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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.


Example of influence of image standardization (shading correction) on the derived grey value histogram and accuracy of object segmentation (artificial image consisting of randomly distributed balls and fibers)
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Figure 3: Example of influence of image standardization (shading correction) on the derived grey value histogram and accuracy of object segmentation (artificial image consisting of randomly distributed balls and fibers)

Mentions: For other than human-machine interaction purposes such as automated feature extraction, etc., virtual slides have to be spatially standardized for background grey values (vignetting), the range of grey values in each color space, and for the normalization of the grey value histogram (having approximately the same grey values in the same number of pixels).[152535] These image corrections should be done for the original virtual slide. It is also useful to perform the corrections after a gradient transformation on the original image (differentiated image). The work on a differentiated image is an appropriate technique to search for membranes or other objects automatically.[2538] An example on how image standardization affects segmentation and grey value distribution is shown in [Figure 3].


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

Kayser K - J Pathol Inform (2011)

Example of influence of image standardization (shading correction) on the derived grey value histogram and accuracy of object segmentation (artificial image consisting of randomly distributed balls and fibers)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Example of influence of image standardization (shading correction) on the derived grey value histogram and accuracy of object segmentation (artificial image consisting of randomly distributed balls and fibers)
Mentions: For other than human-machine interaction purposes such as automated feature extraction, etc., virtual slides have to be spatially standardized for background grey values (vignetting), the range of grey values in each color space, and for the normalization of the grey value histogram (having approximately the same grey values in the same number of pixels).[152535] These image corrections should be done for the original virtual slide. It is also useful to perform the corrections after a gradient transformation on the original image (differentiated image). The work on a differentiated image is an appropriate technique to search for membranes or other objects automatically.[2538] An example on how image standardization affects segmentation and grey value distribution is shown in [Figure 3].

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.