Selection of geometrical features of nuclei оn fluorescent images of cancer cells
The methods of geometric informative features selection of nuclei on fluorescent images of cancer cells are considered. During the survey, a review of existing geometric features was carried out, including both the signs of rotation resisted shape and displacement of the image, as well as signs of l...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Russian |
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National Academy of Sciences of Belarus, the United Institute of Informatics Problems
2019-06-01
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| Series: | Informatika |
| Subjects: | |
| Online Access: | https://inf.grid.by/jour/article/view/474 |
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| author | Ya. U. Lisitsa M. M. Yatskou V. V. Skakun P. D. Pavel D. Kryvasheyeu V. V. Apanasovich |
| author_facet | Ya. U. Lisitsa M. M. Yatskou V. V. Skakun P. D. Pavel D. Kryvasheyeu V. V. Apanasovich |
| author_sort | Ya. U. Lisitsa |
| collection | DOAJ |
| description | The methods of geometric informative features selection of nuclei on fluorescent images of cancer cells are considered. During the survey, a review of existing geometric features was carried out, including both the signs of rotation resisted shape and displacement of the image, as well as signs of location in space. For the selection of characteristics, the methods were used: median, correlation with calculation of the Pearson correlation coefficient, correlation with calculation of the Spearman correlation coefficient, logistic regression model, random forest with CART trees and Gini criterion, random forest with CART trees and error minimization criterion. As a result of the investigation 11 characteristics were selected from 59 features, the quality of classification and time costs were calculated depending on the number of features for describing the objects. The use of 11 features is sufficient for the accuracy of classification as it allows to reduce time costs in 2,3 times. |
| format | Article |
| id | doaj-art-0a66b0d95b5648178dbf542e155de389 |
| institution | Kabale University |
| issn | 1816-0301 |
| language | Russian |
| publishDate | 2019-06-01 |
| publisher | National Academy of Sciences of Belarus, the United Institute of Informatics Problems |
| record_format | Article |
| series | Informatika |
| spelling | doaj-art-0a66b0d95b5648178dbf542e155de3892025-02-03T11:51:44ZrusNational Academy of Sciences of Belarus, the United Institute of Informatics ProblemsInformatika1816-03012019-06-01162717633Selection of geometrical features of nuclei оn fluorescent images of cancer cellsYa. U. Lisitsa0M. M. Yatskou1V. V. Skakun2P. D. Pavel D. Kryvasheyeu3V. V. Apanasovich4Belarusian State UniversityBelarusian State UniversityBelarusian State UniversityBelarusian State UniversityInstitute of IT & Business AdministrationThe methods of geometric informative features selection of nuclei on fluorescent images of cancer cells are considered. During the survey, a review of existing geometric features was carried out, including both the signs of rotation resisted shape and displacement of the image, as well as signs of location in space. For the selection of characteristics, the methods were used: median, correlation with calculation of the Pearson correlation coefficient, correlation with calculation of the Spearman correlation coefficient, logistic regression model, random forest with CART trees and Gini criterion, random forest with CART trees and error minimization criterion. As a result of the investigation 11 characteristics were selected from 59 features, the quality of classification and time costs were calculated depending on the number of features for describing the objects. The use of 11 features is sufficient for the accuracy of classification as it allows to reduce time costs in 2,3 times.https://inf.grid.by/jour/article/view/474correlationrandom forestlogistic regressionmedianclassification |
| spellingShingle | Ya. U. Lisitsa M. M. Yatskou V. V. Skakun P. D. Pavel D. Kryvasheyeu V. V. Apanasovich Selection of geometrical features of nuclei оn fluorescent images of cancer cells Informatika correlation random forest logistic regression median classification |
| title | Selection of geometrical features of nuclei оn fluorescent images of cancer cells |
| title_full | Selection of geometrical features of nuclei оn fluorescent images of cancer cells |
| title_fullStr | Selection of geometrical features of nuclei оn fluorescent images of cancer cells |
| title_full_unstemmed | Selection of geometrical features of nuclei оn fluorescent images of cancer cells |
| title_short | Selection of geometrical features of nuclei оn fluorescent images of cancer cells |
| title_sort | selection of geometrical features of nuclei оn fluorescent images of cancer cells |
| topic | correlation random forest logistic regression median classification |
| url | https://inf.grid.by/jour/article/view/474 |
| work_keys_str_mv | AT yaulisitsa selectionofgeometricalfeaturesofnucleionfluorescentimagesofcancercells AT mmyatskou selectionofgeometricalfeaturesofnucleionfluorescentimagesofcancercells AT vvskakun selectionofgeometricalfeaturesofnucleionfluorescentimagesofcancercells AT pdpaveldkryvasheyeu selectionofgeometricalfeaturesofnucleionfluorescentimagesofcancercells AT vvapanasovich selectionofgeometricalfeaturesofnucleionfluorescentimagesofcancercells |