An Interactive Medical Image Segmentation Framework Using Iterative Refinement
This work addresses medical image segmentation for clinical evaluation, but it is incremental as it builds on existing methods like GrabCut.
The authors tackled the problem of segmenting medical images with irregularities by proposing a two-stage algorithm combining mathematical morphology and GrabCut, which achieved accurate segmentation results with minimal user interaction.
Image segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. 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.