8 July 1999 Progress in the robust automated segmentation of real cell images
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We propose a collection of robust algorithms for the segmentation of cell images from Papanicolaou stained cervical smears (`Pap' smears). This problem is deceptively difficult and often results on laboratory datasets do not carry over to real world data. Our approach is in 3 parts. First, we segment the cytoplasm from the background using a novel method based on the Wilson and Spann multi-resolution framework. Second, we segment the nucleus from the cytoplasm using an active contour method, where the best contour is found by a global minimization method. Third, we implement a method to determine a confidence measure for the segmentation of each object. This uses a stability criterion over the regularization parameter (lambda) in the active contour. We present the results of thorough testing of the algorithms on large numbers of cell images. A database of 20,120 images is used for the segmentation tests and 18,718 images for the robustness tests.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. Bamford, P. Jackway, Brian Lovell, "Progress in the robust automated segmentation of real cell images", Proc. SPIE 3747, New Approaches in Medical Image Analysis, (8 July 1999); doi: 10.1117/12.351626; https://doi.org/10.1117/12.351626

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