The study shows the need for express systems, in which it is necessary to perform the analysis of texture images in various areas of diagnosis, for example, in medical express diagnostics of dermatologic disorders. Since the reliability of decision-making in such systems depends on the quality of image segmentation, which, as a rule, have the nature of spectral-statistical textures, it is advisable to develop methods for segmentation of such images and models for their presentation. A model of spectral-statistical texture is proposed, which takes into account the random nature of changes in the field variations and quasi-harmonics. On its basis, a vector-difference method of texture segmentation has been developed, which is based on the vector transformation of images of spectral and statistical textures based on vector algebra. The stages of the vector-difference method are the following: an evaluation of the spectral texture feature; an evaluation of the statistical texture feature; vector-difference transformation of texture images; a boundary detection of the homogeneous regions. For each pixel of the image in the processing aperture, the features of the spectral and statistical texture are evaluated. Statistical texture evaluation was performed by the quadratic-amplitude transformation. At the stage of vector-difference transformation of texture images, a vector of features of each pixel of an image is constructed, the elements of which are estimates of features of a spectral and statistical texture, and the modulus of the difference of two vectors is calculated. At the stage of boundary detection of homogeneous regions, Canny method was applied. The developed vector-difference texture segmentation method was applied both to model images of spectral-statistical texture and to texture images obtained in technical and medical diagnostics systems, namely, for images of psoriasis disease and wear zones of cutting tools. To compare the segmentation results, frequency-detector and amplitude-detector methods of texture segmentation were applied to these images. The quality of segmentation of homogeneous textured regions was evaluated by the Pratt's criterion and by constructing a confusion matrix. The research results showed that the developed vector-difference texture segmentation method has increased noise tolerance at a sufficient processing speed.

Запропоновано комбiнований метод сегментацiї зображень вiдсканованих документiв, в якому, на вiдмiну вiд вiдомих, проводиться попереднє вiдокремлення областi графiчних i фотозображень вiд текстових областей i фону. При цьому проводиться аналiз зв'язкових компонент, якi є рiзними для графiчних зображень, фотозображень та текстових областей. Для класифiкацiї видiлених областей, на областi фото i графiки використовується блоковий метод. Встановлено, що такий спосiб розбиття областей на блоки менше впливає на якiсть сегментування в порiвняннi з застосуванням блочного методу безпосередньо до вихiдного зображення. Для вiддiлення бiльш складних за формою текстових областей вiд фону застосовано обробка околицi кожного пiкселя. Для видiлення на зображеннях вiдсканованих документiв границь iлюстрацiй використовувався метод Блумберга. Для подiлу на фото i графiку запропоновано розбиття iлюстрацiї на блоки пiкселiв. Кожному блоку пiкселiв вiдповiдає вектор з двох ознак: середнього значення величини локального градiєнта i середнього значення функцiї, що локалiзує на зображеннях вiдсканованих документiв лiнiйнi об'єкти (графiка i символи тексту). Отриманi вектора ознак класифiкувалися машиною опорних векторiв. При видiленнi текстових фрагментiв використовувалися низькочастотна фiльтрацiя i порогове перетворення. Практичне вiдпрацювання комбiнованого методу проведено для сегментацiї тестових зображень вiдсканованих статей газет з бази даних документiв MediaTeam унiверситету Оулу (Фiнляндiя). Встановлено, що комбiнований метод характеризується пiдвищеною швидкодiєю сегментацiї зображень при високiй якостi обробки Ключовi слова: сегментацiя зображень, вiдсканований документ, блочний метод, графiчне зображення, фотозображеня, текстовий фрагмент, зв'язкова компонента, метод Блумберга UDC 004.93

Currently,digital diagnosis systems that process medical images are widely used in the diagnosis processin the field of healthcare. The purpose of such systems is to assist the doctorin establishing the diagnosis, or in monitoring changes in the patient's condition during treatment. Dermatology is one of the areas of medicine where the number of visits to a doctoris high. At the same time, the tasks of establishing a diagnosis and monitoring changes in the patient's condition during treatment are time-consuming and subjective and they depend on knowledge and experience of a dermatologist. However, today, digital systems for the diagnosis of dermatological diseases are not in every locality, expensive and are stationary systems. With the development ofmobile information technologies, it became possible to develop mobile image processing systems for the analysis of dermatological diseases, which allow you to: receiving, analyze, and compareimages before and after treatment at anytime, anywhere.One of the basic procedures in image processing systems is segmentation, the purpose of which is to reduce the amount of processed data. Segmentation methods can be classified asboundary-based methods and region-based methods. Dermatological disease images consistof regions which have difference by texture, that is, the segmentation problem is considered as the task of selection homogeneous regions by texture. The result of image processing depends on the quality of segmentation. To improve the qualityof segmentation, in this work, we developed a detector quasi-periodic texture segmentation method for dermatological images processing,which contain quasi-periodic textures on a complex background in noisy conditions. This method is developed on the basis of the methodology of texture segmentation using detector, the stages of which are localization of spatial frequencies, detection, and contoursegmentation. To localize of spatial frequencies, a wavelet-function improved by transform of graph of power function was used, which increases the accuracy of determining the boundaries of quasi-periodic textures contained in dermatological disease images.On the detection step,the comb filters, which are wavelets with a periodic or quasi-periodic transfer function that are applied to each image line, wereused. The Canny method was use, as a contour preparation. Detectorsegmentation methods are focused on the image model. Therefore, a mathematical model of medical dermatological disease images was proposed, which contain quasi-periodic textures on a complex background in noisy conditions, as a model of a texture image with amplitude-modulated fluctuations in the intensity values by a random change in the amplitude and frequency of the oscillation. The developed method was applied to test medical images of psoriasis disease, which are available on the Internet. The accuracy of the segmentation of medical images of psoriasis disease containing a quasi-periodic texture was evaluated using the proposed method and the method using Gabor filters.It is shown that the proposed method is characterized by high speed and high segmentation quality, that is, it can be used in the development of express-diagnostic systems for monitoring changes in the patient's condition during treatment andto determine a parameter such as lesionsarea.

<abstract> <p>An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease.</p> </abstract>

In this paper the issues of creating Decision Support System was considered. The classification of DSS at the user level is given. The importance of creating mobile DSS was shown. Known methods for evaluating and comparing multicriteria alternatives AHP, MAHP, Topsis was considered, also. The modified algorithm of the heuristic method Smart was proposed. A comparison of the proposed the Smart method modification with known methods, concluded that using the modified method Smart and method Topsis in mobile DSS is expediential. The architecture and the realization of mobile DSS were described. Mobile DSS was realized on the Android platform and it works on the smartphones and the tablets, which allows decision-makers to be mobile in off-line mode.