Multi-phase level set method for precise segmentation and correction of brain MRI

Yang, Yunyun; Zhong, Sheng-hua

doi:10.1007/s11760-020-01724-1

Cited by 3 publications

(8 citation statements)

References 24 publications

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“…Note that, the above limitation exists for all local models; therefore, the performance of these methods depends on the neighbourhood radius. But in the proposed function in (7), this is more significant. To solve this problem, Equation ( 7) is modified as following: 13)…”

Section: Methods Descriptionmentioning

confidence: 93%

“…In the three images of the first row of Figure 3, pixel x and its local neighbour area and evolving contour C are shown. Assume in all these images, based on relation (7), pixel x is detected to be on the 3, the cross section, the column containing pixel x, of each image is displayed. This problem arises because we assume in all images, I (x) is equal to…”

Section: Methods Descriptionmentioning

confidence: 99%

“…where E LSW x is defined in (7). If we assume 𝜆 in ( 7) is −1 for the bright region extraction and +1 for the dark region extraction,…”

Section: Methods Descriptionmentioning

confidence: 99%

“…According to (9), each inside-contour pixel is weighed with its own value; consequently, high-intensity local pixels in region A take large weights; accordingly f in , E D and convergence speed of contours increase. Thus, by appropriately choosing of f in , f out and 𝜆 in (7), the target regions (bright or dark) can be segmented.…”

Section: Methods Descriptionmentioning

confidence: 99%

Section: Methods Descriptionmentioning

confidence: 99%

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A level‐set method for inhomogeneous image segmentation with application to breast thermography images

Koshki

Ahmadzadeh

Zekri

et al. 2020

IET Image Processing

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Various level‐set methods have been suggested for segmenting images with intensity inhomogeneity as local region‐based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region‐based active contour model called local self‐weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well‐known local level‐set methods which show the perfect capability of the model. The segmentation results were evaluated using the F‐score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F‐score, precision and recall criteria on the segmentation of breast thermography images, respectively.

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Section: Methods Descriptionmentioning

confidence: 93%

Section: Methods Descriptionmentioning

confidence: 99%

“…where E LSW x is defined in (7). If we assume 𝜆 in ( 7) is −1 for the bright region extraction and +1 for the dark region extraction,…”

Section: Methods Descriptionmentioning

confidence: 99%

Section: Methods Descriptionmentioning

confidence: 99%

Section: Methods Descriptionmentioning

confidence: 99%