Abstract
In this work we introduce a curvature based shape feature extraction technique. To construct the proposed descriptor, first an input color channel is subject to edge detection and gradient computations. Then, based on the gradient map and edge map, the local curvature of the contour is computed for each pixel as the angular difference between the maximum and minimum gradient angle within a certain neighborhood. Experiments show, that the feature is competitive as far as the classification rate is concerned. Despite its discriminative power, a further positive aspect is the compactness of the feature vector.
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Notes
- 1.
The MATLAB reference implementation can be downloaded from http://www.wavelab.at/sources/Gadermayr12f.
- 2.
The function \(\mathrm{atan2 }\) denotes the four-quadrant implementation of the \(\mathrm{atan }\)-function.
References
Fasano, A., Berti, I., Gerarduzzi, T., Not, T., Colletti, R.B., Drago, S., Elitsur, Y., Green, P.H.R., Guandalini, S., Hill, I.D., Pietzak, M., Ventura, A., Thorpe, M., Kryszak, D., Fornaroli, F., Wasserman, S.S., Murray, J.A., Horvath, K.: Prevalence of celiac disease in at-risk and not-at-risk groups in the united states: a large multicenter study. Arch. Intern. Med. 163, 286–292 (2003)
Liedlgruber, M., Uhl, A.: Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE Rev. Biomed. Eng. 4, 73–88 (2012)
Ciaccio, E.J., Tennyson, C.A., Lewis, S.K., Krishnareddy, S., Bhagat, G., Green, P.H.: Distinguishing patients with celiac disease by quantitative analysis of video capsule endoscopy images. Comput. Methods Programs Biomed. 100(1), 39–48 (2010)
Vécsei, A., Fuhrmann, T., Uhl, A.: Towards automated diagnosis of celiac disease by computer-assisted classification of duodenal imagery. In: Proceedings of the 4th International Conference on Advances in Medical, Signal and Information Processing (MEDSIP ’08), Santa Margherita Ligure, Italy, pp. 1–4, paper no P2.1-009 (2008)
Vécsei, A., Fuhrmann, T., Liedlgruber, M., Brunauer, L., Payer, H., Uhl, A.: Automated classification of duodenal imagery in celiac disease using evolved fourier feature vectors. Comput. Methods Programs Biomed. 95, S68–S78 (2009)
Hegenbart, S., Kwitt, R., Liedlgruber, M., Uhl, A., Vécsei, A.: Impact of duodenal image capturing techniques and duodenal regions on the performance of automated diagnosis of celiac disease. In: Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis (ISPA ’09), Salzburg, Austria, September 2009, pp. 718–723 (2009)
Gschwandtner, M., Liedlgruber, M., Uhl, A., Vécsei, A.: Experimental study on the impact of endoscope distortion correction on computer-assisted celiac disease diagnosis. In: Proceedings of the 10th International Conference on Information Technology and Applications in Biomedicine (ITAB’10), Corfu, Greece, November 2010 (2010)
Häfner, M., Gangl, A., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Classification of endoscopic images using delaunay triangulation-based edge features. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 131–140. Springer, Heidelberg (2010)
Häfner, M., Gangl, A., Liedlgruber, M., Uhl, A., Vécsei, A., Wrba, F.: Endoscopic image classification using edge-based features. In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR’10), Istanbul, Turkey, August 2010, pp. 2724–2727 (2010)
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Recogn. Mach. Intell. 8(6), 679–698 (1986)
Gasbarrini, A., Ojetti, V., Cuoco, L., Cammarota, G., Migneco, A., Armuzzi, A., Pola, P., Gasbarrini, G.: Lack of endoscopic visualization of intestinal villi with the immersion technique in overt atrophic celiac disease. Gastrointest. Endosc. 57, 348–351 (2003)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
Rautkorpi, R., Iivarinen, J.: A novel shape feature for image classification and retrieval. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 753–760. Springer, Heidelberg (2004)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)
Everitt, B.: The Analysis of Contingency Tables. Chapman and Hall, London (1977)
Acknowledgment
This work is partially funded by the Austrian Science Fund (FWF) under Project No. 24366.
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Gadermayr, M., Liedlgruber, M., Uhl, A., Vécsei, A. (2014). Shape Curvature Histogram: A Shape Feature for Celiac Disease Diagnosis. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_17
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