Automatic Detection of Erythrocytes in Fishes using Clustering Segmentation and Supervised Learning
Resumo
The growth of urban areas and the population has favored the increase in pollution and consequently the contamination of river waters. This clue has aroused interest in several aspects, mainly related to the fate and possible effects that these contaminants can cause to human health. The analysis of erythrocytes in fish is an efficient mechanism to identify the presence of genetical terations that maybe being caused by emergent contaminants. This article presents a new proposal for automatic identification of erythrocytes in fish using SLIC segmentation approach and connected components, adjusted using supervised learning, and presenting the performance evaluation in different aspects of the image.
Palavras-chave:
Erythrocytes, Clustering Segmentation, Supervised Learning
Referências
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J. M. Bewes, N. Suchowerska, and D. R. McKenzie, Automated cell colony counting and analysis using the circular hough image transform algorithm (chita), Physics in Medicine Biology, vol. 53, no. 21, p. 5991, [Online]. Available: http://stacks.iop.org/0031-9155/53/i= 21/a=007
J. Byun, M. R. Verardo, B. Sumengen, G. P. Lewis, B. Manjunath, and S. K. Fisher, Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images, Mol Vis, vol. 12, no. 105 - 07, pp. 949 - 60.
K. Souza, A. Pinheiro, W. Amorim, and V. Odakura, Contagem automática de unidades formadoras de colonias de bactérias em placas de petri com o uso do algoritmo template matching.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Su¨sstrunk, Slic superpixels compared to state-of-the-art superpixel methods, IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 11, pp. 2274 - 2282.
A. Tre ´meau and P. Colantoni, Regions adjacency graph applied to color image segmentation, IEEE Transactions on image processing, vol. 9, no. 4, pp. 735 - 744, DOI: 10.1109/83.841950
P. F. Felzenszwalb and D. P. Huttenlocher, Efficient graph-based image segmentation, International journal of computer vision, vol. 59, no. 2, pp. 167 - 181, DOI: 10.1023/b:visi.0000022288.19776.77
R.-F. Chang, C.-J. Chen, and C. -H. Liao, Region-based image retrieval using edgeflow segmentation and region adjacency graph, in Multimedia and Expo, ICME' 04. 2004 IEEE International Conference on, vol. 3. IEEE, 2004, pp. 1883 - 1886. DOI: 10.1109/icme.2004.1394626
P. Janaina, Deteccção da citotoxicidade, genotoxicidade e mutagenicidade, do inseticida fipronil no organismo teste Allium cepa. Dissertação, Instituto de Biociências, Universidade Estadual Paulista.
K. Saarinen, Color image segmentation by a watershed algorithm and region adjacency graph processing, in Image processing, Proceedings. ICIP- 94., IEEE international conference, vol. 3. IEEE, 1994, pp. 1021 - 1025. DOI: 10.1109/icip.1994.413690
F. Chung and L. Lu, Connected components in random graphs with given expected degree sequences, Annals of combinatorics, vol. 6, no. 2, pp. 125 - 145, DOI: 10.1007/pl00012580
F. Jurie and M. Dhome, A simple and efficient template matching algorithm, in Computer Vision, ICCV 2001. Proceedings. Eighth IEEE International Conference on, vol. 2. IEEE, 2001, pp. 544 - 549. DOI: 10.1109/iccv.2001.937673
J. Shu, H. Fu, G. Qiu, P. Kaye, and M. Ilyas, Segmenting overlapping cell nuclei in digital histopathology images, in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013, pp. 5445 - 5448.
N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, in Computer Vision and Pattern Recognition, CVPR 2005. IEEE Computer Society Conference on, vol. 1. IEEE, 2005, pp. 886 - 893. DOI: 10.1109/cvpr.2005.177
A. K. C. Wong and M. A. Vogel, Textural features for image classification, IEEE Transactions on systems, man, and cybernetics, no. 6, pp. 610 - 621, DOI: 10.1109/tsmc.1973.4309314
Publicado
09/09/2019
Como Citar
LEITE, Kleyton Sartori; DA SILVA, Felipe Gomes; CRISPIM, Bruno do Amaral; MEREY, Felipe; GRISOLIA, Alexeia Barufatti; AMORIM, Willian Paraguassu.
Automatic Detection of Erythrocytes in Fishes using Clustering Segmentation and Supervised Learning. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 7-12.
DOI: https://doi.org/10.5753/wvc.2019.7620.
