin

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

PATMA: parser of archival tissue microarray

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