ADAPTIVE PARAMETER SELECTION FOR GRAPH CUT-BASED SEGMENTATION ON CELL IMAGES

Résumé

Graph cut segmentation approach provides a platform for segmenting images in a globally optimised fashion. The graph cut energy function includes a parameter that adjusts its data term and smoothness term relative to each other. However, one of the key challenges in graph cut segmentation is finding a suitable parameter value that suits a given segmentation. A suitable parameter value is desirable in order to avoid image oversegmentation or under-segmentation. To address the problem of trial and error in manual parameter selection, we propose an intuitive and adaptive parameter selection for cell segmentation using graph cut. The greyscale image of the cell is logarithmically transformed to shrink the dynamic range of foreground pixels in order to extract the boundaries of cells. The extracted cell boundary dynamically adjusts and contextualises the parameter value of the graph cut, countering its shrink bias. Experiments suggest that the proposed model outperforms previous cell segmentation approaches.

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