Abstract
In this paper, we propose a novel method for image labeling of colorectal Narrow Band Imaging (NBI) endoscopic images based on a tree of shapes. Labeling results could be obtained by simply classifying histogram features of all nodes in a tree of shapes, however, satisfactory results are difficult to obtain because histogram features of small nodes are not enough discriminative. To obtain discriminative subtrees, we propose a method that optimally selects discriminative subtrees. We model an objective function that includes the parameters of a classifier and a threshold to select subtrees. Then labeling is done by mapping the classification results of nodes of the subtrees to those corresponding image regions. Experimental results on a dataset of 63 NBI endoscopic images show that the proposed method performs qualitatively and quantitatively much better than existing methods.
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References
Cancer Research, U.K.: Bowel cancer statistics (2015). http://www.cancerresearchuk.org/cancer-info/cancerstats/types/bowel/. Accessed 7 Aug 2016
Meining, A., Rösch, T., Kiesslich, R., Muders, M., Sax, F., Heldwein, W.: Inter- and intra-observer variability of magnification chromoendoscopy for detecting specialized intestinal metaplasia at the gastroesophageal junction. Endoscopy 36, 160–164 (2004)
Mayinger, B., Oezturk, Y., Stolte, M., Faller, G., Benninger, J., Schwab, D., Maiss, J., Hahn, E.G., Muehldorfer, S.: Evaluation of sensitivity and inter- and intra-observer variability in the detection of intestinal metaplasia and dysplasia in barrett’s esophagus with enhanced magnification endoscopy. Scand. J. Gastroenterol. 41, 349–356 (2006)
Oba, S., Tanaka, S., Oka, S., Kanao, H., Yoshida, S., Shimamoto, F., Chayama, K.: Characterization of colorectal tumors using narrow-band imaging magnification: combined diagnosis with both pit pattern and microvessel features. Scand. J. Gastroenterol. 45, 1084–1092 (2010)
Takemura, Y., Yoshida, S., Tanaka, S., Kawase, R., Onji, K., Oka, S., Tamaki, T., Raytchev, B., Kaneda, K., Yoshihara, M., Chayama, K.: Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest. Endosc. 75, 179–185 (2012)
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)
Kanao, H., Tanaka, S., Oka, S., Hirata, M., Yoshida, S., Chayama, K.: Narrow-band imaging magnification predicts the histology and invasion depth of colorectal tumors. Gastrointest. Endosc. 69, 631–636 (2009)
Kominami, Y., Yoshida, S., Tanaka, S., Sanomura, Y., Hirakawa, T., Raytchev, B., Tamaki, T., Koide, T., Kaneda, K., Chayama, K.: Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest. Endosc. 83, 643–649 (2016)
Hirakawa, T., Tamaki, T., Raytchev, B., Kaneda, K., Koide, T., Kominami, Y., Yoshida, S., Tanaka, S.: SVM-MRF segmentation of colorectal NBI endoscopic images. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4739–4742 (2014)
Xia, G.S., Delon, J., Gousseau, Y.: Shape-based invariant texture indexing. Int. J. Comput. Vis. 88, 382–403 (2010)
Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Trans. Image Process. 9, 860–872 (2000)
Gross, S., Kennel, M., Stehle, T., Wulff, J., Tischendorf, J., Trautwein, C., Aach, T.: Polyp segmentation in NBI colonoscopy. In: Meinzer, H.P., Deserno, T.M., Handels, H., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2009, pp. 252–256. Springer, Heidelberg (2009)
Ganz, M., Yang, X., Slabaugh, G.: Automatic segmentation of polyps in colonoscopic narrow-band imaging data. IEEE Trans. Biomed. Eng. 59, 2144–2151 (2012)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 898–916 (2011)
Collins, T., Bartoli, A., Bourdel, N., Canis, M.: Segmenting the uterus in monocular laparoscopic images without manual input. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 181–189. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24574-4_22
Bernal, J., Sánchez, J., Vilariño, F.: A region segmentation method for colonoscopy images using a model of polyp appearance. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 134–142. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21257-4_17
Hegadi, R.S., Goudannavar, B.A.: Interactive segmentation of medical images using grabcut. Int. J. Mach. Intell. 3, 168–171 (2011)
Breier, M., Gross, S., Behrens, A., Stehle, T., Aach, T.: Active contours for localizing polyps in colonoscopic NBI image data (2011)
Figueiredo, I.N., Figueiredo, P.N., Stadler, G., Ghattas, O., Araujo, A.: Variational image segmentation for endoscopic human colonic aberrant crypt foci. IEEE Trans. Med. Imaging 29, 998–1011 (2010)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)
Nosrati, M.S., Amir-Khalili, A., Peyrat, J.M., Abinahed, J., Al-Alao, O., Al-Ansari, A., Abugharbieh, R., Hamarneh, G.: Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors. Int. J. Comput. Assist. Radiol. Surg. 11, 1409–1418 (2016)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Liu, F., Lin, G., Shen, C.: CRF learning with CNN features for image segmentation. Pattern Recogn. 48, 2983–2992 (2015). Discriminative Feature Learning from Big Data for Visual Recognition
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1520–1528 (2015)
Jones, R.: Connected filtering and segmentation using component trees. Comput. Vis. Image Underst. 75, 215–228 (1999)
Najman, L., Couprie, M.: Building the component tree in quasi-linear time. IEEE Trans. Image Process. 15, 3531–3539 (2006)
Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans. Image Process. 9, 561–576 (2000)
Cousty, J., Najman, L.: Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 272–283. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21569-8_24
Xu, Y., Géraud, T., Najman, L.: Two applications of shape-based morphology: blood vessels segmentation and a generalization of constrained connectivity. In: Hendriks, C.L.L., Borgefors, G., Strand, R. (eds.) ISMM 2013. LNCS, vol. 7883, pp. 390–401. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38294-9_33
Dufour, A., Tankyevych, O., Naegel, B., Talbot, H., Ronse, C., Baruthio, J., Dokládal, P., Passat, N.: Filtering and segmentation of 3D angiographic data: advances based on mathematical morphology. Med. Image Anal. 17, 147–164 (2013)
Perret, B., Collet, C.: Connected image processing with multivariate attributes: an unsupervised Markovian classification approach. Comput. Vis. Image Underst. 133, 1–14 (2015)
Liu, G., Xia, G.S., Yang, W., Zhang, L.: Texture analysis with shape co-occurrence patterns. In: Pattern Recognition (ICPR), pp. 1627–1632 (2014)
He, C., Zhuo, T., Su, X., Tu, F., Chen, D.: Local topographic shape patterns for texture description. IEEE Sig. Process. Lett. 22, 871–875 (2015)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)
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This work was supported in part by JSPS KAKENHI grants numbers JP14J00223 and JP26280015.
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Hirakawa, T. et al. (2017). Discriminative Subtree Selection for NBI Endoscopic Image Labeling. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_44
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DOI: https://doi.org/10.1007/978-3-319-54427-4_44
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