Fuzzy Connections - Introduction

The Fuzzy Connections and Multiple Sclerosis (MS) Lesion Finder tools have a great deal in common. Both are designed to find fuzzy-connected features in images based on the features' intensity characteristics. However, the Fuzzy Connections tool is a general-purpose tool, while the MS Lesion Finder has been tailored to the specific task of delineating MS lesions in MRI scans of the head.

The algorithm used for the fuzzy connections is described in:
J.K. Udupa and S. Samarasekera. "Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Application in Image Segmentation". Graphical Models and Image Processing 58:246-261 (1996).

Fuzzy connectivity is a seed-growing method: you define one or more regions on interest that are all within the feature you want to extract. The algorithm then grows the seed region(s) based on the fuzzy affinity between pixels and their neighbours. The fuzzy threshold setting then determines a cutoff fuzzy connectedness such that all pixels above the threshold are considered part of the feature. The affinity between pixels takes into account the degree of adjacency between pixels as well as the similarity of their (multi-parametric) intensity values. The result is a new set of ROIs that encompass the fuzzy-connected feature.
Note: as of Version 6.0 of Jim, the fuzzy connectedness segmentation parameters "threshold", "3-D connection" and (for the MS Lesion Finder) "weight on the prior probabilities" are written into each of the ROIs created as the "source" of the ROI.

Start the Fuzzy Connections and MS Lesion Finder tools from the Toolkits menu:

The Fuzzy Connections tool, shown below, is very similar in appearance to the MS Lesion Finder.

The setup which is common to both the Fuzzy Connections and MS Lesion Finder tools is detailed below.

Input Images

Both tools can work one or more input images, where all images show the same anatomical features in exactly the same pixel locations, but with different image contrast. For example, these could be the two echoes (proton-density-weighted and T₂-weighted) from a double-echo MRI sequence. Or they could be T₁-weighted and T₂-weighted images acquired sequentially but with exactly the same data matrix and slice locations. All input images must be of the same dimensionality (i.e., the same number of slices, and rows and columns within the slice), and in order to match the position of each pixel spatially, they must also have the same pixel sizes and slice thicknesses. If you want to work with images that have slightly different pixel positions or different pixel sizes, you may be able to use the Image Registration tool to bring all images into alignment and make them have the same dimensionality. If your images are already aligned, but have different numbers of pixel samples, then you can use the Image Resampler tool to get the numbers of samples to match across all images.

Click the button until you have the correct number of image selection panels. The figure below shows the setup for working with two input images.

If you have too many images, you can remove any of them by clicking on the

icon.

For each of the input images, set an input image file by clicking on the load image icon: . This will bring up a File Chooser that you can use to set the input image. Alternatively, you can simply type in the folder and image file name in the text fields, or you can press the right mouse button and select from the menu of recently-used images.

For each input image, you can also provide an "intensity hint". To the right of each input image, you will see:

You can use this to provide hints to the Fuzzy Connector about the intensity characteristics of the fuzzy-connected feature you want to extract. This can help to make the feature extraction more robust. Select:

if the feature you want to extract is always brighter than the surrounding background in this image.
if the feature you want to extract is always darker than the surrounding background in this image.
if the feature you want to extract varies in intensity relative to the surrounding background in this image, or if you aren't sure.

Fuzzy Threshold

Next, set the fuzzy threshold. Selection of the right fuzzy threshold for your particular problem is a matter of trial and error. Setting a lower threshold results in a more generous inclusion of pixels in the extracted feature (a larger feature), while setting a higher threshold results in a more parsimonious inclusion (a smaller feature).

Set the fuzzy threshold by moving the slider, or by typing in a threshold value. Fuzzy threshold values must lie between 0 an 1 (exclusive).

Seed pixels

Now tell the tool where to find the ROIs that are used to define the seed pixels. The ROIs can either be read from a previously-saved ROI file, or they can be read directly from the image displayed in Jim's main display.

Note: if you are experimenting with different fuzzy thresholds and other settings, it is strongly recommended that you save your seed ROIs to a disk file, since the resulting feature is created as a set of ROIs that can be automatically loaded onto the displayed image. Repeated application of the Fuzzy Connector tool in the mode where seed ROIs are read from the image will result in the feature pixels being used as the seed pixels for the next fuzzy connection application, which is presumably not what you would want.

Load one of your input images into Jim's display.

Start the ROI Toolkit and define some ROIs that are within the feature you want to delineate. Any type(s) of ROI can be used. You can define a single ROI or multiple ROIs as long as they are all contained within the feature. For ROIs that have no area (Marker, Line and Curved Line ROIs), any pixels that the ROI touches will be used as seed pixels. For other ROIs (Rectangular, Elliptical or Irregular), pixels within the boundary of the ROI will be used as seed pixels. Then either:

Save the ROIs to disk and select . Set the Fuzzy Connector to use the ROI file you created by clicking the button and using the File Chooser to select the ROI file.
or
Select . This will use the ROIs defined on the displayed image to set the seed pixels.

Write fuzzy-connected featured to VRML file

The Fuzzy Connector will create ROIs surrounding the fuzzy-connected features that it has found. However, you may want to visualise these features in Jim's 3-dimensional display. If you select the

check box, then a surface model of the features found will be created, which can then be loaded into the 3-D Display as a virtual reality modelling language (VRML) file. The VRML file created will have a name taken from the first input image name, with an extension .wrl

Connection Options

For information about setting the connection options, please see the specific details for the Fuzzy Connector tool or the MS Lesion Finder tool.

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