A method is proposed for extracting the region of interest from biological volume data when non-uniform intensity is involved. Binary coded processing, using thresholding and active contour models, is often used for extracting the region of interest from biological images, but this method suffers from two defects: no allowance is made for the three-dimensional structure of the region of interest and preprocessing of the data is required. On the other hand, the region growing method can extract a complex 3-D structure and requires no preprocessing, although it is difficult to apply to biological volume data that contain a lot of noise and have non- uniform intensity in the region of interest. This paper reports improvements to the conventional region growing method to provide more robustness against non-uniform intensity and noise. This is achieved by only paying attention to the local area, using information inside and outside the region of interest, and by using a median value. This method can be easily applied as the number of parameters is less than that by conventional techniques and no prior knowledge of the original data is needed.
In this paper we extend the autoregressive (AR) model to the multilevel AR model with wavelet transformation, in order to get the AR coefficients at each level as a set of shape descriptors for every level. To get the multilevel AR model, we use the wavelet transformation such as Haar wavelet to a boundary data. Then real AR and complex-AR (CAR) models are adopted to the multilevel boundary data of a shape to extract the features at each level. Furthermore we present the relation of the autocorrelation coefficients between adjacent resolution levels to elucidate the relation between AR model and wavelet transformation. Some experiments are also shown for the multilevel AR and CAR models with a certain similarity measure.
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