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Visual parameter optimisation for biomedical image processing.

Pretorius AJ, Zhou Y, Ruddle RA - BMC Bioinformatics (2015)

Bottom Line: First, it is difficult to optimise multiple input parameters for multiple input images.We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance.It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output.

Results: We present a visualisation method that transforms users' ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.

Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.

No MeSH data available.


Related in: MedlinePlus

Visual parameter optimisation for biomedical image processing. (a) Every data record is represented by a row in a tabular visualisation, with columns for input parameters at the left and columns for output measures at the right. (b) Input images are shown at the top right of the image browser. (c) The image-based output produced for each input image is displayed below it in the image browser. (d) To view image-based output, users select rows in the tabular visualisation. The output images that are shown are the ones produced when the parameter values corresponding to the selected rows in the table are applied to the input images. (e) A list of selected parameters and measures is provided to show which parts of their domains the selected output images correspond to. The data shown here are from the case study and show results of a parameterised colour deconvolution technique applied to stained histology images of a liver section and lymphoma (a type of blood cancer).
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Figure 1: Visual parameter optimisation for biomedical image processing. (a) Every data record is represented by a row in a tabular visualisation, with columns for input parameters at the left and columns for output measures at the right. (b) Input images are shown at the top right of the image browser. (c) The image-based output produced for each input image is displayed below it in the image browser. (d) To view image-based output, users select rows in the tabular visualisation. The output images that are shown are the ones produced when the parameter values corresponding to the selected rows in the table are applied to the input images. (e) A list of selected parameters and measures is provided to show which parts of their domains the selected output images correspond to. The data shown here are from the case study and show results of a parameterised colour deconvolution technique applied to stained histology images of a liver section and lymphoma (a type of blood cancer).

Mentions: We show the relationships between input parameters and output measures in a tabular visualisation (see Figure 1(a)). Columns at the left represent parameters and columns at the right represent measures. Each data record is represented by a row that spans across the columns. The value taken for a parameter or measure is encoded in the corresponding column. If the vertical space per row is more than four pixels, a bar chart encodes every column, otherwise a line chart is used. Although line charts do not prevent over-plotting, they are effective to let users discern high-level patterns when limited vertical space is available.


Visual parameter optimisation for biomedical image processing.

Pretorius AJ, Zhou Y, Ruddle RA - BMC Bioinformatics (2015)

Visual parameter optimisation for biomedical image processing. (a) Every data record is represented by a row in a tabular visualisation, with columns for input parameters at the left and columns for output measures at the right. (b) Input images are shown at the top right of the image browser. (c) The image-based output produced for each input image is displayed below it in the image browser. (d) To view image-based output, users select rows in the tabular visualisation. The output images that are shown are the ones produced when the parameter values corresponding to the selected rows in the table are applied to the input images. (e) A list of selected parameters and measures is provided to show which parts of their domains the selected output images correspond to. The data shown here are from the case study and show results of a parameterised colour deconvolution technique applied to stained histology images of a liver section and lymphoma (a type of blood cancer).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4547193&req=5

Figure 1: Visual parameter optimisation for biomedical image processing. (a) Every data record is represented by a row in a tabular visualisation, with columns for input parameters at the left and columns for output measures at the right. (b) Input images are shown at the top right of the image browser. (c) The image-based output produced for each input image is displayed below it in the image browser. (d) To view image-based output, users select rows in the tabular visualisation. The output images that are shown are the ones produced when the parameter values corresponding to the selected rows in the table are applied to the input images. (e) A list of selected parameters and measures is provided to show which parts of their domains the selected output images correspond to. The data shown here are from the case study and show results of a parameterised colour deconvolution technique applied to stained histology images of a liver section and lymphoma (a type of blood cancer).
Mentions: We show the relationships between input parameters and output measures in a tabular visualisation (see Figure 1(a)). Columns at the left represent parameters and columns at the right represent measures. Each data record is represented by a row that spans across the columns. The value taken for a parameter or measure is encoded in the corresponding column. If the vertical space per row is more than four pixels, a bar chart encodes every column, otherwise a line chart is used. Although line charts do not prevent over-plotting, they are effective to let users discern high-level patterns when limited vertical space is available.

Bottom Line: First, it is difficult to optimise multiple input parameters for multiple input images.We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance.It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output.

Results: We present a visualisation method that transforms users' ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.

Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.

No MeSH data available.


Related in: MedlinePlus