Breast ultrasound images gland segmentation

Braz, Rui; Pinheiro, António; Moutinho, J.; Freire, Mário M.; Pereira, Manuela

doi:10.1109/mlsp.2012.6349748

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…In clinical routine, automatic breast ultrasound image segmentation is very challenging and essential for cancer diagnosis and treatment planning. Braz et al used the unsupervised K-means and the supervised SVM models to classify the image pixel descriptors for breast gland segmentation on breast ultrasound (BUS) images [132]. Image preprocessing techniques were performed to generate the image pixel descriptors.…”

Section: B-mode Motion Trackingmentioning

confidence: 99%

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review

Zhou,

Yang,

Liu

2020

Preprint

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Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies. Despite its safety and efficacy, QUS suffers from several major drawbacks: poor imaging quality, inter-and intra-observer variability which hampers the reproducibility of measurements. Therefore, it is in great need to develop automatic method to improve the imaging quality and aid in measurements in QUS. In recent years, there has been an increasing interest in artificial intelligence (AI) applications in ultrasound imaging. However, no research has been found that surveyed the AI use in QUS. The purpose of this paper is to review recent research into the AI applications in QUS. This review first introduces the AI workflow, and then discusses the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.

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Section: B-mode Motion Trackingmentioning

confidence: 99%