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Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis.

Iakovidis DK, Goudas T, Smailis C, Maglogiannis I - ScientificWorldJournal (2014)

Bottom Line: In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system.However a tool for detecting and quantifying the disease is not yet available.The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, 35100 Lamia, Greece.

ABSTRACT
Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

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Related in: MedlinePlus

Force field generation for the segmentation of the kidney biopsy image illustrated in Figure 1(b). (a) Classifier's output image, where the different greylevels used indicate different class memberships. (b) Classifier's output after postprocessing with the majority-voting algorithm. (c) Generated force field term f2(I) after postprocessing (the image has also been inverted for presentation purposes).
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fig3: Force field generation for the segmentation of the kidney biopsy image illustrated in Figure 1(b). (a) Classifier's output image, where the different greylevels used indicate different class memberships. (b) Classifier's output after postprocessing with the majority-voting algorithm. (c) Generated force field term f2(I) after postprocessing (the image has also been inverted for presentation purposes).

Mentions: The kernel classifier trained with representative samples from the training images assigns to each block of the images under evaluation a class label. The annotated blocks of such an image are then represented using different greylevels that indicate their class membership, thus rendering an output image as the one illustrated in Figure 3(a). It can be noticed that several misclassified regions may exist that could be considered as noise artifacts.


Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis.

Iakovidis DK, Goudas T, Smailis C, Maglogiannis I - ScientificWorldJournal (2014)

Force field generation for the segmentation of the kidney biopsy image illustrated in Figure 1(b). (a) Classifier's output image, where the different greylevels used indicate different class memberships. (b) Classifier's output after postprocessing with the majority-voting algorithm. (c) Generated force field term f2(I) after postprocessing (the image has also been inverted for presentation purposes).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3926425&req=5

fig3: Force field generation for the segmentation of the kidney biopsy image illustrated in Figure 1(b). (a) Classifier's output image, where the different greylevels used indicate different class memberships. (b) Classifier's output after postprocessing with the majority-voting algorithm. (c) Generated force field term f2(I) after postprocessing (the image has also been inverted for presentation purposes).
Mentions: The kernel classifier trained with representative samples from the training images assigns to each block of the images under evaluation a class label. The annotated blocks of such an image are then represented using different greylevels that indicate their class membership, thus rendering an output image as the one illustrated in Figure 3(a). It can be noticed that several misclassified regions may exist that could be considered as noise artifacts.

Bottom Line: In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system.However a tool for detecting and quantifying the disease is not yet available.The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

View Article: PubMed Central - PubMed

Affiliation: Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, 35100 Lamia, Greece.

ABSTRACT
Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

Show MeSH
Related in: MedlinePlus