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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

Bar-chart graphically illustrating the results presented in Table 3.
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Related In: Results  -  Collection


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fig5: Bar-chart graphically illustrating the results presented in Table 3.

Mentions: The average segmentation performance of the supervised Ratsnake approach was measured on each object of the available test images, in comparison with the segmentation performance of the unsupervised Ratsnake, that is, with the plugin being disabled, using only f1(I), and the segmentation performance of the block-based classification approach used for the generation of the force field. The results obtained are apposed in Table 3 and graphically illustrated in Figure 5. In the last row of this table, the average overlap of the initial contours manually drawn by the (nonexpert) users to indicate the respective ROI is also provided. It can be noticed that the best performing method is the supervised Ratsnake approach. The block-based segmentation results are low, indicating that the error introduced by the use of image blocks is significantly high; therefore, the results validate that this approach is inadequate for area measurements. Despite its low accuracy it provides an effective force field for the supervision of Ratsnake. As compared with the initial contour, the overlaps obtained by both the supervised and the unsupervised Ratsnake approach indicate a significant contribution of the snake algorithm.


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

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

Bar-chart graphically illustrating the results presented in Table 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Bar-chart graphically illustrating the results presented in Table 3.
Mentions: The average segmentation performance of the supervised Ratsnake approach was measured on each object of the available test images, in comparison with the segmentation performance of the unsupervised Ratsnake, that is, with the plugin being disabled, using only f1(I), and the segmentation performance of the block-based classification approach used for the generation of the force field. The results obtained are apposed in Table 3 and graphically illustrated in Figure 5. In the last row of this table, the average overlap of the initial contours manually drawn by the (nonexpert) users to indicate the respective ROI is also provided. It can be noticed that the best performing method is the supervised Ratsnake approach. The block-based segmentation results are low, indicating that the error introduced by the use of image blocks is significantly high; therefore, the results validate that this approach is inadequate for area measurements. Despite its low accuracy it provides an effective force field for the supervision of Ratsnake. As compared with the initial contour, the overlaps obtained by both the supervised and the unsupervised Ratsnake approach indicate a significant contribution of the snake algorithm.

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