Segmentation of fish chromosomes in microscopy images: A new dataset
Resumo
The chromosome segmentation is the most important step in automatic karyotype assembling. In this work, we presented a brand new chromosome image dataset and proposed methods for segmenting the chromosomes. Chromosome images are usually low quality, especially fish chromosomes. In order to overcome this issue, we tested three filters to reduce noise and improve image quality. After filtering, we applied adaptive threshold segmentation combined with mathematical morphology and supervised classification methods. Support Vector Machine and k-nearest neighbors were applied to discriminate between chromosomes and image background. The proposed method was applied to segment chromosomes in a new dataset. To enable measure the performance of the methods all chromosomes were manually delineated. The results are evaluated considering the Hausdorff distance and normalized sum of distances between segmented and reference images.
Palavras-chave:
Fish karyotype, chromosome segmentation, computer vision, classification, new dataset
Referências
S. Minaee, M. Fotouhi, B. H. Khalaj, A Geometric Approach For Fully Automatic Chromosome Segmentation (2011) 1–8arXiv:1112. 4164.
N. Madian, K. B. Jayanthi, Overlapped chromosome segmentation and separation of touching chromosome for automated chromosome classification, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2012) 5392–5395.
O. M. Filho, L. A. C. Bertollo., Análise cromossômica de astyanax scabripinnis rivularis (characiformes, characidae) da regi˜ao Três Marias MG., Cienc. Cult (1986) 35:855.
A. Levan, K. Fredga, A. A. Sandberg, Nomenclature for centromeric position on chromosomes, Hereditas 52 (2) (1964) 201–220.
R. Manohar, J. Gawande, Watershed and clustering based segmentation of chromosome images, in: Advance Computing Conference (IACC), 2017 IEEE 7th International, IEEE, 2017, pp. 697–700.
D. Somasundaram, V. R. Vijay Kumar, Separation of overlapped chromosomes and pairing of similar chromosomes for karyotyping analysis, Measurement: Journal of the International Measurement Confederation 48 (1) (2014) 274–281. doi:10.1016/j.measurement.2013.11.024.
M. V. Munot, M. Joshi, N. Sharma, G. Ahuja, Automated detection of cut-points for disentangling overlapping chromosomes, in: 2013 IEEE Point-of-Care Healthcare Technologies (PHT), 2013, pp. 120–123.
W. Yan, D. Li, Segmentation algorithms of chromosome images, in: Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, 2013, pp. 1026–1029.
A. W. Dougherty, J. You, A Kernel-based adaptive Fuzzy C-Means algorithm for M-FISH image segmentation, 2017 International Joint Conference on Neural Networks (IJCNN) (2017) 198–205.
M. Sharma, O. Saha, A. Sriraman, R. Hebbalaguppe, L. Vig, S. Karande, Crowdsourcing for Chromosome Segmentation and Deep Classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2017-July (2017) 786–793. doi:10.1109/ CVPRW.2017.109.
N. Madian, K. B. Jayanthi, S. Suresh, Contour based segmentation of chromosomes in g-band metaphase images, in: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015, pp. 943–947.
P. Karvelis, A. Likas, D. I. Fotiadis, Identifying touching and overlapping chromosomes using the watershed transform and gradient paths, Pattern Recognition Letters 31 (16) (2010) 2474–2488.
R. J. Rodrigues, W. F. Gonc¸alves, J. F. Mari, A comparison between two approaches to segment overlapped chromosomes in microscopy images, in: Anais do XIII Workshop de Vis˜ao Computacional, 2017, pp. 118– 123.
S. Saiyod, P. Wayalun, A hybrid technique for overlapped chromosome segmentation of g-band mataspread images automatic, in: 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), 2014, pp. 400–404.
R. Gonzalez, R. Woods, Digital Image Processing, Pearson/Prentice Hall, 2008.
A. Buades, B. Coll, J.-M. Morel, A non-local algorithm for image denoising, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 2, IEEE, 2005, pp. 60–65.
J. V. Manjón, J. Carbonell-Caballero, J. J. Lull, G. García-Martí, L. Martí-Bonmatí, M. Robles, Mri denoising using non-local means, Medical Image Analysis 12 (4) (2008) 514 – 523.
J. Fritsch, T. Kuehnl, A. Geiger, A new performance measure and evaluation benchmark for road detection algorithms, in: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013, pp. 1693–1700.
F. Ge, S. Wang, T. Liu, Image-segmentation evaluation from the perspective of salient object extraction, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), Vol. 1, IEEE, 2006, pp. 1146–1153.
L. P. Coelho, A. Shariff, R. F. Murphy, Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms, Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (2009) 518–521.
N. Madian, K. B. Jayanthi, Overlapped chromosome segmentation and separation of touching chromosome for automated chromosome classification, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2012) 5392–5395.
O. M. Filho, L. A. C. Bertollo., Análise cromossômica de astyanax scabripinnis rivularis (characiformes, characidae) da regi˜ao Três Marias MG., Cienc. Cult (1986) 35:855.
A. Levan, K. Fredga, A. A. Sandberg, Nomenclature for centromeric position on chromosomes, Hereditas 52 (2) (1964) 201–220.
R. Manohar, J. Gawande, Watershed and clustering based segmentation of chromosome images, in: Advance Computing Conference (IACC), 2017 IEEE 7th International, IEEE, 2017, pp. 697–700.
D. Somasundaram, V. R. Vijay Kumar, Separation of overlapped chromosomes and pairing of similar chromosomes for karyotyping analysis, Measurement: Journal of the International Measurement Confederation 48 (1) (2014) 274–281. doi:10.1016/j.measurement.2013.11.024.
M. V. Munot, M. Joshi, N. Sharma, G. Ahuja, Automated detection of cut-points for disentangling overlapping chromosomes, in: 2013 IEEE Point-of-Care Healthcare Technologies (PHT), 2013, pp. 120–123.
W. Yan, D. Li, Segmentation algorithms of chromosome images, in: Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, 2013, pp. 1026–1029.
A. W. Dougherty, J. You, A Kernel-based adaptive Fuzzy C-Means algorithm for M-FISH image segmentation, 2017 International Joint Conference on Neural Networks (IJCNN) (2017) 198–205.
M. Sharma, O. Saha, A. Sriraman, R. Hebbalaguppe, L. Vig, S. Karande, Crowdsourcing for Chromosome Segmentation and Deep Classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2017-July (2017) 786–793. doi:10.1109/ CVPRW.2017.109.
N. Madian, K. B. Jayanthi, S. Suresh, Contour based segmentation of chromosomes in g-band metaphase images, in: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2015, pp. 943–947.
P. Karvelis, A. Likas, D. I. Fotiadis, Identifying touching and overlapping chromosomes using the watershed transform and gradient paths, Pattern Recognition Letters 31 (16) (2010) 2474–2488.
R. J. Rodrigues, W. F. Gonc¸alves, J. F. Mari, A comparison between two approaches to segment overlapped chromosomes in microscopy images, in: Anais do XIII Workshop de Vis˜ao Computacional, 2017, pp. 118– 123.
S. Saiyod, P. Wayalun, A hybrid technique for overlapped chromosome segmentation of g-band mataspread images automatic, in: 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), 2014, pp. 400–404.
R. Gonzalez, R. Woods, Digital Image Processing, Pearson/Prentice Hall, 2008.
A. Buades, B. Coll, J.-M. Morel, A non-local algorithm for image denoising, in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 2, IEEE, 2005, pp. 60–65.
J. V. Manjón, J. Carbonell-Caballero, J. J. Lull, G. García-Martí, L. Martí-Bonmatí, M. Robles, Mri denoising using non-local means, Medical Image Analysis 12 (4) (2008) 514 – 523.
J. Fritsch, T. Kuehnl, A. Geiger, A new performance measure and evaluation benchmark for road detection algorithms, in: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), IEEE, 2013, pp. 1693–1700.
F. Ge, S. Wang, T. Liu, Image-segmentation evaluation from the perspective of salient object extraction, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), Vol. 1, IEEE, 2006, pp. 1146–1153.
L. P. Coelho, A. Shariff, R. F. Murphy, Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms, Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (2009) 518–521.
Publicado
07/10/2020
Como Citar
RODRIGUES, Rodrigo; PASA, Rubens; KAVALCO, Karine; MARI, João Fernando.
Segmentation of fish chromosomes in microscopy images: A new dataset. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 58-63.
DOI: https://doi.org/10.5753/wvc.2020.13481.
