Analysis of Breast Thermograms Using Asymmetry in Infra-Mammary Curves

Devi, Reena; Anandhamala, G. S.

doi:10.1007/s10916-019-1267-8

Cited by 18 publications

(10 citation statements)

References 16 publications

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“…Out of all the state-of-the-art works shown in Table I, [15] achieves the highest accuracy of breast abnormality detection [15], the best performing state-of-the-art is presented.…”

Section: Comparison With Recent State-of-the-artmentioning

confidence: 99%

“…In [15], a novel asymmetry-based methodology using segmented region of interest (ROI) to identify subjects with In this state-of-the-art, the breasts were extracted together using vertical and horizontal projections (Readers are directed to [15] for more details). Following this, the extracted breasts were segmented using a bifurcation point between the breasts obtained by interpolating and fitting infra-mammary curves (or Catenary curves) on both breasts.…”

Section: Comparison With Recent State-of-the-artmentioning

confidence: 99%

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Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions

Dey

Ali

Rajan

2023

IEEE Open J. Instrum. Meas.

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With an increase in the number of breast cancer cases worldwide, there is an urgent need to develop techniques for early abnormality detection. Thermography is known for its potential to detect breast abnormalities at an early stage. A novel threshold-based non-machine learning asymmetry analysis using textural features is proposed for breast abnormality detection. Breast abnormalities are indicated by regions of elevated temperatures (hot-regions), usually, indicated by red colour in thermograms. In this work, the breast thermograms are segmented to extract breast tissue profiles and then the red-plane of an RGB thermogram is utilized to analyze the natural contralateral symmetry between the left and right breast of an individual. A novel textural feature based on histogram similarity along with known textural features such as fractal dimension, hurst exponent, spectral norm and Frobenius norm are used as features for asymmetry analysis. Bilateral ratios of these features indicate contralateral symmetry between the left and right breast. A bilateral ratio value closer to 1 indicates such symmetry. Hard voting is done among all the bilateral ratios of the textural features to estimate asymmetry between the left and right breast and detect an individual with breast abnormality. The proposed methodology is evaluated on publicly available datasets. It outperforms the state-of-the-art and achieves an accuracy of 96.08%, sensitivity of 100% and specificity of 93.57%. A comparative analysis of statistical and textural features has also been demonstrated. A novel singular value decomposition (SVD)-based abnormal breast detection technique has been proposed with evaluations on a limited dataset.

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“…Out of all the state-of-the-art works shown in Table I, [15] achieves the highest accuracy of breast abnormality detection [15], the best performing state-of-the-art is presented.…”

Section: Comparison With Recent State-of-the-artmentioning

confidence: 99%

Section: Comparison With Recent State-of-the-artmentioning

confidence: 99%

Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions

Dey

Ali

Rajan

2023

IEEE Open J. Instrum. Meas.

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“…Using this technique, we used 30 images for training and remaining for testing. [31][32] This is then repeated for different enhancement techniques.…”

Section: Rmsmentioning

confidence: 99%

“…Devi et al 31 introduced a method of analyzing a breast thermogram to detect breast abnormalities, including cancer. This research work is mainly aimed at the separated ROI, which shows a significant increase in temperature compared to the interactions with neighboring regions and breast thermogram.…”

Section: Literature Reviewmentioning

confidence: 99%

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Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

Raj¹,

Sundaram²,

Jaya³

2020

Int J Imaging Syst Tech

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The early detection of tumor is necessary to save a number of lives. In women, the temperature of the affected area of the tumor is warmer than the unaffected area; therefore the thermography technique can be used to capture the cancerous breast images with a thermal infrared by identifying the temperature difference between them. Color enhancement of the captured breast image is an important consideration for early detection of tumor at this stage. Therefore, in this paper, we propose a self‐adaptive gray level histogram equalization approach to enhance the color of the IR image for early detection of the tumor. This approach does not require any manual feeding of parameters toward images. The final classification of tumorous and non‐tumorous breast images can obtain through certain procedures, which includes, image acquisition, pre‐processing, segmentation, feature extraction and classification. This paper emphasizes the support vector machine (SVM) technique to classify the tumor IR thermography images. The proposed approach is implemented in MATLAB and the experimental results shows an outstanding color enhancement of IR images and better classification compared to other existing methods such as CLAHE, BIi‐histogram equalization and adaptive histogram equalization. The performance was evaluated by using evaluation metrics such as sensitivity, accuracy, and specificity of thermography breast image by the SVM classifier adapted with various color enhancement approaches are found to be 91.6%, 90%, and 87.5%. This approach helps in medical field for early diagnosis with high reliability.

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A Cost-Effective, Agar-based Phantom for Thermogram- Guided Malignancy Analysis

Ramyadevi

2023

EAI/Springer Innovations in Communication and Computing

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Analysis of Breast Thermograms Using Asymmetry in Infra-Mammary Curves

Cited by 18 publications

References 16 publications

Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions

Bilateral Symmetry-Based Abnormality Detection in Breast Thermograms Using Textural Features of Hot Regions

Thermography based breast cancer detection using self‐adaptive gray level histogram equalization color enhancement method

A Cost-Effective, Agar-based Phantom for Thermogram- Guided Malignancy Analysis

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