Elaboration of the transform with generalized comb scaling and wavelet functions for the image segmentation - Полякова, Марина Вячеславовна; Крылов, Виктор Николаевич; Ищенко, Алеся Владимировна (Eastern-European Journal of Enterprise Technologies, 2014)
When designing systems for computer recognition of visual images, segmentation of the processed class of images is an important stage. For many applied problems, the boundaries of objects, filled with structural texture often must be determined on an image. Existing methods for solving such problems are characterized by large computational cost. An alternative is using a parallel analysis of the spectral composition of the structural texture images for reducing image processing time and applying wavelet analysis for selecting objects of a given size. To analyze the image content in several frequency bands, it is advisable to use solutions of two-scale difference equation in the space of generalized functions. The solution of this equation for most sets of its coefficients can be obtained only approximately using the method of successive approximations. In the frequency domain, successive approximations of the solution of two-scale difference equation are characterized by a line spectrum and allow to perform parallel spectral analysis of structural texture images. Therefore, it is proposed to use these functions as analyzing functions of transforms with generalized comb scaling and wavelet functions to reduce image processing time due to replacing several processing levels by one. The proposed transforms were used in developing the information technology for vessel segmentation on angiograms. By applying the transform with the generalized comb wavelet function in the vessel localization, several processing levels were replaced by one. The latter is achieved by the fact that the convolution with the generalized comb wavelet function is similar to using a set of bandpass filters. As a result of experiments, it was shown that using the developed information technology provides vessel localization quality, required for making diagnostic decisions. In this case, the processing time is reduced by 43%, and the characteristics of detection reliability of vessel pixels are changed as follows: type I error probability is reduced by 1.22 times, and type II error probability is increased by 1.14 times.