An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images

Feng, Yuan; Dong, Fenglin; Xia, Xiaolong; Hu, Chun Hong; Fan, Qianmin; Hu, Yanle; Gao, Mingyuan; Mutic, Sasa

doi:10.1002/mp.12350

Cited by 37 publications

(15 citation statements)

References 43 publications

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“…Apparently, all global thresholding methods can be applied in the accumulating process. It was testified that the level set and the FCM could be used in the local window to achieve better results when compared with the traditional ones [34]. Thus, they can be applied in the accumulating process.…”

Section: Multi‐methods Combination Frameworkmentioning

confidence: 99%

Image segmentation framework based on optimal multi‐method fusion

Zheng

Zhang

Huang

et al. 2019

IET Image Processing

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Section: Multi‐methods Combination Frameworkmentioning

confidence: 99%

Image segmentation framework based on optimal multi‐method fusion

Zheng

Zhang

Huang

et al. 2019

IET Image Processing

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“…For region-based segmentation, both traditional methods and machine-learning-based pixel classification methods were investigated. Feng et al [17] adopted an adaptive fuzzy C-means algorithm and the obtained DSC is 0.925. Pons et al [18] reported that their evaluated automated method achieved a DSC of 0.49 using a Markov Random Field (MRF) and a Maximum a Posteriori (MAP) approach, by applying it to clinical data.…”

Section: Fig 1 a Malignant Lesion In Breast Ultrasoundmentioning

confidence: 99%

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

Xing

Wang

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p <0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.

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“…Recently, great progress has been made in processing and segmentation of images and selection of regions of interest (ROIs) in CAD. Feng et al[14] proposed a method of adaptively utilizing neighboring information, which can effectively improve the breast tumor segmentation performance on ultrasound images. Cai et al[15] proposed a phased congruency-based binary pattern texture descriptor, which is effective and robust to segament and classify B-mode ultrasound images regardless of image grey-scale variation.…”

Section: Segmentationmentioning

confidence: 99%

Artificial intelligence in breast ultrasound

Wu¹,

Zhou²,

Xu³

et al. 2019

WJR

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Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

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An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images

Abstract: Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures.

Cited by 37 publications

References 43 publications

Image segmentation framework based on optimal multi‐method fusion

Image segmentation framework based on optimal multi‐method fusion

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

Artificial intelligence in breast ultrasound

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