A Smartphone-Based Cell Segmentation to Support Nasal Cytology

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.

[1]  Gerald Schaefer,et al.  Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods , 2013, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  V. Piuri,et al.  Morphological classification of blood leucocytes by microscope images , 2004, 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA..

[4]  Rosalia Maglietta,et al.  A Novel Approach for the Automatic Estimation of the Ciliated Cell Beating Frequency , 2020, Electronics.

[5]  Xukan Ran,et al.  Deep Learning With Edge Computing: A Review , 2019, Proceedings of the IEEE.

[6]  Md. Kamrul Hasan,et al.  Important Feature Selection & Accuracy Comparisons of Different Machine Learning Models for Early Diabetes Detection , 2018, 2018 International Conference on Innovation in Engineering and Technology (ICIET).

[7]  Danilo Caivano,et al.  The patient centered Electronic Multimedia Health Fascicle - EMHF , 2014, 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings.

[8]  Danilo Caivano,et al.  VoxTester, software for digital evaluation of speech changes in Parkinson disease , 2016, 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[9]  Giovanni Dimauro,et al.  Nasal cytology with deep learning techniques , 2019, Int. J. Medical Informatics.

[10]  Giovanni Dimauro,et al.  Novel Biased Normalized Cuts Approach for the Automatic Segmentation of the Conjunctiva , 2020, Electronics.

[11]  G. Passalacqua,et al.  NASAL cytology: practical aspects and clinical relevance , 2016, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[12]  Sebastiano Impedovo,et al.  Residue-to-binary conversion by the "quotient function" , 2003, IEEE Trans. Circuits Syst. II Express Briefs.

[13]  Vitoantonio Bevilacqua,et al.  Comparative Analysis of Rhino-Cytological Specimens with Image Analysis and Deep Learning Techniques , 2020, Electronics.

[14]  Paul D. Gader,et al.  System-level training of neural networks for counting white blood cells , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[15]  Vitoantonio Bevilacqua,et al.  TestGraphia, a Software System for the Early Diagnosis of Dysgraphia , 2020, IEEE Access.

[16]  Danilo Caivano,et al.  Rhino-Cyt: A System for Supporting the Rhinologist in the Analysis of Nasal Cytology , 2018, ICIC.

[17]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  A. Gigantesco,et al.  Quality of life in mental health services with a focus on psychiatric rehabilitation practice. , 2011, Annali dell'Istituto superiore di sanita.

[19]  Vitoantonio Bevilacqua,et al.  A supervised CAD to support telemedicine in hematology , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[20]  Jianzhong Wang,et al.  Automatic neutrophil nucleus lobe counting based on graph representation of region skeleton , 2012, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[21]  Nikolay I. Chervyakov,et al.  Construction of Residue Number System Using Hardware Efficient Diagonal Function , 2019 .

[22]  Sheikh Iqbal Ahamed,et al.  Smartphone-based Human Hemoglobin Level Measurement Analyzing Pixel Intensity of a Fingertip Video on Different Color Spaces , 2017 .

[23]  Giovanni Dimauro,et al.  PQMET: A digital image quality metric based on human visual system , 2014, 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA).

[24]  Massimo Merenda,et al.  Edge Machine Learning for AI-Enabled IoT Devices: A Review , 2020, Sensors.

[25]  Qing Zheng,et al.  Direct neural network application for automated cell recognition , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[26]  Gianpaolo Francesco Trotta,et al.  A novel approach for Hepatocellular Carcinoma detection and classification based on triphasic CT Protocol , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[27]  Stanislaw Osowski,et al.  Application of Support Vector Machine and Genetic Algorithm for Improved Blood Cell Recognition , 2009, IEEE Transactions on Instrumentation and Measurement.

[28]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[29]  Todor A Popov,et al.  Allergic Rhinitis and its Impact on Asthma (ARIA): achievements in 10 years and future needs. , 2012, The Journal of allergy and clinical immunology.

[30]  Sebastiano Impedovo,et al.  RNS architectures for the implementation of the 'diagonal function' , 2000, Inf. Process. Lett..

[31]  Hongying Liu,et al.  A combined spatial-spectral method for automated white blood cells segmentation , 2013 .

[32]  Rosalia Maglietta,et al.  A Novel Approach for Biofilm Detection Based on a Convolutional Neural Network , 2020, Electronics.

[33]  Danilo Caivano,et al.  Detecting Clinical Signs of Anaemia From Digital Images of the Palpebral Conjunctiva , 2019, IEEE Access.

[34]  Sabina Sonia Tangaro,et al.  Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm , 2015, Pattern Analysis and Applications.

[35]  Christoph Rasche.  Fleckmentation: rapid segmentation using repeated 2-means , 2019, IET Image Process..

[36]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[37]  Giovanni Dimauro.  A new image quality metric based on human visual system , 2012, 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings.

[38]  Danilo Caivano,et al.  Personal Health E-Record - Toward an Enabling Ambient Assisted Living Technology for Communication and Information Sharing Between Patients and Care Providers , 2018, ForItAAL.