Enhancement and segmentation of breast thermograms

Trongtirakul, Thaweesak; Oulefki, Adel; Agaian, Sos С.; Chiracharit, Werapon

doi:10.1117/12.2554594

Cited by 6 publications

(5 citation statements)

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“…To assess the efficiency and robustness of our proposed approach, we use the following performance metrics: Accuracy [24], Sensitivity [25], Matthews Correlation Coefficient [25], and Dice [26]. By definition, higher values of these metrics imply a better segmentation quality.…”

Section: Performance Metrics Evaluationmentioning

confidence: 99%

CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

Biach¹,

Laanaya²,

Minaoui³

2022

IJACSA

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COVID-19 epidemic continues to threaten public health with the appearance of new, more severe mutations, and given the delay in the vaccination process, the situation becomes more complex. Thus, the implementation of rapid solutions for the early detection of this virus is an immediate priority. To this end, we provide a deep learning method called CovSeg-Unet to diagnose COVID-19 from chest CT images. The CovSeg-Unet method consists in the first time of preprocessing the CT images to eliminate the noise and make all images in the same standard. Then, CovSeg-Unet uses an end-to-end architecture to form the network. Since CT images are not balanced, we propose a loss function to balance the pixel distribution of infected/uninfected regions. CovSeg-Unet achieved high performances in localizing COVID-19 lung infections compared to others methods. We performed qualitative and quantitative assessments on two public datasets (Dataset-1 and Dataset-2) annotated by expert radiologists. The experimental results prove that our method is a real solution that can better help in the COVID-19 diagnosis process.

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Section: Performance Metrics Evaluationmentioning

confidence: 99%

CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

Biach¹,

Laanaya²,

Minaoui³

2022

IJACSA

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“…The possibility of having a lesion absence in the image can be presented, resulting in the consideration of patients as normal and healthy, therefore; no segmentation is required. Besides, we assessed statistically the segmentation quality of the proposed against GraphCut, Watershed, and MSA, by picking: Accuracy [37] , Sensitivity [38] , F-Measure [38] , Precision [39] , MCC (Mathew Correlation Coefficient) [38] , Dice [40] , Jaccard [40] , and Specificity [38] . By definition, higher values on these indexes imply a better quality of segmentation.…”

Section: Resultsmentioning

confidence: 99%

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

Oulefki

Agaian

Trongtirakul

et al. 2021

Pattern Recognition

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152

108

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Highlights Automatically enhance, divide, and validate the COVID-19 CT images into regions with similar properties such as contrast and structure. Efficient Kapur entropy-based multilevel thresholding unsupervised procedure. Measure, visualize, and study comparisons of the infected by COVID-19 volume. The experiment results indicate that the proposed method can reach the desired heat-mapping of COVID-19 lesion and has the potential to be used for clinical application including developing country.

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“…This unclarity is because there is minimal contrast between the cancerous region and the surrounding background. 6 These restrictions can be overcome with an adequate image enhancement technique, especially when dealing with dense breasts. Thus, it is essential to examine traditional image technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Even in situations where there is a significant amount of breast tissue, breast thermogram images do not always show cancerous regions clearly. This unclarity is because there is minimal contrast between the cancerous region and the surrounding background 6 …”

Section: Introductionmentioning

confidence: 99%

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

Ahmad,

2024

Adaptive Control & Signal

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SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

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Enhancement and segmentation of breast thermograms

Cited by 6 publications

References 0 publications

CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

CovSeg-Unet: End-to-End Method-based Computer-Aided Decision Support System in Lung COVID-19 Detection on CT Images

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

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