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In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modi¯cations. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre-and post-processing operations is demonstrated. Improvement of rawsegmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

Optical coherence tomography (OCT) is an effective tool for determination of pathological topology that reflects structural and textural metamorphoses of tissue. In this paper, we propose a report about our examining of the validity of OCT in identifying changes using a skin cancer texture analysis compiled from Haralick texture features, fractal dimension, complex directional field features and Markov random field method from different tissues. The experimental data set contains 530 OCT images with normal skin and tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevus. Speckle reduction is an essential pre-processing part for OCT image analyze. In this work, we used an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images (B-and/or C-scans). The Haralick texture features as contrast, correlation, energy, and homogeneity have been calculated in various directions. A boxcounting method and other methodologies have been performed to evaluate fractal dimension of skin probes. The complex directional field calculated by the local gradient methodology provides important data for linear dividing of species. We also estimated autocorrelation function using Markov random fields. Additionally, the boosting has been used for the quality enhancing of the diagnosis method. And, finally, artificial neural network (ANN) has been utilized for comparing received rates. Our results demonstrate that these texture features may present helpful information to discriminate a tumor from healthy tissue. We obtained sensitivity about 92% and specificity about 95% for a task of discrimination between MM and healthy skin. Finally, a universal four classes classificatory has been built with average accuracy 75%.

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We proposed a method of automated scintigram image processing enabling an objective evaluation of the renal parenchyma condition to be made based on scintigram brightness and geometric characteristics with threshold processing. We studied the method using a set of real radionuclide images of a renal transplant. The results of clinical studies confirm the effectiveness of the developed method. We obtained objective numerical values associated with thresholding the image from 40% to 80%, based on which one can form an independent assessment of the presence or absence of focal lesions in the renal parenchyma.

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