By Petra Perner, Ovidio Salvetti
This ebook constitutes the refereed lawsuits of the foreign convention on Mass information research of indications and pictures in medication, Biotechnology and Chemistry, MDA 2006 and 2007, held in Leipzig, Germany..
The subject matters contain innovations and advancements of sign and photograph generating techniques, item matching and item monitoring in microscopic and video microscopic photos, 1D, second and 3D form research, description and have extraction of texture, constitution and site, picture segmentation algorithms, parallelization of photograph research and semantic tagging of pictures from lifestyles technological know-how applications.
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Additional info for Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
If the histogram is mainly bimodal, a generally large valley separates the two main peaks and the two thresholds can be set at the two extremes of the valley. For a multimodal histogram, the thresholds can be shifted towards more internal valleys, depending on user's needs. In fact, a too small value of θi could cause the detection of a number of noisy components, erroneously interpreted as belonging to the foreground. In turn, the value of θf should be set in such a way to prevent excessive foreground fragmentation, which would split foreground components perceived as individual entities into a number of meaningless components.
In this paper, we introduce a method using unsupervised histogram clustering via a curve ﬁtting process based on multi Gaussian models. The histogram is an invariant feature available for medical image processing. Simultaneously, we take the spatial information into consideration to reduce the noise eﬀect and to connect the thin object which is disconnected by noises. The paper is organized as follows: We propose a model using a curve ﬁtting technique to solve the histogram clustering problem in section 2.
The purpose is to normalize the data and to reduce the dimensionality of the problem from 3D to 2D. Second, we perform statistical shape analysis to evaluate the shape variability of the datasets. Our analysis is based on the following two approaches: The complex Bingham distribution model , which involves one parameter that characterizes the degree of variability of the data, and the generalized Procrustes method , which captures the dominant variation of the data. In addition, we employ Kendall’s spherical coordinate system  to visualize the shape distribution of the BAC-triangles.