×

DINOSARC: color features based on selective aggregation of chromatic image components for wireless capsule endoscopy. (English) Zbl 1431.92104

Summary: Wireless capsule endoscopy (WCE) is a noninvasive diagnostic technique enabling the inspection of the whole gastrointestinal (GI) tract by capturing and wirelessly transmitting thousands of color images. Proprietary software “stitches” the images into videos for examination by accredited readers. However, the videos produced are of large length and consequently the reading task becomes harder and more prone to human errors. Automating the WCE reading process could contribute in both the reduction of the examination time and the improvement of its diagnostic accuracy. In this paper, we present a novel feature extraction methodology for automated WCE image analysis. It aims at discriminating various kinds of abnormalities from the normal contents of WCE images, in a machine learning-based classification framework. The extraction of the proposed features involves an unsupervised color-based saliency detection scheme which, unlike current approaches, combines both point and region-level saliency information and the estimation of local and global image color descriptors. The salient point detection process involves estimation of ‘distances on selective aggregation of chromatic image components’ (DINOSARC). The descriptors are extracted from superpixels by coevaluating both point and region-level information. The main conclusions of the experiments performed on a publicly available dataset of WCE images are (a) the proposed salient point detection scheme results in significantly less and more relevant salient points; (b) the proposed descriptors are more discriminative than relevant state-of-the-art descriptors, promising a wider adoption of the proposed approach for computer-aided diagnosis in WCE.

MSC:

92C55 Biomedical imaging and signal processing

Software:

SIFT; SURF
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Koulaouzidis, A.; Iakovidis, D. K.; Karargyris, A.; Rondonotti, E., Wireless endoscopy in 2020: will it still be a capsule?, World Journal of Gastroenterology, 21, 17, 5119-5130, (2015) · doi:10.3748/wjg.v21.i17.5119
[2] Riphaus, A.; Richter, S.; Vonderach, M.; Wehrmann, T., Capsule endoscopy interpretation by an endoscopy nurse—a comparative trial, Zeitschrift für Gastroenterologie, 47, 3, 273-276, (2009) · doi:10.1055/s-2008-1027822
[3] Iakovidis, D. K.; Sarmiento, R.; Silva, J. S., Towards intelligent capsules for robust wireless endoscopic imaging of the gut, Proceedings of IEEE International Conference on Imaging Systems and Techniques (IST)
[4] Figueiredo, I. N.; Leal, C.; Pinto, L.; Figueiredo, P. N.; Tsai, R., Hybrid multiscale affine and elastic image registration approach towards wireless capsule endoscope localization, Biomedical Signal Processing and Control, 39, 486-502, (2018) · doi:10.1016/j.bspc.2017.08.019
[5] Iakovidis, D. K.; Koulaouzidis, A., Software for enhanced video capsule endoscopy: challenges for essential progress, Nature Reviews Gastroenterology and Hepatology, 12, 3, 172-186, (2015) · doi:10.1038/nrgastro.2015.13
[6] Iakovidis, D. K.; Koulaouzidis, A., Automatic lesion detection in wireless capsule endoscopy—a simple solution for a complex problem, Proceedings of IEEE International Conference on Image Processing (ICIP)
[7] Iakovidis, D. K.; Koulaouzidis, A., Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software, Gastrointestinal Endoscopy, 80, 5, 877-883, (2014) · doi:10.1016/j.gie.2014.06.026
[8] Karargyris, A.; Bourbakis, N., Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos, IEEE Transactions on Biomedical Engineering, 58, 10, 2777-2786, (2011) · doi:10.1109/tbme.2011.2155064
[9] Bay, H.; Ess, A.; Tuytelaars, T.; Van Gool, L., Speeded-up robust features (SURF), Computer Vision and Image Understanding, 110, 3, 346-359, (2008) · doi:10.1016/j.cviu.2007.09.014
[10] Yuan, Y.; Li, B.; Meng, M. Q.-H., Bleeding frame and region detection in the wireless capsule endoscopy video, IEEE Journal of Biomedical and Health Informatics, 20, 2, 624-630, (2016) · doi:10.1109/jbhi.2015.2399502
[11] Fu, Y.; Zhang, W.; Mandal, M.; Meng, M. Q.-H., Computer-aided bleeding detection in WCE video, IEEE Journal of Biomedical and Health Informatics, 18, 2, 636-642, (2014) · doi:10.1109/jbhi.2013.2257819
[12] Shi, W.; Chen, J.; Chen, H.; Peng, Q.; Gan, T., Bleeding fragment localization using time domain information for WCE videos, Proceedings of 8th International Conference on BioMedical Engineering and Informatics (BMEI)
[13] Liedlgruber, M.; Uhl, A., Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review, IEEE reviews in Biomedical Engineering, 4, 73-88, (2011) · doi:10.1109/rbme.2011.2175445
[14] Bernal, J.; Sánchez, F. J.; Fernández-Esparrach, G.; Gil, D.; Rodríguez, C.; Vilariño, F., WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians, Computerized Medical Imaging and Graphics, 43, 99-111, (2015) · doi:10.1016/j.compmedimag.2015.02.007
[15] Li, B.; Meng, M. Q.-H., Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection, IEEE Transactions on Information Technology in Biomedicine, 16, 3, 323-329, (2012) · doi:10.1109/TITB.2012.2185807
[16] Yuan, Y.; Li, B.; Meng, M. Q.-H., Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images, IEEE Transactions on Automation Science and Engineering, 13, 2, 529-535, (2016) · doi:10.1109/tase.2015.2395429
[17] Vasilakakis, M.; Iakovidis, D. K.; Spyrou, E.; Koulaouzidis, A.; Peters, T.; Yang, G.; Navab, N., Weakly-supervised lesion detection in video capsule endoscopy based on a bag-of-colour features model, Lecture Notes in Computer Science, 1-8, (2017), Berlin, Germany: Springer, Berlin, Germany
[18] Csurka, G.; Dance, C.; Fan, L.; Willamowski, J.; Bray, C., Visual categorization with bags of keypoints, Proceedings of Workshop on Statistical Learning in Computer Vision, ECCV
[19] Iakovidis, D. K.; Chatzis, D.; Chrysanthopoulos, P.; Koulaouzidis, A., Blood detection in wireless capsule endoscope images based on salient superpixels, Proceedings of Annual International Conference on IEEE Engineering in Medicine and Biology Society, EMBS
[20] Koulaouzidis, A.; Iakovidis, D. K.; Yung, D. E., KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes, Endoscopy International Open, 5, 6, E477-E483, (2017) · doi:10.1055/s-0043-105488
[21] Achanta, R.; Shaji, A.; Smith, K.; Lucchi, A.; Fua, P.; Süsstrunk, S., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 11, 2274-2282, (2012) · doi:10.1109/tpami.2012.120
[22] Lowe, D. G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, 91-110, (2004) · doi:10.1023/b:visi.0000029664.99615.94
[23] Fawcett, T., An introduction to ROC analysis, Pattern Recognition Letters, 27, 8, 861-874, (2006) · doi:10.1016/j.patrec.2005.10.010
[24] Wyszecki, G.; Stiles, W. S., Color Science, 8, (1982), New York, NY, USA: Wiley, New York, NY, USA
[25] Karkanis, S. A.; Iakovidis, D. K.; Maroulis, D. E.; Karras, D. A.; Tzivras, M., Computer-aided tumor detection in endoscopic video using color wavelet features, IEEE Transactions on Information Technology in Biomedicine, 7, 3, 141-152, (2003) · doi:10.1109/titb.2003.813794
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.