Abstract
This work proposes a new method analyzing the shape of connected components (blobs) from segmented images for the classification of colonic polyps. The segmentation algorithm is a novel variation of the fast level lines transform and the resultant blobs are ideal to model the pit pattern structure of the mucosa. The shape of the blobs is described by a mixture of new features (convex hull, skeletonization and perimeter) as well as already proven features (contrast feature). We show that shape features of blobs extracted by segmenting an image are particularly suitable for mucosal texture classification and outperforming commonly used feature extraction methods.
Additionally this work compares and analyzes the influences of image enhancement technologies to the automated classification of the colonic mucosa. In particular, we compare the conventional chromoendoscopy with the computed virtual chromoendoscopy (the i-Scan technology of Pentax). Results imply that computed virtual chromoendoscopy facilitates the discrimination between healthy and abnormal mucosa, whereas conventional chromoendoscopy rather complicates the discrimination.
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References
Kiesslich, R.: Advanced imaging in endoscopy. Eur. Gastroenterol. Hepatol. Rev. 5, 22–25 (2009)
Kodashima, S., Fujishiro, M.: Novel image-enhanced endoscopy with i-scan technology. World J. Gastroenterol. 16, 1043–1049 (2010)
Häfner, M., Kwitt, R., Uhl, A., Gangl, A., Wrba, F., Vécsei, A.: Feature-extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images. Pattern Anal. Appl. 12, 407–413 (2009)
Tamaki, T., Yoshimuta, J., Kawakami, M., Raytchev, B., Kaneda, K., Yoshida, S., Takemura, Y., Onji, K., Miyaki, R., Tanaka, S.: Computer-aided colorectal tumor classification in NBI endoscopy using local features. Med. Image Anal. 17, 78–100 (2013)
Kudo, S.E., Hirota, S., Nakajima, T., Hosobe, S., Kusaka, H., Kobayashi, T., Himori, M., Yagyuu, A.: Colorectal tumours and pit pattern. J. Clin. Pathol. 47, 880–885 (1994)
Xia, G., Delon, J., Gousseau, Y.: Shape-based invariant texture indexing. Int. J. Comput. Vision 88, 382–403 (2010)
Monasse, P., Guichard, F.: Fast computation of a contrast invariant image representation. IEEE Trans. Image Process. 9, 860–872 (2000)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)
Varma, M., Garg, R.: Locally invariant fractal features for statistical texture classification. In: Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil (2007)
Xu, Q., Chen, Y.Q.: Multiscale blob features for gray scale, rotation and spatial scale invariant texture classification. In: Proceedings of 18th International Conference on Pattern Recognition (ICPR), vol. 4, pp. 29–32 (2006)
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This work is partially supported by the Austrian Science Fund, TRP Project 206.
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Häfner, M., Uhl, A., Wimmer, G. (2014). A Novel Shape Feature Descriptor for the Classification of Polyps in HD Colonoscopy. 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_20
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DOI: https://doi.org/10.1007/978-3-319-05530-5_20
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