Fully automatic vertebra detection in x-ray images based on multi-class SVM

Fabian Lecron; Mohammed Benjelloun; Saïd Mahmoudi

doi:10.1117/12.911424

14 February 2012 Fully automatic vertebra detection in x-ray images based on multi-class SVM

Fabian Lecron, Mohammed Benjelloun, Saïd Mahmoudi

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83142D (2012) https://doi.org/10.1117/12.911424
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Automatically detecting vertebral bodies in X-Ray images is a very complex task, especially because of the noise and the low contrast resulting in that kind of medical imagery modality. Therefore, the contributions in the literature are mainly interested in only 2 medical imagery modalities: Computed Tomography (CT) and Magnetic Resonance (MR). Few works are dedicated to the conventional X-Ray radiography and propose mostly semi-automatic methods. However, vertebra detection is a key step in many medical applications such as vertebra segmentation, vertebral morphometry, etc. In this work, we develop a fully automatic approach for the vertebra detection, based on a learning method. The idea is to detect a vertebra by its anterior corners without human intervention. To this end, the points of interest in the radiograph are firstly detected by an edge polygonal approximation. Then, a SIFT descriptor is used to train an SVM-model. Therefore, each point of interest can be classified in order to detect if it belongs to a vertebra or not. Our approach has been assessed by the detection of 250 cervical vertebræ on radiographs. The results show a very high precision with a corner detection rate of 90.4% and a vertebra detection rate from 81.6% to 86.5%.

Citation Download Citation

Fabian Lecron, Mohammed Benjelloun, and Saïd Mahmoudi "Fully automatic vertebra detection in x-ray images based on multi-class SVM", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142D (14 February 2012); https://doi.org/10.1117/12.911424

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