The use of the Hellwig's method for feature selection in the detection of myeloma bone destruction based on radiographic images

摘要

The radiological test is cost-effective, widely available, allows for the visualisation of large areas of the skeleton and can identify long bones potentially at risk for fractures in osteolysis sites. Therefore, radiology is often used in the early stages of multiple myeloma, in the detection and characterisation of complications, and in the assessment of the patient's response to treatment. The accuracy of this method can be improved through the use of appropriate algorithms of computer image processing and analysis. In the study, the feature vector based on humerus CR images was extracted. As a result of the analysis, 279 image descriptors were obtained. Hellwig's method in the selection process was applied. It found the set of feature combinations of the largest integral index of information capacity. To evaluate these combinations, 11 classifiers were built and tested. As a result, 2 feature sets were identified that provided the highest classification accuracy in combination with the K-NN classifier. The 9-NN classifier for the first combination (2 features) was used and 5-NN for the second one (3 features). The classification accuracy (depending on the quality index used) was as follows: overall classification accuracy - 93%, classification sensitivity - 92%, classification specificity - 96%, positive predictive value - 96% and negative predictive value 93%. Results show that: (1) the use of humerus CR images may be useful in the detection of bone damages caused by multiple myeloma; (2) the Hellwig's method is effective in the feature selection of the analysed kind of images. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.