Abstract: Our research follows an ontological approach to automatically extract the necessary indicators from an input sentence and synthesize an infographic corresponding to it. This work includes (1) the creation of a dataset, (2) an end-to-end domain-agnostic framework, and (3) demonstrating the application of the proposed framework. The results demonstrate our framework's ability to extract the necessary textual cues from real-world textual descriptions (from various domains) and synthesize meaningful infographics.
Abstract: In order to obtain the most suitable method for medical image segmentation, we propose a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut on the input image thus resulting in an efficient segmented result. The obtained result can be further refined by user interaction which can be done using the Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images.
Abstract: The baseline 3D visualization techniques focus on making more realistic rendering of the 3D scene at the cost of approximation of the scene which could be critical in the medical domain. Also, there is no provision for user interaction to perform modifications in the visualization model as per the needs. This is required by doctors for studying different parts of the body through the same data. Interactive visualization of medical volume data requires ”realtime rendering” of the scalar data under consideration with "high rendering quality". These two properties are complementary and thus an efficient technique needs to be devised as per the application. Having coupled with the ideas from Computer Graphics to efficiently tackle this problem, the key features of the developed software are: