# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 5 Results and Discussion
### First Derivative (Contrast Attribute)  
The first derivative attribute or contrast attribute was taken based on a pixel compared to the surrounding pixels. It was calculated in the vertical and horizontal directions only. It can also be extended to view the contrast in the diagonal direction. The contrast index was calculated as the total contrast counted inside the ROI divided by the number of pixels inside the ROI. The contrast index showed a significant difference between normal and osteoporotic bone, whereby the osteoporotic bone has a higher value than the normal bone on the CT scan images. In contrast, DEXA imaging showed that the normal bone has a higher value than the osteoporotic bone. There are also some inconsistencies regarding the different modalities of scanning. Therefore, further experiments need to be conducted to verify the robustness of this attribute in different modalities. The direct display of the  
Figure 6: Phase index of different image data of pelvic (left), hip (middle), and lumbar spine (right); (a) normal bone and (b) osteoporotic bone, red box indicate a selected ROI.  
contrast attribute highlights the significant difference between normal and osteoporotic bone, as shown in Figure 7.  
Based on the experimental results, the adoption of signal-processing techniques into image processing can enhance the variability of image attributes, which is possibly helpful for feature enhancement during the application of artificial intelligence (AI) or machine learning (ML) for the automatization of osteoporosis diagnosis.  
As a feasibility study, this research work can contribute to the stage of image attribute generation in medicine by adopting signal-processing techniques that have already been proven in characterizing subsurface objects, much like in the geosciences disciplines, where attributes such as grayscale, instantaneous phase, frequency, roughness, and first derivative (contrast) have been able to highlight significant differences between normal and osteoporosis bone images. Some attributes, i.e., instantaneous phase, roughness, and contrast attributes, produce significantly different visual images between normal and osteoporotic bone. The density of the anomalies of those attributes is quite different. It is possible that converting the image into these attributes will increase the anomalies in the targeted area. If an anomaly detection algorithm is applied, these attributes may obtain better anomaly detection. [PERSON] et al. (2015) used a two-stage training method with an anomaly detection algorithm to maintain a low overfitting value. Their technique consists of a pre-training process for determining sample distribution and an implicit regularization process for optimizing the feature boundaries. They claimed that the proposed method produced a better classification result and consistently outperformed other regularization methods on deep CNNs. Other signal attributes may possess similar potential; however, further  
Figure 7: Contrast index of different image data of pelvic (left), hip (middle), and lumbar spine (right); (a) normal bone and (b) osteoporotic bone, a red box indicates a selected ROI.  
experiments need to be conducted to achieve optimum separation between normal and osteopororotic bone.