Visual dataflow language for image processing

Most current DFVPLs support flow control to facilitate experiments and complex problems. However, current approaches in DFVPLs still remain inefficient. We show that inadequacies in existing visual programming languages may be magnified in applications involving image analysis. These include a lack of efficient communication mechanisms and strong dependency on human involvement to customise properties. For instance, properties in one computational component can not be shared for other components. Moreover, conditional expressions used in control components hold data values that are unrelated with those computational components. Furthermore, since image processing libraries usua.lly only explicitly support pipeline processing, as exemplified by the widely used Insight Toolkit for Medical Image Segmentation and Registration (ITK), a looping algorithm would be difficult to implement without a feedback mechanism supported by the visual language itself.We propose a data-flow visual programming language that encompasses several novel control constructs and parameterised computational units. These components are facilitated by a novel hybrid data-flow model. We also present several conceptual models and design alternatives for control constructs. Several mechanisms and techniques are provided to enhance data propagation for these components. We demonstrate, in an environment that utilises ITK as the underlying processing engine, that the inadequacies in existing DFVPLs can be satisfactorily addressed through the visual components proposed in this thesis