Searching for cell signatures in multidimensional feature spaces
| العنوان: | Searching for cell signatures in multidimensional feature spaces |
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| المؤلفون: | Fátima N. S. de Medeiros, Paulo H. C. Oliveira, Romuere R. V. Silva, Mariana T. Rezende, Daniela Ushizima, Flávio H. D. Araújo, Rodrigo Veras |
| المصدر: | International Journal of Biomedical Engineering and Technology. 36:236 |
| بيانات النشر: | Inderscience Publishers, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | business.industry, Computer science, Feature extraction, Biomedical Engineering, Pattern recognition, Cervical cells, Image (mathematics), Digital image, Software, Feature (computer vision), Quantitative Microscopy, Artificial intelligence, business, Implementation |
| الوصف: | Despite research on cervical cells since 1925, systems to automatically screen images from conventional Pap smear tests continue to be unavailable. One of the main challenges in deploying precise software tools is to validate cell signatures. In this paper, we introduce an analysis framework, CRIC-feat, that expedites the investigation of different image databases and respective descriptors, particularly applicable to Pap images. This paper provides a three-fold contribution: 1) we first review and discuss the main feature extraction protocols for cell description and implementations suitable for cervical cells; 2) we present a new application of Gray level run length (GLRLM) features to Pap images; 3) we evaluate 93 cell classification approaches, and provide a guideline for obtaining the most accurate description, based on two current public databases with digital images of real cells. Finally, we show that the nucleus information is preponderant in cell classification, particularly when considering the GLRLM feature set. |
| تدمد: | 1752-6426 1752-6418 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f55571a1c832e36fd82e9f220ff9bdd1 https://doi.org/10.1504/ijbet.2021.10040044 |
| رقم الأكسشن: | edsair.doi...........f55571a1c832e36fd82e9f220ff9bdd1 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17526426 17526418 |
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