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Open AccessArticle

A Smartphone-Based Cell Segmentation to Support Nasal Cytology

1
Department of Computer Science, University of Bari, 70125 Bari, Italy
2
Department of Computer Science, University of Torino, 10124 Torino, Italy
3
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4567; https://doi.org/10.3390/app10134567
Received: 24 May 2020 / Revised: 24 June 2020 / Accepted: 29 June 2020 / Published: 30 June 2020
(This article belongs to the Special Issue Advances in Image Processing, Analysis and Recognition Technology)
Rhinology studies the anatomy, physiology, and diseases affecting the nasal region—one of the most modern techniques to diagnose these diseases is nasal cytology, which involves microscopic analysis of the cells contained in the nasal mucosa. The standard clinical protocol regulates the compilation of the rhino-cytogram by observing, for each slide, at least 50 fields under an optical microscope to evaluate the cell population and search for cells important for diagnosis. The time and effort required for the specialist to analyze a slide are significant. In this paper, we present a smartphones-based system to support cell segmentation on images acquired directly from the microscope. Then, the specialist can analyze the cells and the other elements extracted directly or, alternatively, he can send them to Rhino-cyt, a server system recently presented in the literature, that also performs the automatic cell classification, giving back the final rhinocytogram. This way he significantly reduces the time for diagnosing. The system crops cells with sensitivity = 0.96, which is satisfactory because it shows that cells are not overlooked as false negatives are few, and therefore largely sufficient to support the specialist effectively. The use of traditional image processing techniques to preprocess the images also makes the process sustainable from the computational point of view for medium–low end architectures and is battery-efficient on a mobile phone. View Full-Text
Keywords: nasal cytology; automatic cell segmentation; rhinology; image analysis nasal cytology; automatic cell segmentation; rhinology; image analysis
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Dimauro, G.; Di Pierro, D.; Deperte, F.; Simone, L.; Fina, P.R. A Smartphone-Based Cell Segmentation to Support Nasal Cytology. Appl. Sci. 2020, 10, 4567.

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