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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 an original image (epithelioid mesothelioma, immunohistochemically stained with Calretinin - a positive marker for mesothelioma), segmented objects, derived structure, and image texture (computed with an autoregressive algorithm)
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Figure 2: Illustration of an original image (epithelioid mesothelioma, immunohistochemically stained with Calretinin - a positive marker for mesothelioma), segmented objects, derived structure, and image texture (computed with an autoregressive algorithm)

Mentions: The standardization of image quality is necessary for any reproducible measurement or interpretation, and the first step if we want to develop a quantitative method on image information. Having images of good quality, we could then try to look for image properties that are related to information. In any image these properties are color features in relation to their image position, and include objects, structures, and texture.[151819] 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. If they are not known to the pathologist (i.e. cannot be associated with a diagnosis), they can only be described (or measured). Often these objects are multiple in an image, and their spatial relationship can be analyzed which are called structures.[1520] Naturally, structures can be “condensed” to a new object, if they aggregate and display in translational symmetries such as rings, tubes, lines, etc.[21–24] The described classic (commonly applied by pathologists) “information” approach correlates image features that are mainly “detected” objects and structures, with external information. The “association function” is the knowledge of the viewing pathologist. In other words, an object has to be known by the viewer in order to be identified.[25] It is the implementation of the well-known sentence: “You only do see what you see, i.e., what you have seen and identified previously, and, therefore, you do already know” (< Man erblickt nur, was man schon weiβ und versteht >, in a letter of J.W. Von Goethe to F. V. Müller, dated 24.4.1819).


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

Kayser K - J Pathol Inform (2011)

Illustration of an original image (epithelioid mesothelioma, immunohistochemically stained with Calretinin - a positive marker for mesothelioma), segmented objects, derived structure, and image texture (computed with an autoregressive algorithm)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Illustration of an original image (epithelioid mesothelioma, immunohistochemically stained with Calretinin - a positive marker for mesothelioma), segmented objects, derived structure, and image texture (computed with an autoregressive algorithm)
Mentions: The standardization of image quality is necessary for any reproducible measurement or interpretation, and the first step if we want to develop a quantitative method on image information. Having images of good quality, we could then try to look for image properties that are related to information. In any image these properties are color features in relation to their image position, and include objects, structures, and texture.[151819] 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. If they are not known to the pathologist (i.e. cannot be associated with a diagnosis), they can only be described (or measured). Often these objects are multiple in an image, and their spatial relationship can be analyzed which are called structures.[1520] Naturally, structures can be “condensed” to a new object, if they aggregate and display in translational symmetries such as rings, tubes, lines, etc.[21–24] The described classic (commonly applied by pathologists) “information” approach correlates image features that are mainly “detected” objects and structures, with external information. The “association function” is the knowledge of the viewing pathologist. In other words, an object has to be known by the viewer in order to be identified.[25] It is the implementation of the well-known sentence: “You only do see what you see, i.e., what you have seen and identified previously, and, therefore, you do already know” (< Man erblickt nur, was man schon weiβ und versteht >, in a letter of J.W. Von Goethe to F. V. Müller, dated 24.4.1819).

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