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Special Issue "Biomedical Infrared Imaging: From Sensors to Applications"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editors

Guest Editor
Dr. Arcangelo Merla

Infrared Imaging Lab, ITAB Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti 66100, Italy
Website | E-Mail
Interests: infrared imaging; medical imaging; neuroimaging; psychophysiology; human–machine interaction
Guest Editor
Dr. Antonio Maria Chiarelli

Assistant Professor, Infrared Imaging Lab, ITAB Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti 66100, Italy
Website | E-Mail
Interests: infrared imaging; diffuse optical imaging; neuroimaging; Bayesian statistics; machine learning

Special Issue Information

Dear Colleagues,

Technological advancement of infrared sensors is particularly important for biomedical research. This Special Issue aims to create a sharing opportunity between experts on infrared sensors development and scientists that apply such technology within the biomedical field through software, experimental procedures, or novel applications. Different innovative detectors, sensitive to different ranges of the infrared spectrum (from near- to far- infrared; e.g., Silicon Photomultipliers, Bolometers, Quantum Well Detectors, etc.), and application that relies on such sensors, with emphasis on, but not only, wireless or portable technologies, are suitable topics. Original papers that describe new research or innovative biomedical application are welcome. We look forward to, and welcome, your participation in this Special Issue.

Prof. Dr. Arcangelo Merla
Dr. Antonio Maria Chiarelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Infrared Detectors
  • Semiconductor
  • High Performance
  • Medical Imaging
  • Portable Technology
  • Cutaneous Temperature
  • Tissue Perfusion
  • Image Analysis

Published Papers (12 papers)

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Research

Jump to: Review

Open AccessArticle Modelling Impulse Response Function of Functional Infrared Imaging for General Linear Model Analysis of Autonomic Activity
Sensors 2019, 19(4), 849; https://doi.org/10.3390/s19040849
Received: 11 January 2019 / Revised: 1 February 2019 / Accepted: 15 February 2019 / Published: 19 February 2019
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Abstract
Functional infrared imaging (fIRI) is a validated procedure to infer autonomic arousal. Currently, fIRI signals are analysed through descriptive metrics, such as average temperature changes in a region of interest (ROI). However, the employment of mathematical models could provide a powerful tool for [...] Read more.
Functional infrared imaging (fIRI) is a validated procedure to infer autonomic arousal. Currently, fIRI signals are analysed through descriptive metrics, such as average temperature changes in a region of interest (ROI). However, the employment of mathematical models could provide a powerful tool for the accurate identification of autonomic activity and investigation of the mechanisms underlying autonomic arousal. A linear temporal statistical model such as the general linear model (GLM) is particularly suited for its simplicity and direct interpretation. In order to apply the GLM, the thermal response linearity and time-invariance of fIRI have to be demonstrated, and the thermal impulse response (TIR) needs to be characterized. In this study, the linearity and time-invariance of the thermal response to sympathetic activating stimulation were demonstrated, and the TIR for employment of the GLM was characterized. The performance of the GLM-fIRI was evaluated by comparison with the GLM applied on synchronous measurements of the skin conductance response (SCR). In fact, the GLM-SCR is a validated procedure to estimate autonomic arousal. Assuming the GLM-SCR as the gold standard approach, a GLM-fIRI sensitivity and specificity of 86.4% and 75.9% were obtained. The GLM-fIRI may allow increased performances in the evaluation of autonomic activity and a broader range of application of fIRI in both research and clinical settings for the assessment of psychophysiological and psychopathological states. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Understanding Eye Movement Signal Characteristics Based on Their Dynamical and Fractal Features
Sensors 2019, 19(3), 626; https://doi.org/10.3390/s19030626
Received: 18 November 2018 / Revised: 26 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
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Abstract
Eye movement is one of the biological signals whose exploration may reveal substantial information, enabling greater understanding of the biology of the brain and its mechanisms. In this research, eye movement dynamics were studied in terms of chaotic behavior and self-similarity assessment to [...] Read more.
Eye movement is one of the biological signals whose exploration may reveal substantial information, enabling greater understanding of the biology of the brain and its mechanisms. In this research, eye movement dynamics were studied in terms of chaotic behavior and self-similarity assessment to provide a description of young, healthy, oculomotor system characteristics. The first of the investigated features is present and advantageous for many biological objects or physiological phenomena, and its vanishing or diminishment may indicate a system pathology. Similarly, exposed self-similarity may prove useful for indicating a young and healthy system characterized by adaptability. For this research, 24 young people with normal vision were involved. Their eye movements were registered with the usage of a head-mounted eye tracker, using infrared oculography, embedded in the sensor, measuring the rotations of the left and the right eye. The influence of the preprocessing step in the form of the application of various filtering methods on the assessment of the final dynamics was also explored. The obtained results confirmed the existence of chaotic behavior in some parts of eye movement signal; however, its strength turned out to be dependent on the filter used. They also exposed the long-range correlation representing self-similarity, although the influence of the applied filters on these outcomes was not unveiled. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Cortical Network Response to Acupuncture and the Effect of the Hegu Point: An fNIRS Study
Sensors 2019, 19(2), 394; https://doi.org/10.3390/s19020394
Received: 27 November 2018 / Revised: 9 January 2019 / Accepted: 9 January 2019 / Published: 18 January 2019
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Abstract
Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), [...] Read more.
Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Metasurfaces for Advanced Sensing and Diagnostics
Sensors 2019, 19(2), 355; https://doi.org/10.3390/s19020355
Received: 30 November 2018 / Revised: 24 December 2018 / Accepted: 9 January 2019 / Published: 16 January 2019
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Abstract
Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and achieved good performances. Despite the plethora of [...] Read more.
Interest in sensors and their applications is rapidly evolving, mainly driven by the huge demand of technologies whose ultimate purpose is to improve and enhance health and safety. Different electromagnetic technologies have been recently used and achieved good performances. Despite the plethora of literature, limitations are still present: limited response control, narrow bandwidth, and large dimensions. MetaSurfaces, artificial 2D materials with peculiar electromagnetic properties, can help to overcome such issues. In this paper, a generic tool to model, design, and manufacture MetaSurface sensors is developed. First, their properties are evaluated in terms of impedance and constitutive parameters. Then, they are linked to the structure physical dimensions. Finally, the proposed method is applied to realize devices for advanced sensing and medical diagnostic applications: glucose measurements, cancer stage detection, water content recognition, and blood oxygen level analysis. The proposed method paves a new way to realize sensors and control their properties at will. Most importantly, it has great potential to be used for many other practical applications, beyond sensing and diagnostics. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples
Sensors 2018, 18(12), 4487; https://doi.org/10.3390/s18124487
Received: 24 November 2018 / Revised: 14 December 2018 / Accepted: 15 December 2018 / Published: 18 December 2018
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Abstract
The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between [...] Read more.
The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm−1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Intravascular Photothermal Strain Imaging for Lipid Detection
Sensors 2018, 18(11), 3609; https://doi.org/10.3390/s18113609
Received: 18 September 2018 / Revised: 22 October 2018 / Accepted: 22 October 2018 / Published: 24 October 2018
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Abstract
Cardiovascular disease (CVD) is one of the major threats to humanity, accounting for one-third of the world’s deaths. For patients with high-risk CVD, plaque rupture can lead to critical condition. It is therefore important to determine the stability of the plaque and classify [...] Read more.
Cardiovascular disease (CVD) is one of the major threats to humanity, accounting for one-third of the world’s deaths. For patients with high-risk CVD, plaque rupture can lead to critical condition. It is therefore important to determine the stability of the plaque and classify the patient’s risk level. Lipid content is an important determinant of plaque stability. However, conventional intravascular imaging methods have limitations in finding lipids. Therefore, new intravascular imaging techniques for plaque risk assessment are urgently needed. In this study, a novel photothermal strain imaging (pTSI) was applied to an intravascular imaging system for detecting lipids in plaques. As a combination of thermal strain imaging and laser-induced heating, pTSI differentiates lipids from other tissues based on changes in ultrasound (US) velocity with temperature change. We designed an optical pathway to an intravascular ultrasound catheter to deliver 1210-nm laser and US simultaneously. To establish the feasibility of the intravascular pTSI system, we experimented with a tissue-mimicking phantom made of fat and gelatin. Due to the difference in the strain during laser heating, we can clearly distinguish fat and gelatin in the phantom. The result demonstrates that pTSI could be used with conventional intravascular imaging methods to detect the plaque lipid. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface
Sensors 2018, 18(9), 2989; https://doi.org/10.3390/s18092989
Received: 17 July 2018 / Revised: 17 August 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
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Abstract
People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca’s area during overt/covert speech, [...] Read more.
People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca’s area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 ± 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 ± 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 ± 7.12% accuracy was achieved for O2Hb and 78.82 ± 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 ± 14.30% accuracy was achieved with O2Hb and 86.81 ± 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain–Computer Interfaces (BCIs) based on NIRS. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Etracker: A Mobile Gaze-Tracking System with Near-Eye Display Based on a Combined Gaze-Tracking Algorithm
Sensors 2018, 18(5), 1626; https://doi.org/10.3390/s18051626
Received: 30 April 2018 / Revised: 15 May 2018 / Accepted: 15 May 2018 / Published: 19 May 2018
Cited by 2 | PDF Full-text (5484 KB) | HTML Full-text | XML Full-text
Abstract
Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient [...] Read more.
Eye tracking technology has become increasingly important for psychological analysis, medical diagnosis, driver assistance systems, and many other applications. Various gaze-tracking models have been established by previous researchers. However, there is currently no near-eye display system with accurate gaze-tracking performance and a convenient user experience. In this paper, we constructed a complete prototype of the mobile gaze-tracking system ‘Etracker’ with a near-eye viewing device for human gaze tracking. We proposed a combined gaze-tracking algorithm. In this algorithm, the convolutional neural network is used to remove blinking images and predict coarse gaze position, and then a geometric model is defined for accurate human gaze tracking. Moreover, we proposed using the mean value of gazes to resolve pupil center changes caused by nystagmus in calibration algorithms, so that an individual user only needs to calibrate it the first time, which makes our system more convenient. The experiments on gaze data from 26 participants show that the eye center detection accuracy is 98% and Etracker can provide an average gaze accuracy of 0.53° at a rate of 30–60 Hz. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Monitoring of Cardiorespiratory Signals Using Thermal Imaging: A Pilot Study on Healthy Human Subjects
Sensors 2018, 18(5), 1541; https://doi.org/10.3390/s18051541
Received: 5 April 2018 / Revised: 5 May 2018 / Accepted: 8 May 2018 / Published: 13 May 2018
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Abstract
Heart rate (HR) and respiratory rate (RR) are important parameters for patient assessment. However, current measurement techniques require attachment of sensors to the patient’s body, often leading to discomfort, stress and even pain. A new algorithm is presented for monitoring both HR and [...] Read more.
Heart rate (HR) and respiratory rate (RR) are important parameters for patient assessment. However, current measurement techniques require attachment of sensors to the patient’s body, often leading to discomfort, stress and even pain. A new algorithm is presented for monitoring both HR and RR using thermal imaging. The cyclical ejection of blood flow from the heart to the head (through carotid arteries and thoracic aorta) leads to periodic movements of the head; these vertical movements are used to assess HR. Respiratory rate is estimated by using temperature fluctuations under the nose during the respiratory cycle. To test the viability and feasibility of this approach, a pilot study was conducted with 20 healthy subjects (aged 18–36 and 1 aged 50 years). The study consisted of two phases: phase A (frontal view acquisitions) and phase B (side view acquisitions). To validate the results, photoplethysmography and thoracic effort (piezoplethysmography) were simultaneously recorded. High agreement between infrared thermography and ground truth/gold standard was achieved. For HR, the root-mean-square errors (RMSE) for phases A and B were 3.53 ± 1.53 and 3.43 ± 1.61 beats per minute, respectively. For RR, the RMSE between thermal imaging and piezoplethysmography stayed around 0.71 ± 0.30 breaths per minute (phase A). This study demonstrates that infrared thermography may be a promising, clinically relevant alternative for the assessment of HR and RR. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Economic Analysis of the Reduction of Blood Transfusions during Surgical Procedures While Continuous Hemoglobin Monitoring Is Used
Sensors 2018, 18(5), 1367; https://doi.org/10.3390/s18051367
Received: 23 March 2018 / Revised: 22 April 2018 / Accepted: 26 April 2018 / Published: 27 April 2018
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Abstract
Background: Two million transfusions are performed in Spain every year. These come at a high economic price for the health system, increasing the morbidity and mortality rates. The way of obtaining the hemoglobin concentration value is via invasive and intermittent methods, the results [...] Read more.
Background: Two million transfusions are performed in Spain every year. These come at a high economic price for the health system, increasing the morbidity and mortality rates. The way of obtaining the hemoglobin concentration value is via invasive and intermittent methods, the results of which take time to obtain. The drawbacks of this method mean that some transfusions are unnecessary. New continuous noninvasive hemoglobin measurement technology can save unnecessary transfusions. Methods: A prospective study was carried out with a historical control of two homogeneous groups. The control group used the traditional hemoglobin measurement methodology. The experimental group used the new continuous hemoglobin measurement technology. The difference was analyzed by comparing the transfused units of the groups. The economic savings was calculated by multiplying the cost of a transfusion by the difference in units, taking into account measurement costs. Results: The percentage of patients needing a transfusion decreased by 7.4%, and the number of transfused units per patient by 12.56%. Economic savings per patient were €20.59. At the national level, savings were estimated to be 13,500 transfusions (€1.736 million). Conclusions: Constant monitoring of the hemoglobin level significantly reduces the need for blood transfusions. By using this new measurement technology, health care facilities can significantly reduce costs and improve care quality. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Open AccessArticle Infrared Hollow Optical Fiber Probe for Localized Carbon Dioxide Measurement in Respiratory Tracts
Sensors 2018, 18(4), 995; https://doi.org/10.3390/s18040995
Received: 18 February 2018 / Revised: 16 March 2018 / Accepted: 26 March 2018 / Published: 27 March 2018
Cited by 2 | PDF Full-text (9028 KB) | HTML Full-text | XML Full-text
Abstract
A real-time gas monitoring system based on optical absorption spectroscopy is proposed for localized carbon dioxide (CO2) measurement in respiratory tracts. In this system, a small gas cell is attached to the end of a hollow optical fiber that delivers mid-infrared [...] Read more.
A real-time gas monitoring system based on optical absorption spectroscopy is proposed for localized carbon dioxide (CO2) measurement in respiratory tracts. In this system, a small gas cell is attached to the end of a hollow optical fiber that delivers mid-infrared light with small transmission loss. The diameters of the fiber and the gas cell are smaller than 1.2 mm so that the probe can be inserted into a working channel of common bronchoscopes. The dimensions of the gas cell are designed based on absorption spectra of CO2 standard gases in the 4.2 μm wavelength region, which are measured using a Fourier-transform infrared spectrometer. A miniature gas cell that is comprised of a stainless-steel tube with slots for gas inlet and a micro-mirror is fabricated. A compact probing system with a quantum cascade laser (QCL) light source is built using a gas cell with a hollow optical fiber for monitoring CO2 concentration. Experimental results using human breaths show the feasibility of the system for in-situ measurement of localized CO2 concentration in human airways. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Review

Jump to: Research

Open AccessReview An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images
Sensors 2019, 19(1), 194; https://doi.org/10.3390/s19010194
Received: 16 November 2018 / Revised: 28 December 2018 / Accepted: 31 December 2018 / Published: 7 January 2019
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Abstract
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key [...] Read more.
Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened. Full article
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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