Abstract—This work aims to achieve an automatic and optimal graph cut phase based on a segmentation method presented in a previous paper. A graph-based segmentation algorithm, starting from a seed point belonging to the region of interest (ROI), is able to find the Minimum Path Spanning Tree (MPST) by using a new cost function and an optimal aggregation criterion. In order to extract the ROI, a graph-cut of the obtained tree is absolutely necessary. By definition, the main drawback of the graph-based segmentation methods is the loss of spatial and contextual information. To overcome this problem, a new method based on compactness measure is here proposed The present approach is applied to the biomedical field, considering Magnetic Resonance Imaging (MRI) volumes of the hand and neurological districts.
Index Terms—Automatic segmentation, graph-based segmentation, graph cut, compactness measure.
The authors are with Università degli Studi di Genova, via Opera Pia 11a, I16145 Genova, Italy (e-mail: sonia.nardotto@edu.unige.it, laura.gemme@edu.unige.it, silvana.dellepiane@unige.it).
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Cite:Sonia Nardotto, Laura Gemme, and Silvana G. Dellepiane, "An Optimal and Automatic Graph Cut Method for Biomedical Images Using Compactness Measure," International Journal of Computer Theory and Engineering vol. 9, no. 2, pp. 73-78, 2017.
