Abstract
Algorithms based on the local description of interest regions are well adapted to the task of detecting and matching equivalent points between two images. Classical descriptors such as SIFT or SURF are efficient when applied to regular images with rich information. When it comes to medical images, these algorithms are not longer applicable without adaptation. For this reason, we propose in this paper a feature-based framework for the detection of objects in medical images with poor information (e.g. X-Ray images). Our approach is based on a modified version of SURF. In order to illustrate our purpose, we apply our framework to the cervical vertebra detection on X-Ray images. The results show that this modified descriptor is an efficient solution in the medical domain. It allows to properly process the vertebra detection in better computing times than other classical descriptors.
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References
Bauer, J., Sünderhauf, N., Protzel, P.: Comparing Several Implementations of Two Recently Published Feature Detectors. In: International Conference on Intelligent and Autonomous Systems (2007)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Casciaro, S., Massoptier, L.: Automatic Vertebral Morphometry Assessment. In: 28th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 5571–5574 (2007)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: International Conference on Computer Vision & Pattern Recognition, vol. 2, pp. 886–893 (2005)
Juan, L., Gwon, O.: A Comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing 3(4), 143–152 (2009)
Lecron, F., Benjelloun, M., Mahmoudi, S.: Points of Interest Detection in Cervical Spine Radiographs by Polygonal Approximation. In: International Conference on Image Processing Theory, Tools and Applications, pp. 81–86. IEEE (2010)
Lecron, F., Benjelloun, M., Mahmoudi, S.: Fully Automatic Vertebra Detection in X-Ray Images Based on Multi-Class SVM. In: Proceedings of the SPIE Medical Imaging, Image Processing 2010, vol. 8314 (2012)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence 27(10), 1615–1630 (2005)
Moradi, M., Abolmaesoumi, P., Mousavi, P.: Deformable registration using scale space keypoints. In: Proceedings of the SPIE Medical Imaging: Image Processing 2006, vol. 6144 (2006)
Sargent, D., Chen, C.I., Tsay, C.M., Wang, Y.F., Koppel, D.: Feature Detector and Descriptor for Medical Images. In: Proceedings of the SPIE Medical Imaging: Image Processing 2009, vol. 7259 (2009)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (1995)
Yang, M., Yuan, Y., Li, X., Yan, P.: Medical Image Segmentation Using Descriptive Image Features. In: Proceedings of the British Machine Vision Conference, pp. 94.1–94.11 (2011)
Zhi, L.-J., Zhang, S.-M., Zhao, D.-Z., Zhao, H., Lin, S.-K.: Medical Image Retrieval Using SIFT Feature. In: 2nd International Congress on Image and Signal Processing, CISP 2009 (2009)
Zhou, H., Miller, P., Zhang, J.: Age classification using Radon transform and entropy based scaling SVM. In: Proceedings of the British Machine Vision Conference, pp. 1–28 (2011)
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Lecron, F., Benjelloun, M., Mahmoudi, S. (2012). Descriptive Image Feature for Object Detection in Medical Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_39
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DOI: https://doi.org/10.1007/978-3-642-31298-4_39
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