Assesment of Tumor in Breast MRI using Kapur's Thresholding and Active Contour Segmentation

Kirthika, A.; Raja, N. Sri Madhava; Sivakumar, R.; Arunmozhi, S.

doi:10.1109/icscan49426.2020.9262402

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Traditional image segmentation algorithms are based on machine-learning algorithms, which are mainly divided into threshold-based segmentation, region-based segmentation, and edge-based segmentation. Kirthika et al [13] achieved extractive segmentation of tumors by selecting a target value, searching for the optimal threshold value by using the cuckoo-search (CS) algorithm until this target threshold is found, and using active-contour at the boundary based on convergent pixel enhancement. Arjmand et al [14] achieved the differentiation of background, healthy tissue, and lesion areas by using a clustering method to segment breast tumors, using k-means clustering based on the characteristics of the lesion areas that differ from the surrounding background areas, and optimizing the initialized center of mass by the CS algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms

Zhang,

Xiao,

Zheng

2023

Scientific Programming

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Breast cancer is a highly prevalent cancer. Triple-negative breast cancer (TNBC) is more likely to recur and metastasize than other subtypes of breast cancer. Research on the treatment of TNBC is of great importance, and accurate segmentation of the breast lesion area is an important step in the treatment of TNBC. Currently, the gold standard for tumor segmentation is still sketched manually by doctors, which requires expertise in the field of medical imaging and consumes a great deal of doctors’ time and energy. Automatic segmentation of breast cancer not only reduces the burden of doctors but also improves work efficiency. Therefore, it is of great significance to study the automatic segmentation technique for breast cancer lesion regions. In this paper, a deep-learning-based automatic segmentation algorithm for TNBC images is proposed. The experimental data were dynamic contrast-enhanced magnetic resonance imaging TNBC dataset provided by the Cancer Hospital of Zhengzhou University. The experiments were analyzed by comparing several models with UNet, Attention-UNet, ResUNet, and SegNet and using evaluation indexes such as Dice score and Iou. Compared to UNet, Attention-UNet, ResUNet, and SegNet, the proposed method improved the Dice score by 2.1%, 1.54%, 0.88%, and 9.65%, respectively. The experimental results show that the proposed deep-learning-based TNBC image segmentation model can effectively improve the segmentation performance of TNBC tumors.

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Section: Literature Reviewmentioning

confidence: 99%

Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms

Zhang,

Xiao,

Zheng

2023

Scientific Programming

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“…Image segmentation is widely used in the medical world, including to identify organ pixels or lesions from medical images such as CT-scan or magnetic resonance imaging (MRI) [5], [6]. One of the segmentation methods that can be used is Thresholding [7], [8]. One of the image segmentation methods that can be used is thresholding, in which the processing is based on differences in the percentage of gray images.…”

Section: Introductionmentioning

confidence: 99%

“…The gray level of the image is expressed by I to L. The I level starts from 1, namely pixel 0. For L, the maximum level Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7. No.…”

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confidence: 99%

Image Transformation With Lung Image Thresholding and Segmentation Method

Sitanggang¹,

Yuhandri

Setiawan³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Image transformation is important to obtain and find certain information about an image that was not previously known, such as pixels, geometry, size, and color. Following this, this research aims to analyze image transformation in producing better values using threshold and segmentation methods. The segmentation process is carried out based on two color models, namely hue saturation value (HSV) and red green blue (RGB). The image data used in this study was the x-ray image of the lungs from www.fk.unair.ac.id. which is processed using the Matlab 2021a application to help the analysis process. on the results of the image segmentation analysis carried out in this case, the greater the HSV and RGB threshold values used in the image data, the better and clearer the segmentation of the detected image results. In other words, the size of the thresholding value generated greatly affects the quality, brightness, size, and color of the resulting image. The best lung X-ray image segmentation results were obtained when using the threshold values HSV = 0.9 and RGB = 9.

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Abnormality Detection in Breast Thermograms Using Modern Feature Extraction Technique

Shenoy,

Satra,

Dholakia

et al. 2023

Lecture Notes in Networks and Systems

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Assesment of Tumor in Breast MRI using Kapur's Thresholding and Active Contour Segmentation

Cited by 5 publications

References 12 publications

Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms

Image Segmentation of Triple-Negative Breast Cancer by Incorporating Multiscale and Parallel Attention Mechanisms

Image Transformation With Lung Image Thresholding and Segmentation Method

Abnormality Detection in Breast Thermograms Using Modern Feature Extraction Technique

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