Abstract
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.
Highlights
To the best of our knowledge, to date, there are no public or private laboratories that carry out the examination of the cell population of the nasal mucosa routinely, as instead it is done for hematological tests. This is for different reasons: firstly, because diagnostics based on nasal cytology have grown recently; secondly, because economic interest is still residual; because the spectrum of diagnosable pathologies is not as extensive as in other fields of medicine
Nasal cytology is a very useful diagnostic method in rhino-allergology—it allows for the detection of cellular variations of an epithelium exposed to acute/chronic irritations or inflammations of different nature and makes it possible to diagnose some nasal pathologies [35,36]
Thanks to the large number of contexts in which digital image processing has been successfully in Thanks to the large number of contexts in which digital image processing has been successfully experimentation, its use has increased in medicine that is becoming highly dependent on it and in experimentation, its use has increased in medicine that is becoming highly dependent on it represents fundamental pillars of modern diagnosis [38,39,40,41,42]
Summary
Thanks to the numerous studies in the field of computer vision applied to the medical and biomedical field, we have many additional tools to support specialists in their tasks [1,2,3,4,5]. To the best of our knowledge, to date, there are no public or private laboratories that carry out the examination of the cell population of the nasal mucosa routinely, as instead it is done for hematological tests This is for different reasons: firstly, because diagnostics based on nasal cytology have grown recently; secondly, because economic interest is still residual; because the spectrum of diagnosable pathologies is not as extensive as in other fields of medicine. In this paper a novel system based on a smartphone is presented to support rhinocytologists during cell observation It carries out cell extraction from the digital image of the microscopic fields.
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