scholarly journals Non-local weighted fuzzy energy-based active contour model with level set evolution starting with a constant function

2019 ◽  
Vol 13 (7) ◽  
pp. 1115-1123 ◽  
Author(s):  
Hongli Lv ◽  
Shujun Fu ◽  
Caiming Zhang ◽  
Xuya Liu
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Constant Function ◽  
Contour Model ◽  
Non Local ◽  
Fuzzy Energy ◽  
Level Set Evolution

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals Robust Active Contour Model Guided by Local Binary Pattern Stopping Function

2017 ◽  
Vol 17 (4) ◽  
pp. 165-182 ◽  
Author(s):  
Abdallah Azizi ◽  
Kaouther Elkourd ◽  
Zineb Azizi
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Local Binary Pattern ◽  
Active Contour Model ◽  
Intensity Variation ◽  
Level Set Function ◽  
Contour Model ◽  
Local Intensity ◽  
Edge Based ◽  
Set Function

AbstractEdge based active contour models are adequate to some extent in segmenting images with intensity inhomogeneity but often fail when applied to images with poorly defined or noisy boundaries. Instead of the classical and widely used gradient or edge stopping function which fails to stop contour evolution at such boundaries, we use local binary pattern stopping function to construct a robust and effective active contour model for image segmentation. In fact, comparing to edge stopping function, local binary pattern stopping function accurately distinguishes object’s boundaries and determines the local intensity variation dint to the local binary pattern textons used to classify the image regions. Moreover, the local binary pattern stopping function is applied using a variational level set formulation that forces the level set function to be close to a signed distance function to eliminate costly re-initialization and speed up the motion of the curve. Experiments on several gray level images confirm the advantages and the effectiveness the proposed model.

Heuristic Initialization for Active Contour Models in CT/MRI Image Processing

2015 ◽  
Vol 15 (03) ◽  
pp. 1550010
Author(s):  
Hao Liu ◽  
Hongbo Qian ◽  
Ning Dai ◽  
Jianning Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Heuristic Method ◽  
Initial Contour ◽  
Active Contour Models ◽  
Contour Model ◽  
Segmentation Approach ◽  
Edge Points ◽  
Contour Models ◽  
Level Set Evolution

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

scholarly journals An Active Contour Model Based on Adaptive Threshold for Extraction of Cerebral Vascular Structures

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jiaxin Wang ◽  
Shifeng Zhao ◽  
Zifeng Liu ◽  
Yun Tian ◽  
Fuqing Duan ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Boundary Term ◽  
Cerebral Vessel ◽  
Penalty Term ◽  
Level Set Function ◽  
Contour Model ◽  
Global Threshold

Cerebral vessel segmentation is essential and helpful for the clinical diagnosis and the related research. However, automatic segmentation of brain vessels remains challenging because of the variable vessel shape and high complex of vessel geometry. This study proposes a new active contour model (ACM) implemented by the level-set method for segmenting vessels from TOF-MRA data. The energy function of the new model, combining both region intensity and boundary information, is composed of two region terms, one boundary term and one penalty term. The global threshold representing the lower gray boundary of the target object by maximum intensity projection (MIP) is defined in the first-region term, and it is used to guide the segmentation of the thick vessels. In the second term, a dynamic intensity threshold is employed to extract the tiny vessels. The boundary term is used to drive the contours to evolve towards the boundaries with high gradients. The penalty term is used to avoid reinitialization of the level-set function. Experimental results on 10 clinical brain data sets demonstrate that our method is not only able to achieve better Dice Similarity Coefficient than the global threshold based method and localized hybrid level-set method but also able to extract whole cerebral vessel trees, including the thin vessels.

Texture Segmentation of Natural Images Based on Active Contour Model

2012 ◽  
Vol 546-547 ◽  
pp. 553-558
Author(s):  
Zhan Wang ◽  
Yun Hui Yan ◽  
De Wei Dong ◽  
Ke Chen Song
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Synthesis ◽  
Texture Features ◽  
Texture Segmentation ◽  
Natural Images ◽  
Contour Model ◽  
Signal Features ◽  
Environmental Image

To segment complex texture natural environment images; the first, the texture features of natural images should be analysed and the texture features should be extracted; The second, texture images segmengtation can be achieved by using Mumford-Shah active contour model, this segmentation model can better process fuzzy, default boundary, and this model can be solved by level set method. This method can express well complex texture signal features of natural images. Through making texture segmentation experiment for standard texture synthesis image and natural environmental image, its results show that the texture segmentation based on Mumford-Shah active contour model can segment natural images.

scholarly journals Fractional Differentiation-Based Active Contour Model Driven by Local Intensity Fitting Energy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Gu ◽  
Renfang Wang
Keyword(s):  
Fractional Order ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Input Image ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Level Set Function ◽  
Contour Model ◽  
Proposed Model

A novel active contour model is proposed for segmentation images with inhomogeneity. Firstly, fractional order filter is defined by eight convolution masks corresponding to the image orientation in the eight compass directions. Then, the fractional order differentiation image is obtained and applied to the level set method. Secondly, we defined a new energy functional based on local image information and fractional order differentiation image; the proposed model not only can describe the input image more accurately but also can deal with intensity inhomogeneity. Local fitting term can enhance the ability of the model to deal with intensity inhomogeneity. The defined penalty term is used to reduce the occurrence of false boundaries. Finally, in order to eliminate the time-consuming step of reinitialization and ensure stable evolution of level set function, the Gaussian filtering method is used. Experiments on synthetic and real images show that the proposed model is efficient for images with intensity inhomogeneity and flexible to initial contour.

scholarly journals Image Segmentation Using Signed Pressure Force Based Active Contour Model

2013 ◽  
Vol 12 (1) ◽  
pp. 3195-3200
Author(s):  
Sagar Chouksey ◽  
Mayur Ghadle ◽  
Shreya Sharma ◽  
Rohan Puranik
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Computational Cost ◽  
Initial Contour ◽  
Pressure Force ◽  
Binary Function ◽  
Level Set Function ◽  
Contour Model ◽  
Set Function

A novel signed pressure force (SPF) based active contour model (ACM) is proposed in this work. It is implemented with help of Gaussian filtering regularized level set method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of this method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed SPF with ACM has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient. The computational cost for traditional re-initialization can also be reduced.

An Improved Integrated Active Contour Model without Re-Initialization for Vector-Valued Images Segmentation

2012 ◽  
Vol 429 ◽  
pp. 271-276 ◽  
Author(s):  
Ji Zhao ◽  
Fu Qun Shao ◽  
Ji Zhao ◽  
Xue Dong Zhang ◽  
Chuang Feng
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Nonlinear Heat Equation ◽  
Level Set Function ◽  
Implementation Scheme ◽  
Level Set Evolution ◽  
Set Function ◽  
Vector Images

In this paper, an improved variational formulation for active contours model is introduced to force level set function to become fast and stably close to signed distance function, which can completely eliminate the need of the costly re-initialization procedure. A restriction item that is a nonlinear heat equation with balanced diffusion rate is attached to variational Integrated Active Contour (IAC) model on the basis of analysis on regions and edges information from all channels of the valued-vector images, so that the level set evolution segmentation process becomes fast and stable. In addition, more efficient discretization method with spatial rotation-invariance gradient and divergence operator is proposed as numerical implementation scheme. Finally, the experiments on some images have demonstrated the efficiency, accuracy and robustness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document