Feature Extraction and Classification of Thorax X-Ray Image in the Assessment of Osteoporosis

Riandini Riandini, Mera Kartika Delimayanti

Abstract


Previous studies showed that it was possible to have a prediction or an early detection of osteoporosis by measuring the thickness of the cortex of the clavicle of thorax x-ray image. The drawback of this system was that it was still dependent on the  operator  of  subjective  vision  applications  in  the measurement. In addition, the accuracy of the system very much relied on the x-ray image quality. Therefore, it is in urgent need of another system which can automatically classify x-ray image and another method of image processing to identify and acknowledge a certain texture of the based image using a set of classes or texture classification given. In this paper, calculation and  analysis  of  a  series  of  image  processing  algorithms  to perform x-ray image classification are done using the K-Nearest Neighbor (KNN) and feature extraction techniques Gray Level Co-occurrence Matrix (GLCM) on small sample size data of 46. Thorax x-ray images of 44 females and 2 males with the average age of 63 years old. T-score of these images had been measured using DEXA scan before as a justification. The proposed method shows that the clavicle cortex thickness measurement using GLCM and KNN method as feature extraction and image classification has its sensitivity of 100% and specificity of 90%. Furthermore,  the  accuracy  which is  obtained from the  entire implementation capability in correctly assessing osteoporosis is 97.83%. Thus, it is evident that it is significantly correlated with predetermined  T-score  of  DEXA  in  the  assessment  of osteoporosis.

 


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