Adaptive Distance Preserving Level Set Evolution for Image Segmentation

A Novel Adaptive Level Set Segmentation Method

“…According to the literature [5, 7] and (4) and (7), get Δ ϕ ADPLS and Δ ϕ LBF and then obtain Δ ϕ by solving (9). …”

“…The parameters used in LBF model are Δ t LBF = 0.1, ε 2 = 1.0, σ = 3.0, and the parameter of punishment μ = 0.002∗255∗255; the values of λ 1 and λ 2 depend on the actual image to be segmented which is described in the literature [14]. The parameters used in ADPLS method are Δ t GIF = 1.0, ε 1 = 1.5, σ = 2.0, and the parameter of punishment β = 0.2/Δ t GIF , α = 10; the value of c should be small when dealing with simple images and be great with multilayer-complex images, which is referred to in the literature [7]. The constant coefficient d in w ( I ) of (10) is set by experience.…”

“…Let Ω ⊂ R 2 be the image domain, and let I : Ω → R be a given gray level image. The ADPLS method [ 7 ] was recently proposed to overcome the disadvantage of the distance preserving level set method proposed by He et al, which requires the initial curve surrounding (let in or keep out) the objects to be detected. It imports a variable weighting coefficient whose sign symbol and size are adjusted by image information.…”

“…Since the introduction by Kass et al [ 1 ], active contour models (ACMs) have been widely used in image segmentation [ 2 – 4 ]. The existing ACMs based on the level set method initially proposed to handle the topological changes during the curve evolution can be broadly classified as either edge-based models [ 5 – 7 ] or region-based models [ 8 – 15 ] according to the type of adopted image features. The basic idea of the level set method is to implicitly embed the moving contour into a higher dimensional level set function and view the contour as its zero level set [ 16 ].…”

“…Edge-based models utilize image gradient to stop evolving contours on the object boundaries. Recently, He et al proposed adaptive distance preserving level set (ADPLS) [ 7 ] evolution for image segmentation, in which the initial curve is no longer required to surround (let in or keep out) the objects to be detected. Moreover, starting with only one initial curve whose position is anywhere in the image, it can automatically detect interior and exterior contours of an object and edges of multiobjects.…”