The paper examines the features of segmentation of the upper respiratory tract to determine nasal air conduction. 2D and 3D illustrations of the segmentation process and the obtained results are given. When forming an analytical model of the aerodynamics of the nasal cavity, the main indicator that characterizes the configuration of the nasal canal is the equivalent diameter, which is determined at each intersection of the nasal cavity. It is calculated based on the area and perimeter of the corresponding section of the nasal canal. When segmenting the nasal cavity, it is first necessary to eliminate air structures that do not affect the aerodynamics of the upper respiratory tract - these are, first of all, intact spaces of the paranasal sinuses, in which diffuse air exchange prevails. In the automatic mode, this is possible by performing the elimination of unconnected isolated areas and finding the difference coefficients of the areas connected by confluences with the nasal canal in the next step. High coefficients of difference of sections between intersections will indicate the presence of separated areas and contribute to their elimination. The complex configuration and high individual variability of the structures of the nasal cavity does not allow segmentation to be fully automated, but this approach contributes to the absence of interactive correction in 80% of tomographic datasets. The proposed method, which takes into account the intensity of the image elements close to the contour ones, allows to reduce the averaging error from tomographic reconstruction up to 2 times due to artificial sub-resolution. The perspective of the work is the development of methods for fully automatic segmentation of the structures of the nasal cavity, taking into account the individual anatomical variability of the upper respiratory tract.


aerodynamics of nasal breathing; nasal cavity; tomographic reconstruction; segmentation; upper respiratory tract; air conduction

Aras A. et al.: Dimensional changes of the nasal cavity after transpalatal distraction using bone-borne distractor: an acoustic rhinometry and computed tomography evaluation. J. Oral Maxillofac. Surg. 68(7), 2010, 1487–1497. DOI:

Avrunin O. G. et al.: Features of image segmentation of the upper respiratory tract for planning of rhinosurgical surgery. Paper presented at the 2019 IEEE 39th International Conference on Electronics and Nanotechnology, ELNANO 2019, 485–488. DOI:

Avrunin O. G. et al.: Principles of computer planning in the functional nasal surgery. Przeglad Elektrotechniczny 93(3), 2017, 140–143 []. DOI:

Avrunin O. G. et al.: Study of the air flow mode in the nasal cavity during a forced breath. Proc. of SPIE 10445, 2017 []. DOI:

Avrunin O. G. et al.: Possibilities of Automated Diagnostics of Odontogenic Sinusitis According to the Computer Tomography Data. Sensors 21, 1198, 2021 []. DOI:

Berger M. et al.: Agreement between rhinomanometry and computed tomography-based computational fluid dynamics. International Journal of Computer Assisted Radiology and Surgery 16(4), 2021, 629–638 []. DOI:

Cankurtaran M. et al.: Acoustic rhinometry in healthy humans: accuracy of area estimates and ability to quantify certain anatomic structures in the nasal cavity. Ann Otol. Rhinol. Laryngol. 116(12), 2007, 906–916. DOI:

Churchill S. E. et al.: Morphological Variation and Airflow Dynamics in the Human Nose. Am. J. Of Hum. Biol. 16, 2004, 625–638. DOI:

Cilluffo G., et al.: Assessing repeatability and reproducibility of anterior active rhinomanometry (AAR) in children. BMC Medical Research Methodology 20(1), 2020 []. DOI:

Clement P. A.: Standardisation Committee on Objective Assessment of the Nasal Airway. Consensus report on 43, 2005, 169–179.

Fyrmpas G. et al.: The value of bilateral simultaneous nasal spirometry in the assessment of patients undergoing. Rhinology 49(3), 2011, 297–303. DOI:

Hsu Y. et al.: Role of rhinomanometry in the prediction of therapeutic positive airway pressure for obstructive sleep apnea. Respiratory Research 21, 2020, 115 []. DOI:

Kang Y. J. et al.: The diagnostic value of detecting sudden smell loss among asymptomatic COVID-19 patients in early stage: The possible early sign of COVID-19. Auris Nasus Larynx 47(4), 2020, 565–573 []. DOI:

Kirichenko L. et al.: Machine learning in classification time series with fractal properties. Data 4(1), 2019, 5 []. DOI:

Kuo C. J. et al.: Application of intelligent automatic segmentation and 3D reconstruction of inferior turbinate and maxillary sinus from computed tomography and analyze the relationship between volume and nasal lesion. Biomedical Signal Processing and Control 57, 2020, 101660 []. DOI:

Li C. et al.: Nasal structural and aerodynamic features that may benefit normal olfactory sensitivity. Chemical Senses 43(4), 2018, 229–237. DOI:

Mlynski G. et al.: Correlation of nasal morphology and respiratory function. Rhinology 39(4), 2001, 197–201.

Moghaddam M. al.: Virtual septoplasty: A method to predict surgical outcomes for patients with nasal airway obstruction. International Journal of Computer Assisted Radiology and Surgery 15(4), 2020, 725–735 []. DOI:

Ohlmeyer S. et al.: Cone beam CT imaging of the paranasal region with a multipurpose X-ray system-image quality and radiation exposure. Applied Sciences 10(17), 2020, 5876 []. DOI:

Ott K.: Computed tomography of adult rhinosinusitis. Radiologic Technology 89(6), 2018, 571–593.

Paul M. A. et al.: Assessment of functional rhinoplasty with spreader grafting using acoustic rhinomanometry and validated outcome measurements. Plastic and Reconstructive Surgery – Global Open. 6(3), 2018, p e1615 []. DOI:

Pavlov S. V. et al.: Information Technology in Medical Diagnostics. CRC Press, 2017.

Radulesco T. et al.: Correlations between computational fluid dynamics and clinical evaluation of nasal airway obstruction due to septal deviation: An observational study. Clinical Otolaryngology 44(4), 2019, 603–611 []. DOI:

Romanyuk S. et al.: Using lights in a volume-oriented rendering. Proc. of SPIE 10445, 2017, 104450U.

Rovira J. R. et al.: Methods and resources for imaging polarimetry. Proc. of SPIE 8698, 2012, 86980T. DOI:

Tang H. et al.: Dynamic Analysis of Airflow Features in a 3D Real-Anatomical Geometry of the Human Nasal Cavity. 15th Australasian Fluid Mechanics Conference, University of Sydney, Australia, 2004.

Toriumi D.M.: Assessment of rhinoplasty techniques by overlay of before-and-after 3D images. Facial Plast Surg Clin North Am. 19(4), 2011, 711–723. DOI:

Valtonen O. et al.: Three-dimensional printing of the nasal cavities for clinical experiments. Scientific Reports 10, 2020, 502 []. DOI:

Vogt K., Jalowayski A. A.: 4-Phase-Rhinomanometry Basics and Practice. Rhinology 21, 2010, 1–50.

Wójcik W., Pavlov S., Kalimoldayev M.: Information Technology in Medical Diagnostics II. London: Taylor & Francis Group, CRC Press, Balkema book, 2019. DOI:

Zhang G. et al.: Correlation between subjective assessment and objective measurement of nasal obstruction. Zhonghua 43(7), 2008, 484–489.


Published : 2022-12-30

Avrunin, O., Nosova, Y., Shushliapina, N., Abdelhamid, I. Y., Avrunin, O., Kyrylashchuk, S., Moskovchuk, O., & Mamyrbayev, O. (2022). ANALYSIS OF UPPER RESPIRATORY TRACT SEGMENTATION FEATURES TO DETERMINE NASAL CONDUCTANCE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(4), 35-40.

Oleg Avrunin
Kharkiv National University of Radio Electronics  Ukraine
Yana Nosova 
Kharkiv National University of Radio Electronics  Ukraine
Nataliia Shushliapina 
Kharkiv National Medical University  Ukraine
Ibrahim Younouss Abdelhamid 
Kharkiv National University of Radio Electronics  Ukraine
Oleksandr Avrunin 
Kharkiv National University of Radio Electronics  Ukraine
Svetlana Kyrylashchuk 
Vinnytsia National Technical University  Ukraine
Olha Moskovchuk 
Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University  Ukraine
Orken Mamyrbayev 
Institute of Information and Computational Technologies of the Kazakh National Technical University named after K. I. Satbayev  Kazakhstan