A novel level set model with automated initialization and controlling parameters for medical image segmentation

Liu, Qingyi; Jiang, Mingyan; Bai, Peirui; Yang, Guang

doi:10.1016/j.compmedimag.2015.12.005

Cited by 24 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main additional computational cost in the proposed model is for computing w j in Equation ( 22) compared with representative models reviewed in Section 2. However, we notice that GðxÞ and I j ðxÞ are independent of the level set functions Φ and clustering centers C which indicate that we can compute GðxÞG T ðxÞ for Equation (23) and I j ðxÞGðxÞ for Equation (24) in advance and keep the results fixed during the iteration to accelerate the proposed model.…”

Section: Extension To Multichannel Case Gincmentioning

confidence: 99%

“…As one of the most important improvements of ACMs, level set methods regard the active contour as the zero-level set contour of a predefined one-dimension higher function named as level set function in the literature. Motion of the contour is implied in evolution of the entire level set function under a principled energy minimization framework instead of directly driving the contour itself [23]. Therefore, interesting elastic behaviours of the active contour are preserved with topological changes of the contour efficiently being handled implicitly during the evolution of the level set function.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Feng

Yang

Lou

et al. 2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

show abstract

Section: Extension To Multichannel Case Gincmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Feng

Yang

Lou

et al. 2020

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Mean shift algorithm is a statistical iteration in data density distribution to find the local extremum, which is widely used in computer‐vision‐related research . The principle of mean shift algorithm can be described as follows: an initial point, which is randomly determined in the sample point space, is set as a center, and an initial circle is obtained by setting the radius; then, vectors from each point in the circle to the initial center point and their average (offset mean) are calculated; after that, the center point is moved to the offset mean and repeats the above steps until the stop condition is satisfied …”

Section: Discription Of the Algorithmmentioning

confidence: 99%

A cascaded Mura defect detection method based on mean shift and level set algorithm for active‐matrix OLED display panel

Sun

Xiao

2018

J Soc Info Display

View full text Add to dashboard Cite

Active‐matrix OLED (AMOLED), as the next‐generation display technology, is being commercially promoted rapidly. And Mura defects occur unavoidable during different phases of the AMOLED panel production process. In this paper, a cascaded Mura detection method leveraging the mean shift and the level set algorithm is proposed. First, we use the mean shift algorithm to find the general contour of the Mura defects that circumvent the issue in established level set segmentation approach wherein the use of the local image information is sensitive to the initial contour. Then, we improve the level set model that combines global and local information to segment Mura defects accurately. The integration of local image information can levitate the challenges in the global image model, which cannot separate the local intensity inhomogeneity and texture background individually. The experiments show that the cascaded method has a superior capability in terms of both accuracy and efficiency.

show abstract

“…Numerous reviews (e.g., 1 [1,2]) present methods to treat segmentation of medical images as a general image processing problem, while others use a priori information relevant to the specific type of the images. Conventional segmentation methods include thresholding [3][4][5][6][7], neural networks [8][9][10][11][12][13][14][15], mode-based methods (such as expectation-maximization) [16,17], clustering [18,19], region growing [20], deformable active contours (snakes) [3,[21][22][23][24][25] and level set methods [26]. The segmentation is usually followed by feature extraction to distinguish malignant and benign masses.…”

Section: Introductionmentioning

confidence: 99%

Phase portrait analysis for automatic initialization of multiple snakes for segmentation of the ultrasound images of breast cancer

Kirimasthong

Rodtook

Chaumrattanakul

et al. 2016

Pattern Anal Applic

View full text Add to dashboard Cite

Segmentation of ultrasound (US) images of breast cancer is one of the most challenging problems of modern medical image processing. A number of popular codes for US segmentation are based on the active contours (snakes) and on a variety of modifications of gradient vector flow. The snakes have been used to locate objects in various applications of medical images. However, the main difficulty in applying the method is initialization. Therefore, we suggest a new method for automatic initialization of active contours based on phase portrait analysis (PPA) of the underlying vector field and a sequential initialization of trial multiple snakes. The PPA makes it possible to exclude the noise and artifacts and properly initialize the multiple snakes. In turn, the trial snakes allow us to differentiate between the seeds initialized inside and outside the desired object. While preceding methods require the manual selection of at least one seed point inside the object or rely on the particular distribution of the gray levels, the proposed method is fully automatic and robust to the noise, as can be seen from the tests with synthetic and real images.

show abstract

A novel level set model with automated initialization and controlling parameters for medical image segmentation

Cited by 24 publications

References 16 publications

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

A cascaded Mura defect detection method based on mean shift and level set algorithm for active‐matrix OLED display panel

Phase portrait analysis for automatic initialization of multiple snakes for segmentation of the ultrasound images of breast cancer

Contact Info

Product

Resources

About