Watershed segmentation for breast tumor in 2-D sonography

Huang, Yu-Len; Chen, Dar Ren

doi:10.1016/j.ultrasmedbio.2003.12.001

Cited by 173 publications

(100 citation statements)

References 24 publications

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“…Despite the speckled nature of echograms, which makes image segmentation and object delineation a difficult task, various researchers [7][8][9][10][11] have developed segmentation procedures for supporting radiologists recognize abnormal areas on US breast images as well as for their own computer-aided diagnosis (CAD) systems.…”

Section: Introductionmentioning

confidence: 99%

Breast Ultrasound Segmentation Using Morphologic Operators and a Gaussian Function Constraint

et al. 2008

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-This work proposes a hybrid segmentation method based on Morphological Operators and on a Gaussian function constraint to delimitate the search dominium, both used for tumor segmentation purposes The Watershed operator was also used to find tumor contour. The proposed method was firstly evaluated using gear simulated images (eight and sixteen teeth with different width and height), a power speckle noise of 10% and a contrast ratio of 10:53 referred to the background. The hybrid method was also applied to 200 ultrasound (US) breast images. For assessing the segmentation performance, similarity parameters (overlap ratio, RS, normalized residual mean square value, nrv, and mean distance between contours, dmed) based on the predefined shape or on the US tumor contour depicted by an expert as a gold standard. The segmented contour of the gear simulated images obtained with the hybrid method better preserves the original shape. This finding was evident for both gears, which is particularly important since the teeth mimic contour irregularities. For the US breast images, the proposed method keeps the tumor boundaries without aggregating improper structures, resulting in RS > 50% for 91% of the images, nrv < 40% for 70%, and dmed < 5 pixels for 77% of the cases. These findings suggest that the hybrid method represents more realistically tumor contours, which is a vital diagnosis characteristic.

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Section: Introductionmentioning

confidence: 99%

Breast Ultrasound Segmentation Using Morphologic Operators and a Gaussian Function Constraint

et al. 2008

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“…It can handle features that the active contour method has difficulties with, including self-intersecting contours and changes in topology. Both the watershed transformation and levels sets have been applied for segmenting structures on B-mode images (Krivanek and Sonka 1998;Huang and Chen 2004;Angelini et al 2005;Moon et al 2005). Their role for elastographic image segmentation will be the topic of our future work.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of elastographic images using a coarse-to-fine active contour model

Liu¹,

Zagzebski²,

Varghese³

et al. 2006

Ultrasound in Medicine & Biology

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Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.

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“…Accurate segmentation has many applications such as medical images [1][2][3][4][5][6][7][8][9][10] . However, owing to noise, low contrast and intensity inhomogeneity still deserve research [11,12] , for example, edge detection and thresholding [4] . Active contours [13] enables closed and smooth edges to be obtained.…”

Section: Introductionmentioning

confidence: 99%

Active Contour Based on Local Statistic Information and an Attractive Force for Ultrasound Image Segmentation

Yuan¹,

Wang²

2017

Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

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Abstract-This paper presents a new active contour model with local intensities through level set method for ultrasound images segmentation. The method is not affected by the limitation of Gaussian distribution. The model is designed by local intensities, alignment term with a sharpening edge coefficient and regularization. Local intensities have the capability of denoising, and local means and variances are considered. The alignment term with a sharpening edge coefficient can sharpen edge and increase the convergence speed. The numerical schedule is implemented by level set method. Experimental results show that proposed method succeed to segment edges for ultrasound images.

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Watershed segmentation for breast tumor in 2-D sonography

Cited by 173 publications

References 24 publications

Breast Ultrasound Segmentation Using Morphologic Operators and a Gaussian Function Constraint

Breast Ultrasound Segmentation Using Morphologic Operators and a Gaussian Function Constraint

Segmentation of elastographic images using a coarse-to-fine active contour model

Active Contour Based on Local Statistic Information and an Attractive Force for Ultrasound Image Segmentation

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