Microscope Volume Segmentation Improved through Non-Linear Restoration

Moacir P. Ponti
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Moacir P. Ponti: Universidade de São Paulo, Brazil

International Journal of Natural Computing Research (IJNCR), 2010, vol. 1, issue 4, 37-46

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.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jncr00:v:1:y:2010:i:4:p:37-46

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