Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue

Abstract

The aim of the study was to compare the accuracy of the classification pertaining to the results of two types of soft tissue and bone reconstructions of the spinal CT in detecting the porosity of L1 vertebral body spongy tissue. The dataset for each type of reconstruction (high-resolution bone reconstruction and soft tissue reconstruction) included 400 sponge tissue images from 50 healthy patients and 50 patients with osteoporosis. Texture feature descriptors were calculated based on the statistical analysis of the grey image histogram, autoregression model, and wavelet transform. The data dimensional reduction was applied by feature selection using nine methods representing various approaches (filter, wrapper, and embedded methods). Eleven methods were used to build the classifier models. In the learning process, hyperparametric optimization based on the grid search method was applied. On this basis, the most effective model and the optimal subset of features for each selection method used were determined. In the case of bone reconstruction images, four models achieved a maximum accuracy of 92%, one of which had the highest sensitivity of 95%, with a specificity of 89%. For soft tissue reconstruction images, five models achieved the highest testing accuracy of 95%, whereas the other quality indices (TPR and TNR) were also equal to 95%. The research showed that the images derived from soft tissue reconstruction allow for obtaining more accurate values of texture parameters, which increases the accuracy of the classification and offers better possibilities for diagnosing osteoporosis.

The possibility of rapid prototyping in the manufacture of 3-D models of cranial implants from CT-DATA

“… To create an implant model, the initial data were sets of tomographic slices (DataSets), performed with a fixed pitch of 1 mm (see Fig. 1) 10,11,12 .  The task of creating an implant model using rapid prototyping tools according to spiral computed tomography consists of 6 main stages:  segmentation -the selection on the images of tomographic sections of bone structures;  determine the location and selection of the bone defect replacement method;  creation of a large implant reconstruction;  preparation of data for prototyping in stl format;  creating a layer-by-layer model of the implant in the G-code format for 3D printing on specific equipment.…”

Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis

“…Soft tissue reconstruction images were used for the research. The research showed that the images derived from soft tissue reconstruction allow obtaining more accurate values of texture parameters, increasing the accuracy of classification, and offering better possibilities for diagnosing osteoporosis [ 33 ]. From the series of images, the sample sections showing the inside of the circle with the spongy essence were selected ( Figure 1 ).…”