scholarly journals COASTLINE DETECTION USING FUSION OF OVER SEGMENTATION AND DISTANCE REGULARIZATION LEVEL SET EVOLUTION

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 513-518 ◽  
Author(s):  
S. Toure ◽  
O. Diop ◽  
K. Kpalma ◽  
A. S. Maiga
Keyword(s):  
Level Set ◽  
High Tide ◽  
Texture Features ◽  
Ground Truth ◽  
Optical Remote Sensing ◽  
Medium Resolution ◽  
High Resolution Images ◽  
Dry Sand ◽  
Level Set Evolution ◽  
Distance Regularization

<p><strong>Abstract.</strong> Coastline detection is a very challenging task in optical remote sensing. However the majority of commonly used methods have been developed for low to medium resolution without specification of the key indicator that is used. In this paper, we propose a new approach for very high resolution images using a specific indicator. First, a pre-processing step is carried out to convert images into the optimal colour space (HSV). Then, wavelet decomposition is used to extract different colour and texture features. These colour and texture features are then used for Fusion of Over Segmentation (FOOS) based clustering to have the distinctive natural classes of the littoral. Among these classes are waves, dry sand, wet sand, sea and land. We choose the mean level of high tide water, the interface between dry sand and wet sand, as a coastline indicator. To find this limit, we use a Distance Regularization Level Set Evolution (DRLSE), which automatically evolves towards the desired sea-land border. The result obtained is then compared with a ground truth. Experimental results prove that the proposed method is an efficient coastline detection process in terms of quantitative and visual performances.</p>

Enhanced distance regularization for re-initialization free level set evolution with application to image segmentation

2014 ◽  
Vol 141 ◽  
pp. 223-235 ◽  
Author(s):  
Xuchu Wang ◽  
Jinxiao Shan ◽  
Yanmin Niu ◽  
Liwen Tan ◽  
Shao-Xiang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Free Level ◽  
Level Set Evolution ◽  
Distance Regularization

scholarly journals Modified distance regularized level set evolution for brain ventricles segmentation

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Thirumagal Jayaraman ◽  
Sravan Reddy M. ◽  
Manjunatha Mahadevappa ◽  
Anup Sadhu ◽  
Pranab Kumar Dutta
Keyword(s):  
Level Set ◽  
Morphological Changes ◽  
Similarity Index ◽  
Neuronal Loss ◽  
Optimization Procedure ◽  
Ground Truth ◽  
Structural Similarity ◽  
Computed Tomography Images ◽  
Level Set Evolution ◽  
The Brain

AbstractNeurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

scholarly journals A Minimal Cost Path and Level Set Evolution Approach for Carotid Bifurcation Segmentation

2009 ◽  
Author(s):  
Karl Krissian ◽  
Sara Arencibia
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Carotid Bifurcation ◽  
Minimal Cost ◽  
Thin Tube ◽  
Lumen Segmentation ◽  
Cost Paths ◽  
Level Set Evolution ◽  
The Cost

We propose a new approach for semi-automatic segmentation of the carotid bifurcation as part of the Carotid Lumen Segmentation and Stenosis Grading Challenge MICCAI’2009 workshop. Three initial points are provided as input, belonging to the Common, the External and the Internal Carotid Arteries. Our algorithm is divided into two main steps: first, two minimal cost paths are tracked between the CCA and both the ECA and the ICA. The cost functions are based on a multiscale vesselness response. Second, after detecting the junction position and cutting or extending the paths based on the requested lengths, a level set segmentation is initialized as a thin tube around the computed paths and evolves until reaching the vessel wall or a maximal evolution time. Results on training and testing datasets are presented and compared to the manual segmentation by three observers, based on a ground truth and using four quality measures.

Adaptive Distance Preserving Level Set Evolution for Image Segmentation

2009 ◽  
Vol 19 (12) ◽  
pp. 3161-3169 ◽  
Author(s):  
Chuan-Jiang HE ◽  
Meng LI ◽  
Yi ZHAN
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Evolution

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.

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