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Computational techniques to segment and classify lumbar compression fractures

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Abstract

Vertebral fractures are important indicators of osteoporosis. Fractures with partial collapse of vertebral bodies are referred to as vertebral compression fractures (VCFs) that are usually non-traumatic in nature. Some common causes of VCFs are trauma, bone failure related to osteoporosis (benign) and metastatic cancer (malignant). This paper aims at developing a system for computer-aided diagnosis to help in the detection, labeling and segmentation of lumbar vertebral body (VB) and to further classify each VB into normal, malignant and benign VCFs. After the initial preprocessing, morphological, shape and angular features are used in the detection, labeling and segmentation steps. Various shape and statistical texture features are extracted from the segmented VB and are fed to the classifier for the final decision. The segmentation and classification results obtained were compared with the ground truth manual segmentation of the lumbar VB and the decision labels of the fractures provided by the experts. The dice similarity coefficient (DSC) for segmentation reached up to 94.27%, and the classification results show that shape and texture features together are able to correctly classify with an accuracy rate of 95.34%. The final outcomes are expected to be useful in the analysis of vertebral compression fractures.

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Acknowledgements

The first author would like to thank the University Grants Commission’s (UGC) Maulana Azad National Fellowship (MANF) for funding the present work (Grant Number F1-17.1/2014-15/MANF-2014-15-CHR-KAR-46275/(SA III/Website) dated Feb 2015).

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  1. Department of Studies in Computer Science, University of Mysore, Manasagangotri, Mysore, Karnataka, 570006, India

    Adela Arpitha & Lalitha Rangarajan

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  1. Adela Arpitha
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  2. Lalitha Rangarajan
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Correspondence to Adela Arpitha.

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Adela Arpitha, Rangarajan, L. Computational techniques to segment and classify lumbar compression fractures. Radiol med 125, 551–560 (2020). https://doi.org/10.1007/s11547-020-01145-7

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