Advances in Mass Data Analysis of Signals and Images in by Petra Perner, Ovidio Salvetti

By Petra Perner, Ovidio Salvetti

This ebook constitutes the refereed court cases of the foreign convention on Mass facts research of signs and pictures in medication, Biotechnology and Chemistry, MDA 2006 and 2007, held in Leipzig, Germany..

The subject matters contain thoughts and advancements of sign and photograph generating methods, item matching and item monitoring in microscopic and video microscopic photographs, 1D, second and 3D form research, description and have extraction of texture, constitution and placement, picture segmentation algorithms, parallelization of picture research and semantic tagging of pictures from lifestyles technology applications.

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Additional resources for Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry: International Conference, MDA 2006/2007,

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Bottom: the components found at the end of the process for the 128×128 image, left, and the 64×64 image, right. Fig. 5. The segmentation obtained for the running example In Fig. 5, the segmentation of the running example is shown. The white lines, superimposed on the original image, identify the borders of the components of the foreground that have been singled out. 3 Extending the Method to Color Images In this section, we briefly discuss a possible way to extend our method to color images. Starting from an input color image C, the three gray-level images in the (RGB) color space are computed.

The first step is aimed at identifying suitable seeds, corresponding to the objects of interest in the image, while the second step associates to the identified seeds pixels removed during the first step, provided that fusions are not created. Segmentation is accomplished by using also information derived from a lower resolution representation of the image, with the purpose of reducing the number of foreground components to the most significant ones. Some hints regarding extension of the method to color images are also discussed.

The variogram-based thresholding could better recognize the true boundaries of the objects than both Otsu thresholding and FCM-based segmentation. The two latter methods yielded many false positive pixels and resulted in over-segmentation. The watershed-based method provided similar results with those obtained from the proposed segmentation method. However, it can be observed that the segmentation result obtained from the watershed method for the images shown in Figure 2 fails to highlight the larger spots; whereas this visual effect can be noticed in those obtained from the proposed approach.

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