Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

Ramadan, Hiba; Lachqar, Chaymae; Tairi, Hamid

doi:10.1016/j.bspc.2020.101945

Cited by 15 publications

(3 citation statements)

References 41 publications

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“…35 ), ultrasound image of breast tumor (reproduced with permission from ref. 36 ), and MR image of the left ventricle. Yellow curves denote the boundaries obtained by the level-set algorithm, magenta curves represent the final contours obtained by the crack-propagation method, and green curves denote the manual segmentation boundaries.…”

Section: Resultsmentioning

confidence: 99%

“…The middle-right section of this image has an interference region similar to image (h), which affects the segmentation accuracy of the level-set algorithm. Image (j) is an ultrasound image of a breast tumor 36 . Different from the ultrasound image (e) for the cross section of a blood vessel, which has uniform grayscale values inside the target area, this image contains substantial transverse texture information within the target area in addition to the interference around the tumor.…”

Section: Resultsmentioning

confidence: 99%

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Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Huang

et al. 2020

Nat Commun

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Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Huang

et al. 2020

Nat Commun

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show abstract

“…Salient object detection (SOD) aims to recognize notable objects of a given scene [2]. It is broadly utilized in medical image segmentation [3][4][5], object tracking [6,7], personal re-identification [8,9], monitoring applications [10][11][12], video segmentation [13], etc.…”

Section: Introductionmentioning

confidence: 99%

RGBD Salient Object Detection, Based on Specific Object Imaging

Liao

et al. 2022

Sensors

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RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can more intuitively display the complete information of the detection target. To take care of this issue, we propose a RGBD salient object detection method, based on specific object imaging, which can quickly capture and process important information on object features, and effectively screen out the salient objects in the scene. The screened target objects include not only the edge of the object, but also the complete feature information of the object, which realizes the detection and imaging of the salient objects. We conduct experiments on benchmark datasets and validate with two common metrics, and the results show that our method reduces the error by 0.003 and 0.201 (MAE) on D3Net and JLDCF, respectively. In addition, our method can still achieve a very good detection and imaging performance in the case of the greatly reduced training data.

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Breast Tumor Segmentation in Ultrasound Images Based on U-NET Model

Michael

2022

Advances in Intelligent Systems and Computing

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Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images

Cited by 15 publications

References 41 publications

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

RGBD Salient Object Detection, Based on Specific Object Imaging

Breast Tumor Segmentation in Ultrasound Images Based on U-NET Model

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