scholarly journals Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing

2021 ◽  
Vol 17 (11) ◽  
pp. 76
Author(s):  
Ignasius Boli Suban ◽  
Suyoto Suyoto ◽  
Pranowo Pranowo
Keyword(s):  
Image Segmentation ◽  
Parallel Processing ◽  
Level Set ◽  
Fuzzy Clustering ◽  
Lattice Boltzmann ◽  
Rapid Development ◽  
Medical Image Segmentation ◽  
Processing Unit ◽  
Level Set Function ◽  
Set Function

The rapid development of computer technology has had a significant influence on advances in medical science. This development concerns segmenting medical images that can be used to help doctors diagnose patient diseases. The boundary between objects contained in an image is captured using the level set function. The equation of the level set function is solved numerically by combining the Lattice Boltzmann (LBM) method and fuzzy clustering. Parallel processing using a graphical processing unit (GPU) accelerates the execution of the segmentation process. The results showed that image segmentation with a relatively large size could be done quickly. The use of parallel programming with the GPU can accelerate up to 39.22 times compared to the speed of serial programming with the CPU. In addition, the comparisons with other research and benchmark data show consistent results.

A Variational Level Set Remote Sensing SAR Image Segmentation Approach for Oil Spill Detecting Based on Fuzzy Cluster

2015 ◽  
Vol 719-720 ◽  
pp. 1049-1055 ◽  
Author(s):  
Jin Yu Liu ◽  
Zheng Ning Zhang ◽  
He Meng Yang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Oil Spill ◽  
Fuzzy Clustering ◽  
Sar Image ◽  
Segmentation Method ◽  
Level Set Function ◽  
Sar Image Segmentation ◽  
Variational Level Set ◽  
Set Function

Synthetic Aperture Radar (SAR) has become one of the important means for the ocean remote sensing detection of oil spills. The existing SAR image segmentation method has the issues of edge blur, poor contrast, non-uniform intensity image, so the segmentation effect is not ideal. This paper presents a variational level set SAR image of oil spill detection method based on fuzzy clustering. First of all, apply the threshold method on initial segmentation of the original SAR image to transform the initial segmented image as fuzzy clustering. Secondly, introduce the clustering results into the initial level set function to achieve the initial contour. Finally, add fuzzy clustering model in the level set energy function to complete the level set evolution process and get the final segmented image. This paper uses the threshold segmentation results to achieve the initialization of the variational level set function profile. In theory, it could improve the level set method for efficiency, and reduce the wrong segmentation phenomenon. The experimental results show that the SAR image segmentation method of oil spill has good segmentation qualities and is suitable for the edge complex image segmentation.

scholarly journals An Image Segmentation Method Based on Improved Regularized Level Set Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2393 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
External Energy ◽  
Regularization Term ◽  
Level Set Function ◽  
New Energy ◽  
Segmentation Approach ◽  
Set Function ◽  
Distance Regularization

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images

2019 ◽  
Vol 23 (6) ◽  
pp. 1073-1079
Author(s):  
Zhongming Luo ◽  
Yu Zhang ◽  
Zixuan Zhou ◽  
Xuan Bi ◽  
Haibin Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Clustering Algorithm ◽  
Evolution Equations ◽  
High Efficiency ◽  
Level Set Function ◽  
Level Set Segmentation ◽  
Segmentation Algorithms ◽  
Fcm Clustering ◽  
Set Function

To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation.

scholarly journals Segmentation of Regions of Interest Using Active Contours with SPF Function

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Farhan Akram ◽  
Jeong Heon Kim ◽  
Chan-Gun Lee ◽  
Kwang Nam Choi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
High Intensity ◽  
Active Contours ◽  
Regions Of Interest ◽  
Gaussian Kernel ◽  
Level Set Function ◽  
Edge Based ◽  
Inner Region ◽  
Set Function

Segmentation of regions of interest is a well-known problem in image segmentation. This paper presents a region-based image segmentation technique using active contours with signed pressure force (SPF) function. The proposed algorithm contemporaneously traces high intensity or dense regions in an image by evolving the contour inwards. In medical image modalities these high intensity or dense regions refer to tumor, masses, or dense tissues. The proposed method partitions an image into an arbitrary number of subregions and tracks down salient regions step by step. It is implemented by enforcing a new region-based SPF function in a traditional edge-based level set model. It partitions an image into subregions and then discards outer subregion and partitions inner region into two more subregions; this continues iteratively until a stopping condition is fulfilled. A Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed segmentation algorithm has been applied to different images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.

scholarly journals Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yang Li ◽  
Wei Liang ◽  
Yinlong Zhang ◽  
Jindong Tan
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Lumbar Vertebrae ◽  
Ct Image ◽  
Global Level ◽  
Level Set Function ◽  
Weak Boundary ◽  
Irregular Boundaries ◽  
Set Function ◽  
Level Set Approach

Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.

A VARIATIONAL MULTIPHASE LEVEL SET ALGORITHM FOR IMAGE SEGMENTATION BASED ON ACTIVE CONTOUR MODEL

2013 ◽  
Vol 09 (01) ◽  
pp. 1250004 ◽  
Author(s):  
HAIYING LIU ◽  
YU CHENG ◽  
MAX Q.-H. MENG
Keyword(s):  
Mathematical Model ◽  
Image Segmentation ◽  
Level Set ◽  
Active Contour Model ◽  
Previous Method ◽  
Regularization Term ◽  
Level Set Function ◽  
Set Function ◽  
Distance Regularization ◽  
Double Well Potential

A novel variational multiphase level set mathematical model is derived for image segmentation with two contributions. By virtue of eliminating the time-consuming re-initialization procedure and neglecting the property of the level set function during the evolution process, we in this paper present two strategies that may be taken as our contributions to solving these problems. Two scenarios are considered, namely, first, the distance regularization term which is defined by double-well potential function with two minimum points is introduced to our mathematical model for avoiding the re-initialization process. Second, by combining a Tikhonov-like regularization term which can guarantee the smoothness for the evolution curve over the previous method. Numerical simulation studies are presented to verify our new model via evaluating and comparing with existing algorithms.

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