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Machine Vision and Applications
Published in: Machine Vision and Applications 1/2021

01-02-2021 | Original Paper

Hessian-polar context: a descriptor for microfilaria recognition

Authors: Faroq AL-Tam, António dos Anjos, Hamid Reza Shahbazkia

Published in: Machine Vision and Applications | Issue 1/2021

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Abstract

This paper presents a new effective descriptor for microfilaria. Since microfilaria is a thin elastic object, the proposed descriptor handles it locally. At each medial point of the microfilaria, the local structure of the microfilaria votes for a given shape. Accumulating these votes in the polar domain yields a rich descriptor. Experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods.
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Footnotes
Literature
1.
AL-Tam, F., dos Anjos, A., Pion, S., Boussinesq, M., Shahbazkia, H.R.: Microfilariae classification using multiple classifiers for color and shape features. Open Eng. 6(1), 2016 (2016)CrossRef
2.
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). San Francisco, CA, USA (2010)
3.
4.
Boussinesq, M.: Loiasis. Ann. Trop. Med. Parasitol. 100(8), 715–731 (2006)CrossRef
5.
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)CrossRef
6.
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)CrossRef
7.
Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. In: Suri, J., Setarehdan, S., Singh, S. (eds.) Advanced Algorithmic Approaches to Medical Image Segmentation, Advances in Computer Vision and Pattern Recognition, pp. 541–558. Springer, London (2002)
8.
D’Ambrosio, M.V., Bakalar, M., Bennuru, S., Reber, C., Skandarajah, A., Nilsson, L., Switz, N., Kamgno, J., Pion, S., Boussinesq, M., Nutman, T.B., Fletcher, D.A.: Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope. Sci. Transl. Med. 7(286), 286 (2015). https://​doi.​org/​10.​1126/​scitranslmed.​aaa3480CrossRef
9.
Figueiredo, M., Leitao, J.: A nonsmoothing approach to the estimation of vessel contours in angiograms. IEEE Trans. Med. Imaging 14(1), 162–172 (1995)CrossRef
10.
Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale vessel enhancement filtering. In: Wells, W., Colchester, A., Delp, S. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI-98. Lecture Notes in Computer Science, vol. 1496, pp. 130–137. Springer, Berlin (1998)
11.
Hladůvka, J., König, A., Gröller, E.: Exploiting eigenvalues of the Hessian matrix for volume decimation. Tech. Rep. TR-186-2-00-19 (2000)
12.
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)CrossRef
13.
14.
Li, K., Lu, Z., Liu, W., Yin, J.: Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake. Pattern Recognit. 45(4), 1255–1264 (2012)CrossRef
15.
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 465–470. San Francisco, CA, USA (1996)
16.
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef
17.
Meijster, A., Roerdink, J.B., Hesselink, W.H.: A general algorithm for computing distance transforms in linear time. In: Mathematical Morphology and its Applications to Image and Signal Processing, pp. 331–340. Springer (2002)
18.
Morard, V., Dokládal, P., Decenciere, E.: Parsimonious path openings and closings. IEEE Trans. Image Process. 23(4), 1543–1555 (2014)MathSciNetCrossRef
19.
Organization, W.H.: Bench aids for the diagnosis of filarial infections (1997)
20.
Peng, Y., Dharssi, S., Chen, Q., Keenan, T.D., Agrón, E., Wong, W.T., Chew, E.Y., Lu, Z.: Deepseenet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs. Ophthalmology 126(4), 565–575 (2019)CrossRef
21.
Perkins Simon, A.W., Wolfart, E.: Hypermedia Image Processing Reference. Wiley, New York (1996)
22.
Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)
23.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
24.
van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRef
25.
26.
Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)CrossRef
27.
Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)CrossRef
28.
Tankyevych, O., Talbot, H., Dokladal, P.: Curvilinear Morpho-Hessian filter. In: The 5th IEEE International Symposium on Biomedical Imaging: From Nano (2008)
29.
Telea, A., Van Wijk, J.J.: An augmented fast marching method for computing skeletons and centerlines. In: Proceedings of the symposium on Data Visualisation 2002, pp. 251–ff. Eurographics Association (2002)
30.
Vedaldi, A., Fulkerson, B.: Vlfeat: An open and portable library of computer vision algorithms. In: Proceedings of the International Conference on Multimedia, MM’10, pp. 1469–1472. ACM, New York, NY, USA (2010)
31.
Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 603–610 (2011)
32.
33.
Zhang, Y., Koydemir, H.C., Shimogawa, M.M., Yalcin, S., Guziak, A., Liu, T., Oguz, I., Huang, Y., Bai, B., Luo, Y., et al.: Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning. Light Sci. Appl. 7(1), 108 (2018)CrossRef
34.
Zhu, W., Liu, C., Fan, W., Xie, X.: Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 673–681 (2018)
35.
Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 603–611. Springer (2017)
Metadata
Title
Hessian-polar context: a descriptor for microfilaria recognition
Authors
Faroq AL-Tam
António dos Anjos
Hamid Reza Shahbazkia
Publication date
01-02-2021
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 1/2021
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-020-01154-6

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