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

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.

Effect of radiation dose reduction on texture measures of trabecular bone microstructure: an in vitro study

Osteoporosis is characterized by bone loss and degradation of bone microstructure leading to fracture particularly in elderly people. Osteoporotic bone degeneration and fracture risk can be assessed by bone mineral density and trabecular bone score from 2D projection dual-energy X-ray absorptiometry images. However, multidetector computed tomography image based quantification of trabecular bone microstructure showed significant improvement in prediction of fracture risk beyond that from bone mineral density and trabecular bone score; however, high radiation exposure limits its use in routine clinical in vivo examinations. Hence, this study investigated reduction of radiation dose and its effects on image quality of thoracic midvertebral specimens. Twenty-four texture features were extracted to quantify the image quality from multidetector computed tomography images of 11 thoracic midvertebral specimens, by means of statistical moments, the gray-level co-occurrence matrix, and the gray-level run-length matrix, and were analyzed by an independent sample t-test to observe differences in image texture with respect to radiation doses of 80, 150, 220, and 500 mAs. The results showed that three features-namely, global variance, energy, and run percentage, were not statistically significant ([Formula: see text]) for low doses with respect to 500 mAs. Hence, it is evident that these three dose-independent features can be used for disease monitoring with a low-dose imaging protocol.