S Manisha’s scientific contributions

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Publications (1)


Fig.1.Training Phase  
Fig.1. shows the Training Phase model. Before building the model, the top and bottom slices of each organ are first manually identified. Then, linear interpolation is applied to generate the same number of slices for the organ in every training image. This is for establishing anatomical correspondences. 2-D OACAM models are then constructed for each slice level from the images in the training set. The LW cost function and GC parameters are also estimated in this stage. Interpolation is a method of constructing new data points within the range of a discrete set of known data points. In engineering and science, one often has a number of data points, obtained by sampling or experimentation, which represent the values of a function for a limited number of values of the independent variable. It is often required to interpolate (i.e. estimate) the value of that function for an intermediate value of the independent variable. This may be achieved by curve fitting or regression analysis. Suppose the formula for some given function is known, but too complex to evaluate  
Fig.3.Shape Model of Training Image.  
Fig.5. ACM Output Fig.5. shows the RMs difference between the original texture and the combined model. This is the ACM Output.
Fig.6. (a)Test Image (b) Selection of Contour Point (c) Contour Drawn of the Abdominal Output (d) Comparisons of Segmented Liver Image With Similar Model Image (e) Final 2-D Segmentation.  
CLG for Automatic Image Segmentation
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August 2014

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121 Reads

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58 Citations

SSRN Electronic Journal

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S Santhana Priya

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S Manisha

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[...]

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M S Ramasubhaeswari

This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is represented as edges. In graph theory, a cut is a partition of the nodes that divides the graph into two disjoint subsets. For initialization, a pseudo strategy is employed and the organs are segmented slice by slice through the OACAM (Oriented Active Contour Appearance Model). Initialization provides rough object localization and shape constraints which produce refined delineation. This method is tested with different set of images including CT and MR images (3D image) and produced perfect segmentation results.

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Citations (1)


... While comparing with five color spaces, segmentation scheme produces results noticeably better in RGB color space compared to all other color spaces. [51] presented an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. ...

Reference:

Call for Papers - Journal of Electrical Systems (JES), E-ISSN: 1112-5209, indexed in Scopus & Web of Science
CLG for Automatic Image Segmentation

SSRN Electronic Journal