Multi-slices breast ultrasound lesion segmentation using Multi-Scale Vector Field Convolution snake

Sassi, O.; Sellami, Lamia; Slima, Mohamed Ben; Hamida, Ahmed Ben; Chtourou, Khalil

doi:10.1109/atsip.2014.6834604

Cited by 1 publication

(2 citation statements)

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“…The edge-based approach is dependent on the threshold or local filtering, whereas the region-based approach is positioned on pixels which involve the selection of initial seed points to obtain the lesion [26,27]. The most popular edge-based methods are balloon snake [28,29], gradient vector flow snake [30,31], vector field convolution (VFC) [15] and multi-scale VFC (MsVFC) [32,33]. However, in these edge-based approaches, if noise is present in the image, it might prevent the snake from finding the correct contours of an object, and as a result the object contour is closer by the initial snake curve [32].…”

Section: Introductionmentioning

confidence: 99%

“…The most popular edge-based methods are balloon snake [28,29], gradient vector flow snake [30,31], vector field convolution (VFC) [15] and multi-scale VFC (MsVFC) [32,33]. However, in these edge-based approaches, if noise is present in the image, it might prevent the snake from finding the correct contours of an object, and as a result the object contour is closer by the initial snake curve [32]. It is also sensitive to the weak edge.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid segmentation method based on multi‐scale Gaussian kernel fuzzy clustering with spatial bias correction and region‐scalable fitting for breast US images

Panigrahi¹,

Verma²,

Singh³

2018

IET Computer Vision

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Automated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi-scale Gaussian kernel induced fuzzy C-means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region-scalable fitting energy function. The result obtained from the MsGKFCM_S method is utilised to initialise the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters of the curve evolution process. The proposed approach is evaluated on a database of 127 breast US images consisting of 75 malignant and 52 solid benign cases. The performance of proposed approach is compared with other related techniques, using performance measures such as Jaccard Index, dice similarity, shape similarity, Hausdroff difference, area difference, accuracy and F-measure. Results indicate that the proposed approach can successfully detect lesions in breast US images, with high accuracy of 97.889 and 97.513%. Moreover, the proposed approach has the capability of handling shadowing effects, acoustic enhancement and multiple lesions.

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