Active contours textural and inhomogeneous object extraction

Mabood, Lutful; Ali, Haider; Badshah, Noor; Chen, Ke; Khan, Gulzar Ali

doi:10.1016/j.patcog.2016.01.021

Cited by 33 publications

(33 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Suppose that I: Ω⊂R 2 is an image domain, I: Ω → R 2 is an input image, and C(q) is a closed curve. Then, the GAC model is formalized by minimizing the following energy functional as E GAC = 1 0 g(|∇I(C(q))|)|C (q)|dq, (1) where g is a strictly decreasing function. Usually, a satisfactory edge stopping function (ESF) should be defined, which is regular and positive at object boundaries [21], e.g.,…”

Section: The Gac Modelmentioning

confidence: 99%

“…where G σ denotes the Gaussian kernel function and G σ *I describes the convolution operation of I with G σ . Using the steepest descent method and the calculus of variations, we obtain the Euler-Lagrange form of Equation (1), which is written as…”

Section: The Gac Modelmentioning

confidence: 99%

“…Image segmentation is an important task in the field of image analysis and object detection and aims to segment an image into distinctive subregions that are meaningful to analyze [1]. Segmentation is the intermediate step between image processing and image analysis as well as the bridge from lowto high-level research in computer vision.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Sun

Meng

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF-and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.2 of 20 an object with strong edges; however, they cannot detect the weak edges of an object. Moreover, the methods are sensitive to noise and do not easily obtain satisfactory segmentation results for blurred images [2]. In addition, the contour should initially be set near the object; otherwise, it is difficult to obtain correct segmentation results [17]. Region-based models make full use of image statistical information, whereas edge-based models do not. Thus, region-based models have multiple advantages over edge-based models. For example, because regional information is used, region-based models are less sensitive to contour initialization and noise. Furthermore, these region-based models can easily segment images with weak boundaries or even those without boundaries [18]. One of the most typical region-based methods was proposed by Chan and Vese (C-V) [11], which is based on the Mumford-Shah functional [19]. The C-V model is based on the assumption that image intensities are homogeneous in each region. However, this assumption does not suit the intensity of inhomogeneous images, which limits the method's further applications [20,21].Recently, hybrid methods have gained popularity among region-based methods. These methods combine region (local or global) and edge information in their energy formulations [22]. Zhang et al. [23] proposed the selective binary and Gaussian filtering regularized level set (SBGFRLS) model. This model combines the advantages of region-based and edge-based active contours and introduces a region-based SPF function, which utilizes the image global intensity means from the C-V method. This metho...

show abstract

Section: The Gac Modelmentioning

confidence: 99%

Section: The Gac Modelmentioning

confidence: 99%

See 1 more Smart Citation

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Sun

Meng

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The purpose of texture image segmentation is to divide a provided image into multiple regions with similar texture appearance [1]. In the past decades, a number of texture image segmentation methods have been introduced, which could be generally divided into three groups: region based [2], graph based [3], and active contour model (ACM) based methods [4]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Most of segmentation methods of texture images, such as many histological images [9], natural images [10], or synthetic structures [4], generally consists of two steps, extracting features and building a model for segmentation. In the feature extraction step, a variety of features have been proposed to capture local statistical properties…”

Section: Introductionmentioning

confidence: 99%

Neutrosophic Set Transformation Matrix Factorization Based Active Contours for Color Texture Segmentation

Dong

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Active contour model (ACM) has widely used for segmenting two-phase images. However, its performance may not be satisfactory for some color texture images when their features cannot be effectively extracted. To alleviate this problem, in this paper, a novel neutrosophic set transformation matrix factorization-based active contour (NSTMF-AC) approach is proposed for color texture segmentation. The proposed NSTMF-AC is an effective and robust color texture segmentation method. Particularly, to effectively capture a wide range of texture information, the proposed method extracts the features from the triple domains, including spatial, wavelet, and spectral domains, and then uses neutrosophic set (NS) transform and the corresponding operations to reduce the indeterminacy contained in the image. Furthermore, the method obtains the resulting NS transformation matrix and utilizes a factorization-based ACM to perform image segmentation. Finally, the proposed method is compared with the state-of-the-art segmentation algorithms on a variety of natural images. The experimental results demonstrate that the proposed NSTMF-AC method is more robust for two-phase image segmentation than other methods. INDEX TERMS Texture segmentation, neutrosophic set, matrix factorization, active contour model.

show abstract

Image-selective segmentation model for multi-regions within the object of interest with application to medical disease

et al. 2020

View full text Add to dashboard Cite

Detection and extraction of an object of interest and accurate boundaries segmentation in a given image has been of interest in the last decades due to its application in different fields. To successfully segment a single object, interactive/selective segmentation techniques has been developed as a supplement to the existing global segmentation techniques. Even though existing interactive/ selective segmentation techniques perform well in segmenting the images with prominent edges those methods are less efficient or even fail in segmenting images having muti-regions of different intensity scale. In this paper, we design a new variational selective segmentation model which incorporates the idea of area-based fitting term along with a signed pressure force function based on a generalized average into a variational energy function. The new model is capable to capture the object of interest which can be single or multi-region within the object of interest. To evaluate the performance of our new model, we compare our results with state of the art models by showing same efficiency and reliability on detecting single-region and an outperforming for multi-region selective segmentation. Comparison tests were carried out on synthetic and real data images.

show abstract

Active contours textural and inhomogeneous object extraction

Cited by 33 publications

References 20 publications

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

Neutrosophic Set Transformation Matrix Factorization Based Active Contours for Color Texture Segmentation

Image-selective segmentation model for multi-regions within the object of interest with application to medical disease

Contact Info

Product

Resources

About