How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)

Kayser, Klaus; Görtler, Jürgen; Metze, Konradin; Goldmann, Torsten; Vollmer, Ekkehard; Mireskandari, Masoud; Kosjerina, Zdravko; Kayser, Gian

doi:10.1186/1746-1596-3-s1-s11

Cited by 32 publications

(35 citation statements)

References 20 publications

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“…Texture derived parameters strongly depend upon the image size, which should be kept constant within the system [13,14]. Dependent upon the available computation power the image size could be set to 2 k×2 k pixel; however an image size of 0.5 k ×0.5 k pixels was sufficient in our measurements [15].…”

Section: Discussionmentioning

confidence: 99%

“…There do several algorithms exist how to compare images in between, most of them are designed to assure good quality image copies. [10,13,14]. Virtual slides often do not fulfil a homogenous illumination, and might be too "dark" or too pale dependent upon the staining procedure and scanner assessment.…”

Section: Image Standardization Proceduresmentioning

confidence: 99%

“…The differentiated image is mainly serves for segmentation algorithms. The difference of filter parameters are a measure of image quality, i.e., the smaller the image required filter parameters the better is the image quality [14] The described algorithms are applied for each color space separately, especially if working with images presented in the red -green -blue (rgb) color space. The corrected and standardized image serves for further investigations.…”

Section: Image Standardization Proceduresmentioning

confidence: 99%

“…Therefore, measures of image quality are addressed to shading correction, normal distribution of gray values and absolute height of gray values (usual 8 bits). The mathematical correction procedures belong to the class of local dependent fil- ters [14,17]. These filter operations can be applied to the original virtual slide and its differentiated derivative.…”

Section: Image Standardization Proceduresmentioning

confidence: 99%

See 3 more Smart Citations

AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

Kayser

Görtler²,

Bogovac³

et al. 2010

Folia Histochem Cytobiol.

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Abstract:The technological progress in digitalization of complete histological glass slides has opened a new door in tissue -based diagnosis. The presentation of microscopic images as a whole in a digital matrix is called virtual slide. A virtual slide allows calculation and related presentation of image information that otherwise can only be seen by individual human performance. The digital world permits attachments of several (if not all) fields of view and the contemporary visualization on a screen. The presentation of all microscopic magnifications is possible if the basic pixel resolution is less than 0.25 microns. To introduce digital tissue -based diagnosis into the daily routine work of a surgical pathologist requires a new setup of workflow arrangement and procedures. The quality of digitized images is sufficient for diagnostic purposes; however, the time needed for viewing virtual slides exceeds that of viewing original glass slides by far. The reason lies in a slower and more difficult sampling procedure, which is the selection of information containing fields of view. By application of artificial intelligence, tissue -based diagnosis in routine work can be managed automatically in steps as follows: 1. The individual image quality has to be measured, and corrected, if necessary. 2. A diagnostic algorithm has to be applied. An algorithm has be developed, that includes both object based (object features, structures) and pixel based (texture) measures. 3. These measures serve for diagnosis classification and feedback to order additional information, for example in virtual immunohistochemical slides. 4. The measures can serve for automated image classification and detection of relevant image information by themselves without any labeling. 5. The pathologists' duty will not be released by such a system; to the contrary, it will manage and supervise the system, i.e., just working at a "higher level". Virtual slides are already in use for teaching and continuous education in anatomy and pathology. First attempts to introduce them into routine work have been reported. Application of AI has been established by automated immunohistochemical measurement systems (EAMUS, www.diagnomX.eu). The performance of automated diagnosis has been reported for a broad variety of organs at sensitivity and specificity levels >85%). The implementation of a complete connected AI supported system is in its childhood. Application of AI in digital tissue -based diagnosis will allow the pathologists to work as supervisors and no longer as primary "water carriers". Its accurate use will give them the time needed to concentrating on difficult cases for the benefit of their patients.

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Section: Discussionmentioning

confidence: 99%

Section: Image Standardization Proceduresmentioning

confidence: 99%

Section: Image Standardization Proceduresmentioning

confidence: 99%

Section: Image Standardization Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

Kayser

Görtler²,

Bogovac³

et al. 2010

Folia Histochem Cytobiol.

Self Cite

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show abstract

“…The quality of digitized images has been focussed in the citation by Kayser K. et al, [47], that are estimated by spatial independence and local filtering operations that investigate in brightness, low peak given to noise ratio, full range of grey scale values, maximum gradients, equalisation of grey value distribution, and existence of grey values for thresholds.…”

Section: Texturementioning

confidence: 99%

Review on Histopathological Slide Analysis using Digital Microscopy

Bhattacharjee¹,

Mukherjee²,

Nag³

et al. 2014

IJAST

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Evaluation of nuclear chromatin using grayscale intensity and thresholded percentage area in liquid‐based cervical cytology

Lee

Han

Yoo

et al. 2018

Diagnostic Cytopathology

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How to measure image quality in tissue-based diagnosis (diagnostic surgical pathology)

Abstract: Background: Automated image analysis, measurements of virtual slides, and open access electronic measurement user systems require standardized image quality assessment in tissuebased diagnosis.

Cited by 32 publications

References 20 publications

AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

AI (artificial intelligence) in histopathology--from image analysis to automated diagnosis.

Review on Histopathological Slide Analysis using Digital Microscopy

Evaluation of nuclear chromatin using grayscale intensity and thresholded percentage area in liquid‐based cervical cytology

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