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长骨 X 射线图像中的骨癌评估和破坏模式分析。

Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image.

机构信息

Department of Computer Science and Engineering, Indian Institute of Information Technology Kalyani, Kalyani, India.

Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, India.

出版信息

J Digit Imaging. 2019 Apr;32(2):300-313. doi: 10.1007/s10278-018-0145-0.

Abstract

Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-cost diagnostic tool for diagnosis and visualization of bone cancer. In this paper, a novel technique for the assessment of cancer stage and grade in long bones based on X-ray image analysis has been proposed. Cancer-affected bone images usually appear with a variation in bone texture in the affected region. A fusion of different methodologies is used for the purpose of our analysis. In the proposed approach, we extract certain features from bone X-ray images and use support vector machine (SVM) to discriminate healthy and cancerous bones. A technique based on digital geometry is deployed for localizing cancer-affected regions. Characterization of the present stage and grade of the disease and identification of the underlying bone-destruction pattern are performed using a decision tree classifier. Furthermore, the method leads to the development of a computer-aided diagnostic tool that can readily be used by paramedics and doctors. Experimental results on a number of test cases reveal satisfactory diagnostic inferences when compared with ground truth known from clinical findings.

摘要

骨癌起源于骨骼,迅速扩散到身体的其他部位,影响患者。骨癌的快速初步诊断始于分析骨骼 X 射线或 MRI 图像。与 MRI 相比,X 射线图像为诊断和可视化骨癌提供了一种低成本的诊断工具。在本文中,提出了一种基于 X 射线图像分析的长骨癌症分期和分级评估的新技术。受癌症影响的骨骼图像通常在受影响区域显示骨骼纹理的变化。融合了不同的方法来进行我们的分析。在提出的方法中,我们从骨骼 X 射线图像中提取某些特征,并使用支持向量机 (SVM) 来区分健康和癌变的骨骼。基于数字几何的技术用于定位受癌症影响的区域。使用决策树分类器对疾病的当前阶段和等级进行特征描述,并识别潜在的骨骼破坏模式。此外,该方法还开发了一种计算机辅助诊断工具,便于护理人员和医生使用。与临床发现的已知真实情况相比,在许多测试案例上的实验结果表明,该方法可以得出令人满意的诊断推论。

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