Paper
24 June 1998 Deformable membrane for the segmentation of cytological samples
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Abstract
In clinical cytology quantitative parameters have to be extracted from a large number of biological samples to obtain diagnostically relevant and reproducible information. Computer-assisted microscopy can provide methods that increase the quality and comparability of clinical studies by reducing the subjective influence of human operators on their results. In order to guarantee the correctness of extracted parameters automatic and reliable segmentation of the samples is required. For the detection of cytological objects a novel deformable membrane model is presented which is strictly based on macroscopical mechanics and statics. This is appropriate for modeling physiological membranes, because their shape is determined exclusively by mechanical forces. The self-driven membrane converges iteratively towards a stable state, where the contrary forces are in balance. However, active contours may not yield sufficient detection quality for acquisition of quantitative parameters. Therefore, after convergence a stochastic optimization process corrects the contour according to local graylevel information. This yields a contour that is well- adapted to the local graylevel structure. Additionally, for subsequent cytometric quantifications a local measure of confidence is provided for the contour. this can be used to enhance the robustness of the extracted parameters by incorporating the confidence factors in the quantification process. The method is applied to cytological and histological samples of different magnification.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Volker H. Metzler, Joerg Bredno, Thomas Martin Lehmann, and Klaus Spitzer "Deformable membrane for the segmentation of cytological samples", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310852
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Cited by 14 scholarly publications.
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KEYWORDS
Stochastic processes

Image segmentation

Tin

Microscopy

Tissues

Mechanics

Annealing

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