Microscope Volume Segmentation Improved through Non-Linear Restoration

Microscope Volume Segmentation Improved through Non-Linear Restoration

Moacir P. Ponti (Universidade de São Paulo, Brazil)
Copyright: © 2012 |Pages: 10
DOI: 10.4018/978-1-4666-1574-8.ch020
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Abstract

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparison between the segmentation results before and after restoration is presented. The evaluation of the region segmentation included the root mean squared error and a normalized uniformity measure. Results showed significant improvement of segmentation when using the non-linear restoration method based on prior known information, such as the imaging system and the noise statistics.
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Optical Microscope Volumes

Volumes acquired through conventional optical microscopes have two main sources of degradation: a) frequencies cut-off that works as a low-pass filter, which causes a blurring effect especially throughout the z-axis (Goodman, 1996), and b) photon-count noise, a signal-dependent noise that can be well modeled by a Poisson distribution, due to the nature of light based sensors, such as the CCD (charged-coupled devices) (Snyder & Miller, 1991).

Restoration on Microscope Volumes

Pre-processing steps in computer vision applications uses often linear and smoothing filters to improve image condition and the posterior analysis (Colicchio et al., 2005). However, these linear filters and smoothing operators such as Gaussian filters, as well as non-linear morphological opening or closing operators, may remove important structures present in images (Agard, 1984). Then, restoration methods that are based on image degradation and formation can improve the results. By using the theoretical model of a point-spreading function (PSF) of the microscope developed by Gibson and Lanni (1991), and using the noise statistics knowledge, it is possible to apply non-linear restoration algorithms that are best suited to the problem.

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