Image standardization in tissue – based diagnosis

Kayser, Klaus; Borkenfeld, Stephan; Görtler, Jürgen; Kayser, Gian

doi:10.1186/1746-1596-5-s1-s13

Cited by 8 publications

(11 citation statements)

References 2 publications

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“…[1819] Images of cellular morphology are still its basis. They serve, however, only as starting point to invest in biological properties and gene expressions of individual patient's disease, especially cancer.…”

Section: Resultsmentioning

confidence: 99%

“…In any image these properties are color features in relation to their image position, and include objects, structures, and texture. [ 15 18 19 ] The principle is exemplarily explained in Figure 2 . Objects are items that can be identified (interpreted) and “directly associated” with a meaning, for example, trees, animals, buildings, or biological meaningful units such as vessels, cells, nuclei, chromosomes, genes, membranes, etc.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of virtual slides: Approaches to analysis of content-based image information

Kayser

2011

Journal of Pathology Informatics

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Virtual microscopy, which is the diagnostic work on completely digitized histological and cytological slides as well as blood smears, is at the stage to be implemented in routine diagnostic surgical pathology (tissue-based diagnosis) in the near future, once it has been accepted by the US Food and Drug Administration. The principle of content-based image information, its mandatory prerequisites to obtain reproducible and stable image information as well as the different compartments that contribute to image information are described in detail. Automated extraction of content-based image information requires shading correction, constant maximum of grey values, and standardized grey value histograms. The different compartments to evaluate image information include objects, structure, and texture. Identification of objects and derived structure depend on segmentation accuracy and applied procedures; textures contain pixel-based image information only. All together, these image compartments posses the discrimination power to distinguish between object space and background, and, in addition, to reproducibly define regions of interest (ROIs). ROIs are image areas which display the information that is of preferable interest to the viewing pathologist. They contribute to the derived diagnosis to a higher level when compared with other image areas. The implementation of content-based image information algorithms to be applied for predictive tissue-based diagnoses is described in detail.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of virtual slides: Approaches to analysis of content-based image information

Kayser

2011

Journal of Pathology Informatics

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“…The aim being to develop a generalized CADS, it is mandatory to manage color calibration of each device used along the process, from histological staining up to image acquisition [9]. For this study, we have collected image patches from two histological types: Invasive Ductal Carcinoma (IDC) and Mastosis (Ma) with patches from the 'normal' morphology for further be able to remove non-informative patches.…”

Section: Methodsmentioning

confidence: 99%

Out-of-sample extension of diffusion maps in a computer aided diagnosis system. Application to breast cancer virtual slide images

et al. 2013

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“…Slide scanners create a virtual image of the whole glass slide (Kayser et al, 2006; Kayser et al, 2010; Pantanowitz et al, 2013). Whole slide images of TMA are enormous, in the context of size, pixels (resolution), as well as disk space.…”

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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Image standardization in tissue – based diagnosis

Cited by 8 publications

References 2 publications

Quantification of virtual slides: Approaches to analysis of content-based image information

Quantification of virtual slides: Approaches to analysis of content-based image information

Out-of-sample extension of diffusion maps in a computer aided diagnosis system. Application to breast cancer virtual slide images

PATMA: parser of archival tissue microarray

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