# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 2 Literature Review  
### Medical Problem: Osteoporosis  
Osteoporosis is a common disease, especially for the elderly. Osteoporosis is defined as a disease characterized by reduced bone mass and alteration of bone architecture resulting in increased bone fragility and fracture risk [3]. People with osteoporosis usually also have a high risk of bone fractures. However, this disease is treatable, although early detection is often difficult. The diagnosis is usually determined by analyzing the bone mineral densitometry based on dual energy X-ray absorptiometry (DEXA) scanning. Unfortunately, DEXA has several limitations in assessing the severity of osteoporosis at certain locations in the human body. Density evaluation based on DEXA also lacks standardization, especially in measuring hard and soft tissues. There are large discrepancies in the measurement results from different DEXA instrument manufacturers [4]. Furthermore, DEXA is not always available in certain hospitals or clinics. Hence, a diagnostic alternative using different modalities, e.g., utilizing CT scans, is needed to cover the limitations of the existing method.  
Bone quality is crucial to a person's health, especially in supporting the function of the body's skeleton. Low-quality bones due to diseases such as osteoporosis result in bone fragility, which causes the bones to become severely vulnerable to fractures from intentional and unintentional excessive loading during daily activities. Osteoporosis is a systemic skeletal disease indicated by a decrease in bone density. This condition causes the bones to become more brittle and imposes a high risk of bone fractures. Osteoporosis can affect all types of bones, including the femur, spine, and wrist. Osteoporosis is found in about 200 million patients, with a higher potential for bone fractures in women [5].  
The risk factor for fractures in osteoporosis generally increases with age and gender, particularly in post-menopausal women, people with low body weight, and people with a history of parental fractures. Osteoporosis can be diagnosed by looking at bone fragility using dual energy X-ray absorptiometry (DEXA), which is the main standard for diagnosing osteoporosis without fractures. However, it is widely reported that DEXA scans of diagnosed patients with fractures reveal normal BMD (bone mineral density), whereas diagnoses based on CT scans indicate osteoporosis. This shows that there is still room for the sensitivity of DEXA to be improved for better osteoporosis detection. It is inherently necessary to have alternative scanning techniques that are more sensitive than the existing technique for the early prognosis of osteoporosis.  
The ability to diagnose osteoporosis earlier on can reduce the risk of bone fractures. Effective medical action can be undertaken if the symptoms of osteoporosis are readily identified and the patient exercises proper caution. However, early detection of osteoporosis symptoms is sometimes difficult due to limited diagnostic techniques from existing technology. Lack of sensitivity from the current imaging techniques and limited imaging data limits the ability to detect the quality of bone directly. The widely utilized method that directly indicates bone quality is executed by reading the grayscale level; this level is known as the Hounsfield unit (HU) on CT scan data. However, this HU reading is often ambiguous, as it is not very sensitive to indicating bone quality, more specifically in cases involving osteoporosis.  
Thus, this study aims to develop an image attribute to differentiate normal bone from osteoporosis bone which can be used as an alternative technology and that is safe for detecting osteoporosis and helping AI/ML studies in the future for automated identification of the disease. The success of this technology is expected to help doctors enforce the necessary medical measures earlier to treat osteoporoporotic patients.