Global Threshold and Region-Based Active Contour Model for Accurate Image Segmentation

Altarawneh, Nuseiba M.; Luo, Siwei; Regan, Brian; Sun, Changming; Jia, Fucang

doi:10.5121/sipij.2014.5301

Cited by 10 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where C 1 and C 2 are defined in (8). The SPF function modulates the signs of the pressure force inside and outside the region of interest so that the curve shrinks when outside the object, or expands when inside the object.…”

Section: The Acm With Sbgfrls Modelmentioning

confidence: 99%

“…Moreover, the level set function of this model is regularized by the selective binary and Gaussian filtering SBGFRLS which reduces the computational coast of the reinitialization step which in turn makes it more efficient than the traditional level set methods [8]. However, the ACM with SBGFRLS has two major disadvantages: the parameter α must be tuned according to images which make the model parameter dependable.…”

Section: The Acm With Sbgfrls Modelmentioning

confidence: 99%

“…The fundamental idea of ACM is to evolve a curve under some constraints from a given image to reach the boundaries of the desired objects, by minimizing energy functional. According to the energy, ACM are classified into two main categories: edge-based models [1][2][3][4] and region-based models [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Abdallah

Elkourd²

2016

ELCVIA

View full text Add to dashboard Cite

Intensity inhomogeneity is a well-known problem in image segmentation. In this paper, we present a new region-based active contour model for image segmentation which can handle intensity inhomogeneity problem. This model introduced a new region-based signed pressure force (SPF) function, which uses the local mean values provided by the local binary fitting (LBF) model. In addition, the proposed model utilizes a new regularization operation such as morphological opening and closing to regularize the level set function in the evolution process. Experimental results on synthetic and real images show that the proposed model gives satisfactory segmentation results as well as less sensitivity to the initial contour location and less time consuming compared with the related methods.

show abstract

Section: The Acm With Sbgfrls Modelmentioning

confidence: 99%

Section: The Acm With Sbgfrls Modelmentioning

confidence: 99%

See 1 more Smart Citation

Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Abdallah

Elkourd²

2016

ELCVIA

View full text Add to dashboard Cite

show abstract

“…Methods that use texture properties work to simulate processes in human's brain. On the other hand, level set methods have been widely used in segmentation of medical images [25][26][27]. Despite of their wide and effective uses, successful application of level set relies heavily on the initial position.…”

Section: Introductionmentioning

confidence: 99%

A Modified Distance Regularized Level Set Model for Liver Segmentation from CT Images

Altarawneh¹,

Luo²,

Regan³

et al. 2015

SIPIJ

Self Cite

View full text Add to dashboard Cite

Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our experimental results confirm that the method yields a satisfactory overall segmentation outcome.Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.

show abstract

“…Finally, texture based methods attempt to emulate the procedure that human's brain processes. Level set methods have been investigated and widely utilized in image segmentation especially for medical images segmentation [20][21][22].Current approaches in using level set methods represent promising approaches for segmenting irregular object shapes such as liver. However it has a strict requirement on the initial position.…”

Section: Introductionmentioning

confidence: 99%

Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force

Altarawneh¹,

Luo²,

Regan³

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Self Cite

View full text Add to dashboard Cite

Organ segmentation from medical images is still an open problem and liver segmentation is a much more challenging task among other organ segmentations. This paper presents a liver segmentation method from a sequence of computer to mography images.We propose a novel balloon force that controls the direction of the evolution process and slows down the evolving contour in regions with weak or without edges and discourages the evolving contour from going far away from the liver boundary or from leaking at a region that has a weak edge, or does not have an edge. The model is implemented using a modified Distance Regularized Level Set (DRLS) model. The experimental results show that the method can achieve a satisfactory result. Comparing with the original DRLS model, our model is more effective in dealing with over segmentation problems.

show abstract

Global Threshold and Region-Based Active Contour Model for Accurate Image Segmentation

Cited by 10 publications

References 17 publications

Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

Fast Region-based Active Contour Model Driven by Local Signed Pressure Force

A Modified Distance Regularized Level Set Model for Liver Segmentation from CT Images

Liver Segmentation from CT Images Using a Modified Distance Regularized Level Set Model Based on a Novel Balloon Force

Contact Info

Product

Resources

About