Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

Area of Science:

• Medical Imaging
• Computer-Aided Diagnosis
• Bone Disease Research

Background:

• Osteoporosis is a bone disease characterized by decreased mineral density, leading to increased fracture risk.
• Manual segmentation of medical images for osteoporosis diagnosis is inefficient and time-consuming.
• Computer-aided diagnosis (CAD) systems offer efficient, timely, and cost-effective disease diagnosis.

Purpose of the Study:

• To survey vision-based CAD systems for osteoporosis diagnosis.
• To focus on advanced segmentation techniques for pathological bone disease.
• To review image acquisition, feature extraction, and classification methods for osteoporosis.

Main Methods:

• Review of image acquisition techniques relevant to osteoporosis diagnosis.
• Discussion of machine learning paradigms for image segmentation in osteoporosis.
• Brief description of feature extraction and classification methods.

Main Results:

• Automatic segmentation improves the efficiency and accuracy of diagnosing osteoporosis from medical images.
• Advanced machine learning techniques show promise in enhancing segmentation accuracy.
• The paper provides a comprehensive overview of current CAD approaches for osteoporosis.

Conclusions:

• Vision-based CAD systems, particularly those employing advanced segmentation, are crucial for early and accurate osteoporosis diagnosis.
• Further research into proposed architectures can lead to improved diagnostic capabilities.
• Enhanced CAD systems can significantly improve treatment outcomes and patient survival rates for osteoporosis.