Object Detection and Thigh CT Tissue Identification Analysis Using Graph Cut Based Image Segmentation with Statistical Priors

Members: Taposh Biswas, Samuel Awidi, Sokratis Makrogiannis

Image segmentation is a field of image analysis that aims to partition an image scene into regions corresponding to objects. It is a popular research topic of image analysis with many applications to the computer vision and medical imaging domains including object recognition and delineation of anatomical structures and tissues. [1]

The goal of this thesis is to investigate whether graph cut techniques can be used to delineate the objects in a visual scene for biomedical and computer vision applications.

The graph cuts method is one of the leading automated segmentation methods for 2D and 3D images. It delineates the regions by creating graph partitions and finds the optimal graph partition by minimizing an energy function that consists of data and smoothness terms. Graph cuts represent the set of pixels in the image using graph vertices. The second goal will be to apply this technique to lower leg and abdomen to identify tissues and quantification. [2]

Our results are validated by k-means clustering method.

  1. Sokratis Makrogiannis, George Economou, Spiros Fotopoulos, and Nikolaos G. Bourbakis Segmentation of Color Images Using Multiscale Clustering and Graph Theoretic Region Synthesis. IEEE transactions on System, Vol. 35, No. 2,  2005
  2. Taposh Biswas, Graph Cut Based Image Segmentation Using Statistical Priors and Its Application to Object Detection and Thigh CT Tissue Identification Analysis. Master’s Thesis Book, Delaware State University, 2018.