# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 1 Introduction  
Osteoporosis is a degenerative disease of the bones caused by decreased bone quality and density; it is commonly found in the elderly. With reduced bone quality and density in cases of osteoporosis, bones will become more fragile and carry a high risk of bone fractures. Osteoporosis is usually detected based on DEXA (dual-energy X-ray absorptiometry) dataexamination for BMD evaluation, with manual inspection by specialists. Automatized detection of osteoporosis based on image modalities is now possible based on artificial intelligence (AI)/machine learning (ML). However, joint issues such as the limited number of data and attributes extracted from the image are still limiting the enhancement of AI/ML input features. To solve this problem, we have tried to adopt signal-processing techniques from other disciplines (e.g., the geosciences discipline) to enhance the attributes/features of medical image processing.  
There are similarities between the problems experienced in the medical field and in other disciplines such as geoscience, and these are prevalent when their technologies attempt to understand the object behind the surface (either geobodies in the geoscience domain or inside the human body). Various technologies commonly used in both disciplines include destructive and non-destructive methods. As an example, the data in the geosciences discipline are mostly recorded as the signal. In contrast, the others in the medical field are kept as an image. However, both technologies are designed to improve the understanding of the object beneath the surface. Thus, a key question is whether it is possible to adopt technology from one discipline and apply it to the others to improve their workflow for specific purposes, for example, through implementing new technologies where artificial intelligence (AI)/machine learning (ML) are applied.  
One of the key techniques in geoscience for subsurface characterization is the seismic method. The seismic method is a non-destructive method used to analyze the subsurface, wherein acoustic waves from an active seismic source are recorded as a signal and analyzed. Data processing utilizes a variety of signal-processing techniques to improve the resulting image. The other challenging problem in geosciences is the case of determining the optimum dew point pressure, which is very important during the production of hydrocarbon from a targeted reservoir. Selection of a suitable empirical model can be achieved by applying hybrid neuro-fuzzy [1]. Similarly, in medicine, different apparatuses such as X-ray scanning machines have been used to image the human organs inside the body. The effectiveness of the interdisciplinary transfer of technology within medical geology is most intriguing, wherein the concept of geology in the geosciences discipline is applied to link the natural environment and human health for the betterment of global society [2].  
This paper addresses the possibility of adopting the standard data-processing techniques into the image processing of medical data to enhance features that can be used for further studies, such as the application of AI/ML for better automatization and recognition in medical cases. This paper aims to enhance the image attributes taken from CT scanning to solve some specific issues in osteoporosis identification.