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

Kayser K - J Pathol Inform (2011)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: UICC-TPCC, Institute of Pathology, Charite, Charite Platz, D-10118 Berlin, Germany.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Illustration of grey value histogram, derived segmentation thresholds, and segmented objects (epithelioid mesothelioma, H and E, same case as displayed in Figure 2)
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Figure 5: Illustration of grey value histogram, derived segmentation thresholds, and segmented objects (epithelioid mesothelioma, H and E, same case as displayed in Figure 2)

Mentions: All object segmentation algorithms are based upon grey value thresholds. The detection of maxima and minima in the grey value histogram is, therefore, an indicator of the accuracy to potentially segment objects.[17] In addition to the normalization of the grey value histogram, the number and significance of grey value maxima and minima are characteristics that describe suitable potential thresholds to separate an object area from the background.[214171838–40] The grey value difference between a maximum and the associated minimum should amount to at least 5% of the histogram peak, if reproducible object segmentation is aimed.[20254041] An example of evaluated potential thresholds in a standardized histological image (epithelioid mesothelioma) together with the summary of object segmentation prerequisites is depicted in [Figure 5].


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

Kayser K - J Pathol Inform (2011)

Illustration of grey value histogram, derived segmentation thresholds, and segmented objects (epithelioid mesothelioma, H and E, same case as displayed in Figure 2)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3046376&req=5

Figure 5: Illustration of grey value histogram, derived segmentation thresholds, and segmented objects (epithelioid mesothelioma, H and E, same case as displayed in Figure 2)
Mentions: All object segmentation algorithms are based upon grey value thresholds. The detection of maxima and minima in the grey value histogram is, therefore, an indicator of the accuracy to potentially segment objects.[17] In addition to the normalization of the grey value histogram, the number and significance of grey value maxima and minima are characteristics that describe suitable potential thresholds to separate an object area from the background.[214171838–40] The grey value difference between a maximum and the associated minimum should amount to at least 5% of the histogram peak, if reproducible object segmentation is aimed.[20254041] An example of evaluated potential thresholds in a standardized histological image (epithelioid mesothelioma) together with the summary of object segmentation prerequisites is depicted in [Figure 5].

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: UICC-TPCC, Institute of Pathology, Charite, Charite Platz, D-10118 Berlin, Germany.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus