VolHOG: A volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI. Issue 8 (31st July 2014)

• Record Type: Journal Article
• Authors: Daenzer, Stefan; Freitag, Stefan; von Sachsen, Sandra; Steinke, Hanno; Groll, Mathias; Meixensberger, Jürgen; Leimert, Mario
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2014-07-31
• Subjects: Magnetic resonance imaging -- MRI: anatomic, functional, spectral, diffusion -- Segmentation -- Musculoskeletal diseases; biomedical MRI -- bone -- deformation -- diseases -- feature extraction -- gradient methods -- image classification -- image segmentation -- learning (artificial intelligence) -- medical image processing -- neurophysiology -- noise -- object recognition -- physiological models -- spin‐spin relaxation -- support vector machines; machine learning algorithms -- bivariate histograms of oriented gradients; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Inference methods or devices; Medical magnetic resonance imaging -- Three dimensional image processing -- Three dimensional sensing -- Image detection systems -- Pathology -- Medical image contrast -- Anisotropy -- Researchers -- Medical image segmentation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4890587
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The automatic recognition of vertebrae in volumetric images is an important step toward automatic spinal diagnosis and therapy support systems. There are many applications such as the detection of pathologies and segmentation which would benefit from automatic initialization by the detection of vertebrae. One possible application is the initialization of local vertebral segmentation methods, eliminating the need for manual initialization by a human operator. Automating the initialization process would optimize the clinical workflow. However, automatic vertebra recognition in magnetic resonance (MR) images is a challenging task due to noise in images, pathological deformations of the spine, and image contrast variations. Methods: This work presents a fully automatic algorithm for 3D cervical vertebra detection in MR images. We propose a machine learning method for cervical vertebra detection based on new features combined with a linear support vector machine for classification. An algorithm for bivariate gradient orientation histogram generation from three‐dimensional raster image data is introduced which allows us to describe three‐dimensional objects using the authors' proposed bivariate histograms. Results: A detailed performance evaluation on 21 T2‐weighted MR images of the cervical vertebral region is given. A single model for cervical vertebrae C3–C7 is generated and evaluated. The results show that the generic model performs equally well for each of the cervical vertebrae C3–C7. The algorithm's performance is also evaluated on images containing various levels of artificial noise. The results indicate that the proposed algorithm achieves good results despite the presence of severe image noise. Conclusions: The proposed detection method delivers accurate locations of cervical vertebrae in MR images which can be used in diagnosis and therapy. In order to achieve absolute comparability with the results of future work, the authors are following an open data approach by making the image dataset used in their performance evaluation available to the public.