High precision automated face localization in thermal images: oral cancer dataset as test case

Chakraborty, Mrityunjoy; Raman, Suraj Kiran; Mukhopadhyay, Sudipta; Patsa, Santanu; Anjum, Nadeem; Ray, Jharna

doi:10.1117/12.2254236

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…In addition, there are a lot of other reasons why thermal imaging is not applied in clinical routines, especially due to artefacts caused by facial hair, skin conditions, scars, "blushing" effect, asymmetrical distribution of adipose tissues, facial paralysis, veins near the superficial layer of the skin, etc. To date, IRT has been adopted extensively in the early detection of breast cancer [20][21][22][23], but for orofacial tumor detection, the available evidence is limited and weak [24,25]. It should also be underlined that according to the newest healthcare guidelines [26,27], current evidence does not support the routine use of thermography as a screening procedure in either field of clinical diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

Mačianskytė

Adaškevičius

2022

Sensors

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Traditional computed tomography (CT) delivers a relatively high dose of radiation to the patient and cannot be used as a method for screening of pathologies. Instead, infrared thermography (IRT) might help in the detection of pathologies, but interpreting thermal imaging (TI) is difficult even for the expert. The main objective of this work is to present a new, automated IRT method capable to discern the absence or presence of tumor in the orofacial/maxillofacial region of patients. We evaluated the use of a special feature vector extracted from face and mouth cavity thermograms in classifying TIs against the absence/presence of tumor (n = 23 patients per group). Eight statistical features extracted from TI were used in a k-nearest neighbor (kNN) classifier. Classification accuracy of kNN was evaluated by CT, and by creating a vector with the true class labels for TIs. The presented algorithm, constructed from a training data set, gives good results of classification accuracy of kNN: sensitivity of 77.9%, specificity of 94.9%, and accuracy of 94.1%. The new algorithm exhibited almost the same accuracy in detecting the absence/presence of tumor as CT, and is a proof-of-principle that IRT could be useful as an additional reliable screening tool for detecting orofacial/maxillofacial tumors.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

Mačianskytė

Adaškevičius

2022

Sensors

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“…As elevated body temperature is an important indicator of a possible underlying physiological process, thermal imaging is a great non-intrusive tool for presenting that biological state. The non-invasive nature of thermal cameras has resulted in extensive utilization of these devices for detecting and diagnostic purposes in several areas of medicine [1,2,3], and none-medical research such as fire safety [4,5,6], transportation [7,8,9], and building construction [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

Ashrafi¹,

Azarbayjania²,

Tabkhi³

2022

Preprint

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Facial thermography is one of the most popular research areas in infrared thermal imaging, with diverse applications in medical, surveillance, and environmental monitoring. However, in contrast to facial imagery in the visual spectrum, the lack of public datasets on facial thermal images is an obstacle to research improvement in this area. Thermal face imagery is still a relatively new research area to be evaluated and studied in different domains.The current thermal face datasets are limited in regards to the subjects' distance from the camera, the ambient temperature variation, and facial landmarks' localization. We address these gaps by presenting a new facial thermography dataset.This article makes two main contributions to the body of knowledge. First, it presents a comprehensive review and comparison of current public datasets in facial thermography. Second, it introduces and studies a novel public dataset on facial thermography, which we call it Charlotte-ThermalFace. Charlotte-ThermalFace contains more than10000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. The data is fully annotated with the facial landmarks, ambient temperature, relative humidity, the air speed of the room, distance to the camera, and subject thermal sensation at the time of capturing each image. Our dataset is the first publicly available thermal dataset annotated with the thermal sensation of each subject in different thermal conditions and one of the few datasets in raw 16-bit format. Finally, we present a preliminary analysis of the dataset to show the applicability and importance of the thermal conditions in facial thermography. The full dataset, including annotations, are freely available for research purpose at

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Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches

Kopaczka

Nestler

Merhof

2017

Lecture Notes in Computer Science

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High precision automated face localization in thermal images: oral cancer dataset as test case

Cited by 3 publications

References 3 publications

Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

Automatic Detection of Human Maxillofacial Tumors by Using Thermal Imaging: A Preliminary Study

A Novel Fully Annotated Thermal Infrared Face Dataset: Recorded in Various Environment Conditions and Distances From The Camera

Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches

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