Abstract-The paper presents the method of medical images similarity estimation based on feature extraction and analysis. The proposed method has been developed for and tested on rat brain histological images, however, it can be applied for other types of medical images, since the general approach is based on consideration of the shape of core components present in a given template image. The proposed method can be used in image analysis tools in a wide range of image-based medical investigations, in particular, in the brain researches.The theoretical background of the proposed method is presented in the paper. The expert evaluation approach used for assessment of the proposed method effectiveness is explained and illustrated by examples. The method of medical images similarity estimation based on feature analysis consists of several stages: colour model conversion, image normalization, anti-noise filtering, contours search, conversion, and feature analysis. The results of the proposed method algorithmic realization are demonstrated and discussed.

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Background. The evaluation of video image objects is a relatively difficult task. While solving the task of the geometric representation of a surveillance object, the following additional factors should be considered: possible overlapping of objects, similarity of complex elements, similarity of object elements and background, etc. Objective. The development of a method for complicated objects shape evaluation for application in video surveillance systems for estimation of dynamics of an object's movement, examination of the object's behavior on a probable execution of unauthorized actions, and for other tasks. Methods. The procedure of the background subtraction is used for identification of a raster shape of the surveillance object. To detect a vector shape of the object contours, the DEI approach is applied. The sorting procedures are used for identification of reference contour points and for forming the smooth curves. Results. The proposed method includes the following stages: color space conversion and normalization, object shape detection, contours detection and analysis, sorting of vector data, forming of smooth contour curve, object area computing. When the contour points number is reduced in 1.5 times, an average error of the proposed method compared with the DEI approach for accuracy rate is 0.75 %, for performance rate it is 8.43 %, for resource consuming rate it is 3.09 %. Conclusions. The proposed method allows to define an array of vector contour points which represent an "approximate" surveillance object of a complicated shape and it minimizes the data volume to be used in further analysis of a motion trajectory and other similar tasks without decreasing the accuracy. In addition, this method enables describing the surveillance object by an equal quantity of contour points that in turn can simplify the task of surveillance objects classification.

Phonetic algorithms are effective tools using in text retrieval systems. Phonetic algorithms are used:in retrieval services of the Internet, in retrieval of medical database, to check spelling in different languages, to translate from one language to another, to find and remove duplicates of data, to generate realistic personal data, for initial processing of voice requests; for processing and storing personal data, for indexing musical works, historical documents, for interlanguage transliteration. Phonetic algorithms code the words with similar pronunciation by identical symbols. This allows to compare and index many words in text retrieval systems based on their phonetic similarity.One of these phonetic algorithms is Soundex algorithm. The description of Soundex algorithm in English [1]: Stage 1. To remember the first letter of the word. Stage 2. To remove the h and w except for the first letter of the word.