Suspicious-Region Segmentation From Breast Thermogram Using DLPE-Based Level Set Method

Pramanik, Sourav; Banik, Debapriya; Bhattacharjee, Debotosh; Nasipuri, Mita; Bhowmik, Mrinal Kanti; Majumdar, G.

doi:10.1109/tmi.2018.2867620

Cited by 47 publications

(21 citation statements)

References 33 publications

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“…The work in [60] proposed an active contour model for the segmentation of the Suspicious regions (SRs) in thermal breast images (TBIs). The proposed segmentation method comprises three steps.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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“…Among them, mammography is still considered as a consistent technique for screening. However, it exhibits some limitations while applying on high-risk women [5][6][7][8]. Ultrasound is noninvasive and is suitable for young women, but it purely depends on the expertise of the operator.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Segmentation of Inflamed ROI to Improve the Accuracy of Identifying Benign and Malignant Cases in Breast Thermogram

Venkatachalam

Shanmugam

Heltin

et al. 2021

Journal of Oncology

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Effective analysis of breast thermography needs an accurate segmentation of the inflamed region in Infrared Breast Thermal Images (IBTI) which helps in the diagnosis of breast cancer. However, IBTI suffers from intensity inhomogeneity, overlapping regions of interest, poor contrast, and low signal-to-noise ratio (SNR) due to the imperfect image acquisition process. To mitigate this, this work proposes an enhanced segmentation of the inflamed Region of Interest (ROI) using an active contour method driven by the multiscale local and global fitted image (MLGFI) model. The first phase proposes a bilateral histogram difference-based thresholding (BHDT) method for locating the inflamed ROI. This is then used for automatic initialization of active contours driven by MLGFI to segment the inflamed ROI from IBTI effectively. To prove the effectiveness of this segmentation method, its performance is compared with ground truth image and its accuracy is also evaluated with the state-of-the-art methods (Fuzzy C Means (FCM), Chan-Vese (CV-ACM), and K-means). From the analysis, it is found that the proposed method not only increases the precision and the segmentation accuracy but also reduces the oversegmentation and undersegmentation rate significantly. In the second phase, area-based feature (AF) and average intensity-based feature (AIF) along with the GLCM (gray level cooccurrence matrix) based second-order statistical features are extracted from the inflamed ROI. Based on these features, a system is developed to effectively classify the benign and malignant breast conditions. From the results, it is observed that the proposed model exhibits an improved accuracy of 91.5%, sensitivity of 91%, and specificity of 92% compared to the whole breast thermogram. Hence, it is concluded that the proposed method will improve the efficacy of thermal imaging in the diagnosis of breast cancer.

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“…Images are segmented with respect to information such as color intensities and textures. Depending on the image information, we can further classify these models into edge-based ACMs [15][16][17][18][19] and region-based ACMs [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Saliency-Driven Active Contour Model for Image Segmentation

et al. 2020

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Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

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Suspicious-Region Segmentation From Breast Thermogram Using DLPE-Based Level Set Method

Cited by 47 publications

References 33 publications

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

Enhanced Segmentation of Inflamed ROI to Improve the Accuracy of Identifying Benign and Malignant Cases in Breast Thermogram

Saliency-Driven Active Contour Model for Image Segmentation

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