An Image Analysis & Software Development Environment

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    What is NeatVision
    Feature Overview
   Sample Files
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Developer Info

Sample Files

Data files:

NeatVision data files are given the .dat suffix in the examples below. Netscape users can download .dat files by right clicking on the desired link and selecting 'Save Link As' from the resulting menu.

Colour Codes:

Each data connection has two properties data type and connection status. There are currently 8 supported data types, these are listed below.

  Integer / Array data
  Double precision Floating point data
  Boolean data
  String data
  Fourier data

The other connection property is it's status. There are two main states for a connection, connected and disconnected. There is also an addition sub state disconnected but  using default value, these states and associated colours are listed below.

  Node connected
  Node disconnected
  Node disconnected, default available

Aim:Edge Detection. A range of the edge detectors available to NeatVision are applied to a sample image. A user defined threshold (integer input) is applied prior to applying the binary border block. Alternatively, this can be chosen interactively using the blocks slide bar (activate by double clicking the threshold block).

dat File: edgedetector.dat

Visual Workspace:

Aim: Isolate the largest item in the field of view. The input image is passed through a threshold block with a user defined level of 74 prior to erosion. The image is eroded to remove small white regions. The image is then labelled (i.e. a unique grey scale is applied to every distinct binary object). The processing flow path is split to allow multiple operations on the same data. The lower path allows the image to be enhanced and sent to an output window. The upper path data is passed through a dual threshold block, allowing a unique blob to be isolated and displayed. The input values to the dual threshold can be either predefined integer inputs, or the user can select them interactively via the slide bars.

dat File: largest.dat

Visual Workspace:

Aim: Feedback implementation. This example will allow the input image to be processed a number of times (as determined by the user defined integer input to the feedback block). The second (lower) output window allows the user to see the effect on the input image for each pass through the feedback cycle.

dat File: feedback.dat

Visual Workspace:

Aim: If .. else  implementation. This example will allow conditional processing of the input image (as determined by Boolean (orange) input to the if block). The processing path can be redirected based on the conditional Boolean input.

dat File: ifelse.dat

Visual Workspace:

Aim: For loop  implementation. This is a standard implementation of a for loop structure. As per the example given below, the feedback actions will be implemented as the loop variable increments from 1 to 7 in steps of 1 (as determined by integer (green) inputs to the for block).  The second (upper) output window allows the user to see the effect on the input image for each pass through the feedback cycle. The loop variable is indicated by the third (lower) output from the for block.

dat File: forloop.dat

Visual Workspace:

Aim: Image Probing.

Aim: DICOM data manipulation. This example illustrates how you can process data from a DICOM sequence (*.dcm) and store the modified data as a DICOM sequence. The original DICOM header is stripped from the original sequence and forms the header for the new processed sequence. The top example illustrates how we can take a single image from the volume, perform a Sobel edge detection operation on it and replace the modified data in the DICOM sequence. The bottom example illustrates how by employing looping we can doe this for the full DICOM sequence.

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