Quantificação de Elementos do Tecido Pancreático Através da Segmentação com Superpíxeis e Graph-Cut
Abstract
Digital images obtained through microscopes are important sources of information used for various activities, some of them involving the diagnosis of diseases and the study of treatments. Thus, Digital Image Processing (DIP) techniques are tools that assist in this data collection. In this context, this work involved the development of a system for quantification of microscopic images of pancreatic tissue. Among the technologies used are Python programming language, Visual Code Studio text editor, and Tkinter, OpenCV, Pillow and SciPy libraries. Furthermore, to validate the system, the results of islets areas obtained by PySG (Python SLIC Graph-Cut – developed system) and ImageJ, an open access software widely used in biological sciences for DIP, were compared. In both systems, the islets were contoured in the images, creating a mask from which the results were extracted. Then, a statistical hypothesis testing was performed, obtaining a p-value of 0.4849 and reaching the conclusion that the developed system was functional and had a similar performance to another system with the same purpose.
Keywords:
Histologia, Processamento de Imagem Assistida por Computador, Simple Linear Iterative Clustering
References
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M. T. McCann, "Tools for Automated Histology Image Analysis," Ph.D dissertation, Depart. of Biomed. Eng., Carnegie Mellon Univ., Pittsburgh, PA, 2015.
M. T. McCann el al., "Automated Histology Analysis," IEEE Signal Processing Mag., vol. 32, no. 1, pp. 75-87, Jan 2015.
L. M. Rato, "Analysis of pancreas histological images for glucose intolerance identification using ImageJ - preliminary results," in Comput. Vision and Medical Image Process. IV, 1st ed., Boca Raton, U.S.: CRC Press, 2013, pp. 319-322.
F. N. Lima, "Contagem automática de micronúcleos em células de peixes," Trabalho de Conclusão de Curso, Fac. de Ciênc. Exatas e Tec. (Universidade Federal da Grande Dourados), Dourados, MS, 2016.
C. R. M. Maurício, "Contador de células vermelhas baseado em imagens para múltiplas espécies de animais silvestres e domésticos," Tese de Doutorado, Prog. de Pós-Grad. em Eng. Elétr. e Info. Ind., Univ. Tec. Fed. do Paraná, Curitiba, PR, 2017.
J. C. Klock, "Montagem de cariótipo de peixes assistida por computador," Dissertação de Mestrado, Prog. de Pós-Grad. em Eng. Elétr. e Info. Ind., Univ. Tec. Fed. do Paraná, Curitiba (PR), 2017.
A. A. Siddiqi, "Early skin tumor detection from microscopic images through image processing," in Mehran University Research Journal of Engineering & Technology, vol. 36, no.4, 2016.
A. Damian, "Morphological studies of the pancreas in the white wistar rat," in Bulletin of University of Agricultural Sciences and Veterinary Medicine, vol. 67, no. 1, 2010.
J. M. R. Eulálio el al., "Critical analysis and systematization of rat pancreatectomy terminology," in Acta Cirúrgica Brasileira, vol. 31, no. 10, pp. 698-74, 2016.
A. Haligür, E. Karakurum, and G. Dilek, "Morphological aspects of the pancreasin the rat and the rabbit: An investigation into the location, ducts, arteries and veins," in Mehmet Akif Ersoy 'Universitesi Veteriner Fakültesi Dergisi, 2018.
D. S. Longnecker, "Anatomy and histology of the pancreas," in The Pancreapedia: Exocrine Pancreas Knowledge Base (American Pancreatic Association), pp. 1-16, 2014.
C. B. C. Buzato, S. Arana, and C. P de F. Carvalho, "Histologia do Fígado, Vias Biliares e Pâncreas," in Sistema Digestório: Integração Básico-Clínica, São Paulo, BR: Blucher, 2016, ch. 14, pp. 335-368.
M. G. Mense, and T. J. Rosol, "Endocrine pancreas," in Boorman's Pathology of the Rat, Cambridge, U.K.: Accademic Press, 2017, ch. 35, pp. 695-704.
K. J. Millman, and M. Aivazis, "Python for scientists and engineers," in Computing in Science & Engineering, vol. 13, no. 2, pp. 9-12, 2011.
C. J. da Silva, "Visual Code Studio," in Angular 5 Projects, Berkeley: Apress, 2018, pp. 57-68.
R. Achanta et al., "SLIC superpixels," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, 2012.
E. B. Alexandre, "FT-SLIC: Geração de Superpixels com Base em Agrupamento Iterativo Linear Simples e Transformada Imagem-Floresta," Dissertação de Mestrado, Prog. de Pós-Grad. Em Ciênc. da Comp., Inst. de Mat. e Estat. da Univ. de São Paulo, São Paulo (SP), 2017.
X. Wang, P. Ma, and J. Zhao. "Brain tumor CT image segmentation based on slic0 superpixels," in 2016 9th Int. Congress on Image and Signal Processing, BioMed. Eng. and Informatics (CISP-BMEI), 2016.
X. Chen, and L. Pan, "A survey of graph cuts/graph search based medical image segmentation," in IEEE Reviews in Biomedical Engineering, vol. 11, p 112-124, 2018.
M. Basso, "Segmentação baseado em grafos (grab-cut) aplicado em imagens LiDAR para a extração semi-automática de edificações," in Anais do XVII Simpósio Brasileiro de Sensoriamento Remoto - SBSR, pp. 7443-7447, 2015.
C. T. Rueden et al., "ImageJ2: ImageJ for the next generation of scientific image data," in BMC Bioinformatics, vol. 18, no. 1, 2017.
H. A. Miot, "Avaliação da normalidade dos dados em estudos clínicos e experimentais," in Jornal Vascular Brasileiro, vol. 16, no. 2, pp. 88-91, 2017.
G. M. Campos, "Estatística Prática para Docentes e Pós-Graduandos: A escolha do teste mais adequado," 2000, Disponível em: <https://www.forp.usp.br/restauradora/gmc/gmclivro/gmclivrocap14.html>. Acesso em: 2 de setembro de 2021.
S. Niknamian, "The Prime Cause of type-2 (T2D), Type-1 Diabetes (T1D) and the Relation Between Diabetes and Cancer," Disponível em: <https://osf.io/nhwfe/>. Acesso em: 28 de agosto de 2020.
D. B. Durón et al., "Pancreatitis Aguda: Evidencia Actual," in Arch. de Med., vol. 14, no. 1, pp. 1-10, 2018.
G. A. Ródenas, M. M. Tornero, and F. C. Álvarez, "Pancreatitis crónica," in Medicine - Programa. de Formacíon Médica Continuada Acreditado., vol. 12, no. 8, pp. 421-429, 2016.
M. Soldan, "Rastreamento do câncer do pâncreas," in Revista do Colégio Brasileiro de Cirurgiões, vol. 44, no. 2, pp. 109-111, 2017.
H. Pedrini, and W. R. Schwartz, "Histologia do Fígado, Vias Biliares e Pâncreas," in Análise de imagens digitais: Princípios, Algoritmos e Aplicações, São Paulo, BR: Thomson Learning, 2018.
U. Rani, and A. Amsini. "Image processing techniques used in digital pathology imaging: An overview," in International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), vol. 5, no. 1, 2018.
J. Mikulka. "Imagej plug-ins for microscopic image processing," in 34th InternationalConference on Telecommunications and Signal Processing (TSP). Budapest, Hungary: IEEE, 2011. p. 537-540.
J. Mikulka, R. Burget, and K. Ríha. "Methods for the evaluation of qualityin machine processing of biomedical images," in 36th International Conference on Telecommunications and Signal Processing (TSP). Rome, Italy: IEEE, 2013.
S. Çayir et al., "Segmentation of the main structures in Hematoxylin and Eosin images," in 2018 26th Signal Processing and Communications Applications Conference (SIU), May 2018.
L. Farias, and F. Queiroz, "Detecção e contagem automática de podócitos por segmentação de cor em imagens microscópicas," in Anais do XVIII Escola Regional de Computação; Bahia, Alagoas e Sergipe, SBC, p. 229-237, 2018.
Y, Fujita. et al., "Evaluation of pancreatic fibrosis with acoustic radiation force impulse imaging and automated quantification of pancreatic tissue components," Pancreas, vol. 47, no. 10, p. 1277-1282, 2018.
H. Lee, and J. Kim. "Segmentation of overlapping cervical cells in microscopic images with superpixel partitioning and cell-wise contour refinement," in 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016.
Published
2021-10-13
How to Cite
ZHU, Kame Haung; PERES, Fabiana Frata Furlan; MAURICIO, Claudio Roberto Marquetto.
Quantificação de Elementos do Tecido Pancreático Através da Segmentação com Superpíxeis e Graph-Cut. In: LATIN AMERICAN CONGRESS ON FREE SOFTWARE AND OPEN TECHNOLOGIES (LATINOWARE), 18. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 87-93.
DOI: https://doi.org/10.5753/latinoware.2021.19910.
