Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study

Yousefi, Bardia; Ibarra-Castanedo, Clemente; Maldague, Xavier

doi:10.1109/tim.2020.3031129

Cited by 20 publications

(26 citation statements)

References 49 publications

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“…This might require further investigation using multimodal imaging analyses. There are also discussions on using a single thermal frame or multiple frames for selecting variability of thermal patterns across the acquisition time using different infrared-based approaches [5,35,[95][96][97], which can be investigated further. Applying multiple thermal frames provided a chance of capturing thermal heterogeneity in the ROI for the duration of the acquisition, which might not be recorded by a single frame input system.…”

Section: Discussionmentioning

confidence: 99%

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

2020

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Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3–81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.

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Section: Discussionmentioning

confidence: 99%

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

2020

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“…The visual results along with F-score based results are presented to prove the efficacy of the proposed model. In [19], the authors present a comparative study of extracting subsurface defects in thermal patterns. The non-negative matrix factorization methods are used and compared on the thermal data and results are presented in terms of detection accuracies.…”

Section: Related Workmentioning

confidence: 99%

Feature Concatenation based Multilayered Sparse Tensor for Debond Detection Optical Thermography

Ahmed¹,

Baseer²,

Tian³

et al. 2022

IJACSA

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Composites being the key ingredients of the manufacturing in the aerospace, aircraft, civil and related industries, it is quite important to check its quality and health during its manufacture or in service. The most commonly found problem in the CFRPs is debonding. As debonds are subsurface defects, the general methods are not quite effective and require destructive tests. The Optical Pulse Thermography (OPT) is a quite promising technology that is being used for detecting the debonds. However, the thermographic time sequences from the OPT system have a lot of noise and normally the defects information is not clear. For solving this problem, an improved tensor nuclear norm (I-TNN) decomposition is proposed in the concatenated feature space with multilayer tensor decomposition. The proposed algorithm utilizes the frontal slice of the tensor to define the TNN and the core singular matrix is further decomposed to utilize the information in the third mode of the tensor. The concatenation helps embed the low-rank and sparse data jointly for weak defect extraction. To show the efficacy and robustness of the algorithm experiments are conducted and comparisons are presented with other algorithms.

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“…Yousefi et al [64][65][66] introduced the Low-rank SPCT and applied several Non-negative Matrix Factorization (NMF) approaches. They conducted a comparative analysis on lowrank matrix approximation using the NMF approach and showed more promising results than other component-based (PCT, CCIPCT, SCPT) methods.…”

Section: Advances Of Pctmentioning

confidence: 99%

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

2021

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Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

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Measuring Heterogeneous Thermal Patterns in Infrared-Based Diagnostic Systems Using Sparse Low-Rank Matrix Approximation: Comparative Study

Cited by 20 publications

References 49 publications

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

Feature Concatenation based Multilayered Sparse Tensor for Debond Detection Optical Thermography

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

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