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Segmenting transparent phase objects, such as biological cells from brightfield microscope images, is a difficult problem due to the lack of observable intensity contrast and noise. Previous image analysis solutions have used excessive defocusing or physical models to obtain the underlying phase properties. Here, an improved cell boundary detection algorithm is proposed to accurately segment multiple cells within the level set framework. This uses a novel speed term based on local phase and local orientation derived from the monogenic signal, which renders the algorithm invariant to intensity, making it ideal for these images. The new method can robustly handle noise and local minima, and distinguish touching cells. Validation is shown against manual expert segmentations. ©2008 IEEE.

Original publication




Conference paper

Publication Date



181 - 184