A region‐based segmentation method for ultrasound images in HIFU therapy

Zhang, Dong; Liu, Yu; Yang, Yan; Xu, Mei‐Dong; Yu, Yan; Qin, Qianqing

doi:10.1118/1.4950706

Cited by 16 publications

(10 citation statements)

References 52 publications

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“…Segmenting the tumor from a medical image can improve the diagnostic accuracy of doctors, and become a guide for the surgery. In the past, many two-dimensional (2D) image segmentation techniques have been developed, such as the active contour model (ACM) [ 1 ], region growing, zero crossing [ 2 ], thresholding [ 3 ], region-based segmentation [ 4 ], watershed [ 5 ], fuzzy c-means (FCM), texture features, and the level set method (LSM) [ 6 , 7 ]. However, breast MRI has a relatively low resolution, and tumor boundaries are often indistinct as tumors infiltrate surrounding healthy tissue.…”

Section: Introductionmentioning

confidence: 99%

3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

Wang

Huang

Yang

2018

Journal of Healthcare Engineering

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Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.

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Section: Introductionmentioning

confidence: 99%

3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

Wang

Huang

Yang

2018

Journal of Healthcare Engineering

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“…However, automatic detection and segmentation in medical images are still challenging, as most of the medical images usually suffer from low contrast, heavy noise, inhomogeneities, and artifacts . In order to facilitate the detection and segmentation, intensity adjustment and noise suppression have been used to improve the image quality …”

Section: Introductionmentioning

confidence: 99%

“…However, as a safe and inexpensive imaging modality, ultrasound is the most commonly used modality in practice. Therefore, an automatic diagnostic assistance system for ultrasound images can provide objective diagnostic support for physicians to discriminate the renal lesions and consequently improve the detection of diseases as well as eliminate manual procedures …”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4] However, automatic detection and segmentation in medical images are still challenging, as most of the medical images usually suffer from low contrast, heavy noise, inhomogeneities, and artifacts. 5,6 In order to facilitate the detection and segmentation, intensity adjustment and noise suppression have been used to improve the image quality. 7 For the purpose of achieving efficient detection and segmentation, some approaches have been proposed to use certain features of specific lesions.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an automatic diagnostic assistance system for ultrasound images can provide objective diagnostic support for physicians to discriminate the renal lesions and consequently improve the detection of diseases as well as eliminate manual procedures. 6,17 In this proposed method, two bottom-up saliency detection models based on global information are implemented to endow saliency values for every pixel, the potential lesion regions are extracted as distinct regions from these two saliency maps. In the subsequent segmentation, local information, i.e., local intensity information and a novel DoGÀLBP feature in local areas, and edge indicators which are represented by intensity variance in small patches are used in the vector valued Chan-Vese (CV) model for the fine segmentation of lesions.…”

Section: Introductionmentioning

confidence: 99%

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Automatic renal lesion segmentation in ultrasound images based on saliency features, improved LBP, and an edge indicator under level set framework

Gui

Yang

2017

Medical Physics

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Purpose: Segmentation of lesions in ultrasound images is widely used for preliminary diagnosis. In this paper, we develop an automatic segmentation algorithm for multiple types of lesions in ultrasound images. The proposed method is able to detect and segment lesions automatically as well as generate accurate segmentation results for lesion regions. Methods: In the detection step, two saliency detection frameworks which adopt global image information are designed to capture the differences between normal and abnormal organs as well as these between lesions and the normal tissues around them. In the segmentation step, three types of local information, i.e., image intensity, improved local binary patterns (LBP) features, and an edge indicator, are embedded into a modified level set framework to carry out the segmentation task. Results: The cyst and carcinoma regions in the ultrasound images of the human kidneys can be automatically detected and segmented by using the proposed method. The efficiency and accuracy of the method are validated by quantitative evaluations and comparative measurements with three wellrecognized segmentation methods. Specifically, the average precision and dice coefficient of the proposed method in segmenting renal cysts are 95.33% and 90.16%, respectively, while those in segmenting renal carcinomas are 94.22% and 91.13%, respectively. The average precision and dice coefficient of the proposed method are higher than those of three compared segmentation methods. Conclusions: The proposed method can efficiently detect and segment the renal lesions in ultrasound images. In addition, since the proposed method utilizes the differences between normal and abnormal organs as well as these between lesions and the normal tissues around them, it can be possibly extended to deal with lesions in other organs of ultrasound images as well as lesions in medical images of other modalities.

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