Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours

Abstract

A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope (IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are able to adapt to the curve topology of the cell boundary. Segmentation performance is evaluated using six indices: boundary displacement error, global consistency error, variation of information, Jaccard distance error, rand index and F-index.