Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology

Abstract

<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>

Texture segmentation method for computer-assisted dermatologic diagnostic system

“…As mentioned above, different types of psoriasis are different in color and texture, so they can be represented by different models of texture (statistical, spectral and combined (spectralstatistical)) taking the color into account. The models of spectral, statistical, and spectral-statistical textures were proposed in [15]. Let ) , ( m y x  be the color characteristic of the image, then models of texture (spectral, statistical, and spectral-statistical) with taking into account color are represented as (1)-( 3).…”