Limits...
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

Demonstration of automated selection of ROI in a pleural biopsy (epitheloid mesothelioma, H & E). The accuracy of the tested algorithm (2227 slides, biopsies obtained from colon, lung, pleura, and stomach) revealed >97%)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3046376&req=5

Figure 6: Demonstration of automated selection of ROI in a pleural biopsy (epitheloid mesothelioma, H & E). The accuracy of the tested algorithm (2227 slides, biopsies obtained from colon, lung, pleura, and stomach) revealed >97%)

Mentions: To furthermore tune the algorithm, a virtual slide can be spatially analyzed for regions which display with no or only a few textures, and consequently do not contain objects and structures. These areas are called background. Obviously, they do not contribute to image information. Thus, the image can be divided into two non-overlapping areas, namely, the object space and the background. The same procedure can then be applied for the object space only, and sub-areas displaying with the “maximum of textures, objects, or structures” can be separated.[15181925] These areas are called "busy" and correspond to ROI. They can be reproducible identified by measures of the texture entropy and structural entropy based upon segmented objects [Figure 6,[38] as well as by application of spectral analysis,[48] or by mimicking the pathologist's pathway through a histological image.[4950]


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

Kayser K - J Pathol Inform (2011)

Demonstration of automated selection of ROI in a pleural biopsy (epitheloid mesothelioma, H & E). The accuracy of the tested algorithm (2227 slides, biopsies obtained from colon, lung, pleura, and stomach) revealed >97%)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Demonstration of automated selection of ROI in a pleural biopsy (epitheloid mesothelioma, H & E). The accuracy of the tested algorithm (2227 slides, biopsies obtained from colon, lung, pleura, and stomach) revealed >97%)
Mentions: To furthermore tune the algorithm, a virtual slide can be spatially analyzed for regions which display with no or only a few textures, and consequently do not contain objects and structures. These areas are called background. Obviously, they do not contribute to image information. Thus, the image can be divided into two non-overlapping areas, namely, the object space and the background. The same procedure can then be applied for the object space only, and sub-areas displaying with the “maximum of textures, objects, or structures” can be separated.[15181925] These areas are called "busy" and correspond to ROI. They can be reproducible identified by measures of the texture entropy and structural entropy based upon segmented objects [Figure 6,[38] as well as by application of spectral analysis,[48] or by mimicking the pathologist's pathway through a histological image.[4950]

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