Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture.

Graphical abstract

计算机辅助诊断（CAD）彻底改变了医学诊断领域。它们通过以有效，及时和具有成本效益的方式对疾病进行早期诊断，有助于提高治疗潜力并提高生存率。自动分割已使放射科医生成功地分割了感兴趣的区域，从而改善了从医学图像中诊断疾病的能力，而手动分割不可能如此有效。本文的目的是调查基于视觉的CAD系统，尤其关注于被称为骨质疏松症的病理性骨疾病的分割技术。骨质疏松症是骨骼的一种状态，骨骼的矿物质密度降低并且变得多孔，使骨骼容易因小伤或跌倒而容易骨折。这篇文章介绍了用于骨质疏松症诊断的医学图像的图像获取技术。本文还讨论了用于骨质疏松症疾病细分的高级机器学习范例。还简要描述了骨质疏松症中的其他图像处理步骤，例如特征提取和分类。最后，本文提出了改善骨质疏松症诊断的未来方向，并提出了所提出的架构。

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