Aggregation of Region-based and Boundary-based Knowledge Biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT Images

Abstract

Background: Osteoporosis is a term used to represent the reduced bone density, which is caused by insufficient bone tissue production to balance the old bone tissue removal. Medical Imaging procedures such as X-Ray, Dual X-Ray and Computed Tomography (CT) scans are used widely in osteoporosis diagnosis. There are several existing procedures in practice to assist osteoporosis diagnosis, which can operate using a single imaging method. Objective: The purpose of this proposed work is to introduce a framework to assist the diagnosis of osteoporosis based on consenting all these X-Ray, Dual X-Ray and CT scan imaging techniques. The proposed work named “Aggregation of Region-based and Boundary-based Knowledge biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT images” (ARBKSOD) is the integration of three functional modules. Methods: Fuzzy Histogram Medical Image Classifier (FHMIC), Log-Gabor Transform based ANN Training for osteoporosis detection (LGTAT) and Knowledge biased Osteoporosis Analyzer (KOA). Results: Together, all these three modules make the proposed method ARBKSOD scored the maximum accuracy of 93.11%, the highest precision value of 93.91% while processing the 6th image batch, the highest sensitivity of 92.93%, the highest specificity of 93.79% is observed during the experiment by ARBKSOD while processing the 6th image batch. The best average processing time of 10244 mS is achieved by ARBKSOD while processing the 7th image batch. Conclusion: Together, all these three modules make the proposed method ARBKSOD to produce a better result.