Early Diagnosis of Osteoporosis Using Active Appearance Model and Metacarpal Radiogrammetry

M. Sam, Anu Shaju Areeckal, D. S. Sumam
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引用次数: 6

Abstract

Osteoporosis is a condition of fragile bone with an increased susceptibility to fracture. Since the gold standard method used for the diagnosis of osteoporosis, Dual X-ray Absorptiometry (DXA), is expensive and not widely available in low economies, there is a need for low cost approaches to detect bone loss in people. A new automated radiogrammetric method for early diagnosis of osteoporosis from a single hand radiograph is proposed. In this technique, the third metacarpal bone is segmented from hand X-ray images using Active Appearance Models (AAM). Points of interest acquired from the segmented bone are used to take radiogrammetric measurements, from which bone indices are calculated. Data used in this work was acquired from 138 subjects in two hospitals in India. Significant radiogrammetric features were selected using statistical analysis. The bone indices are observed to be significantly correlated with Bone Mineral Density (BMD) of the lumbar spine measured using DXA. Different classification models were trained using the significant features. The results obtained are promising and can be used as a cost effective diagnostic tool for early detection of osteoporosis.
活动外观模型与掌骨放射测量早期诊断骨质疏松症
骨质疏松症是一种易碎的骨骼,易发生骨折。由于用于诊断骨质疏松症的金标准方法——双x射线吸收测定法(DXA)价格昂贵,而且在低经济体中没有广泛使用,因此需要低成本的方法来检测人们的骨质流失。提出了一种新的单手x线片早期诊断骨质疏松症的自动放射测量方法。在这项技术中,使用主动外观模型(AAM)从手部x射线图像中分割第三掌骨。从分段骨中获得的兴趣点用于进行放射测量,从中计算骨指数。本研究使用的数据来自印度两家医院的138名受试者。通过统计分析选择显著的放射测量特征。观察到骨指数与DXA测量的腰椎骨矿物质密度(BMD)显著相关。利用显著性特征训练不同的分类模型。所获得的结果是有希望的,可以作为一种成本有效的早期发现骨质疏松症的诊断工具。
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