Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods

Hakim, Aayesha; Awale, R. N.

doi:10.1080/17686733.2021.1974209

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…The B-channel grayscale image in the RGB color space was selected to obtain the outside edge features of the egg. Its binary image was obtained using the maximum interclass variance method (Otsu), and the Canny edge operator [ 35 ] was used to extract the outside edge of the egg.…”

Section: Methodsmentioning

confidence: 99%

“…When the time reached 8.78776 s, the temperature of the albumen started to rise because it was subjected to both thermal convection and heat transfer from the eggshell (Figure 3h). To ensure the stability of the internal chemical structure of the eggs and to highlight temperature changes, we used hot air at 20,25,30,35,40, and 45 °C to heat the egg's blunt end.…”

Section: Initial and Boundary Conditionsmentioning

confidence: 99%

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Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

Zhang

Xing-liang

et al. 2023

Sensors

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In this paper, we proposed a nondestructive detection method for egg freshness based on infrared thermal imaging technology. We studied the relationship between egg thermal infrared images (different shell colors and cleanliness levels) and egg freshness under heating conditions. Firstly, we established a finite element model of egg heat conduction to study the optimal heat excitation temperature and time. The relationship between the thermal infrared images of eggs after thermal excitation and egg freshness was further studied. Eight values of the center coordinates and radius of the egg circular edge as well as the long axis, short axis, and eccentric angle of the egg air cell were used as the characteristic parameters for egg freshness detection. After that, four egg freshness detection models, including decision tree, naive Bayes, k-nearest neighbors, and random forest, were constructed, with detection accuracies of 81.82%, 86.03%, 87.16%, and 92.32%, respectively. Finally, we introduced SegNet neural network image segmentation technology to segment the egg thermal infrared images. The SVM egg freshness detection model was established based on the eigenvalues extracted after segmentation. The test results showed that the accuracy of SegNet image segmentation was 98.87%, and the accuracy of egg freshness detection was 94.52%. The results also showed that infrared thermography combined with deep learning algorithms could detect egg freshness with an accuracy of over 94%, providing a new method and technical basis for online detection of egg freshness on industrial assembly lines.

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Section: Methodsmentioning

confidence: 99%

Section: Initial and Boundary Conditionsmentioning

confidence: 99%

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

Zhang

Xing-liang

et al. 2023

Sensors

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“…Hakim and Awale [111] applied five image segmentation methods to extract the hottest blood vessel patterns from benchmark breast thermograms available in a public data set. The authors concluded that the particle swarm optimization algorithm and multi-seed region-growing technique yield the best segmentation results.…”

Section: Breast Thermogramsmentioning

confidence: 99%

A Review of Techniques and Bio-Heat Transfer Models Supporting Infrared Thermal Imaging for Diagnosis of Malignancy

D’Alessandro,

Tavakolian,

Sfarra

2024

Applied Sciences

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The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and thermogram post-processing methods is comprehensively reviewed. Once the thermal signatures of different malignant diseases are described, the updated thermographic techniques (steady-state and dynamic) used for cancer diagnosis are discussed in detail, along with the recommended best practices to ensure the most significant thermal contrast observable between the cancerous and healthy tissues. Regarding the dynamic techniques, particular emphasis is placed on innovative methods, such as lock-in thermography, thermal wave imaging, and rotational breast thermography. Forward and inverse modeling techniques for the bio-heat transfer in skin and breast tissues, supporting the thermographic examination and providing accurate data for training artificial intelligence (AI) algorithms, are reported with a special focus on real breast geometry-based 3D models. In terms of inverse techniques, different data processing algorithms to retrieve thermophysical parameters and growth features of tumor lesions are mentioned. Post-processing of infrared images is also described, citing both conventional processing procedures and applications of AI algorithms for tumor detection.

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“…Threshold segmentation is a way to achieve the location and range of the defects. Hakim and Awale [31] used five state-of-the-art image segmentation methods to extract the hottest blood vessel patterns. Particle swarm optimization algorithm and multi-seed region-growing technique provided the best segmentation results that are closer to the ground truth images.…”

Section: Pretreatment Of Imagementioning

confidence: 99%

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

Gou

Qingying

Yao

et al. 2023

Meas. Sci. Technol.

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Active infrared thermography has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis algorithm with sliding-window mechanism and the 1DResnet50-CBAM (One Dimensional - Residual Neural Network - Convolutional Block Attention Module) model was proposed for the reconstructed image sequence of active infrared thermography to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using principal component analysis algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional (3D) defect data were transformed into one-dimensional (1D) temporal infrared thermal signal. Moveover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion principal component analysis algorithm with sliding-window mechanism and 1DResnet50-CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and quantitative analysis using infrared thermal detection technology.

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Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods

Cited by 10 publications

References 25 publications

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

Nondestructive Detection of Egg Freshness Based on Infrared Thermal Imaging

A Review of Techniques and Bio-Heat Transfer Models Supporting Infrared Thermal Imaging for Diagnosis of Malignancy

Algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

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