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.

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 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>

scholarly journals Automatic segmentation in image stacks based on multi-constraint level-set evolution

2015 ◽  
Vol 26 (s1) ◽  
pp. S1501-S1514
Author(s):  
Ning Dai ◽  
Hao Liu ◽  
Yuehong Tang ◽  
Jianning Zhao ◽  
Xiaosheng Cheng
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Level Set Evolution ◽  
Constraint Level

scholarly journals Automatic Segmentation of Dental Cone Beam Computed Tomography Image Based on Level Set Method Using Morphology Operators and Polynomial Fitting

2020 ◽  
Vol 4 (1) ◽  
pp. 45-52
Author(s):  
Fahmi Syuhada Syuhada ◽  
Agus Zainal Arifin
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Cone Beam Computed Tomography ◽  
Automatic Segmentation ◽  
Region Of Interest ◽  
Ground Truth ◽  
Cone Beam ◽  
Polynomial Fitting ◽  
Analysis Process ◽  
Computed Tomography Image

Abstract Automatic Segmentation of dental cone beam computed tomography (CBCT) images is challenging due to the intensity of the teeth that have low level intensity. In this paper we proposes a new method for automatic teeth segmentation in slices of CBCT images based on level let method using morphology operators and polynomial fitting. Morphology operators are used to construct the Region of Interest (ROI) area of dental objects in the image slice. ROI is used to focus the analysis process on areas of dental objects which generally have a polynomial pattern distribution. Polynomial fitting is obtained to estimation arc of teeth structure in CBCT images. Level Set is implemented to evolve the ROI to obtain the contours of dental objects. Comparison between proposed method result and the ground truth images shows that the method gives best average accuracy, sensitivity, and specificity value of 99.02%, 95.32%, 99.09%, respectively. This value that the proposed method is promising for accurate segmentation of the entire tooth form on CBCT images.

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

Carpal Bone Segmentation Using Fully Convolutional Neural Network

2019 ◽  
Vol 15 (10) ◽  
pp. 983-989
Author(s):  
Liang Kim Meng ◽  
Azira Khalil ◽  
Muhamad Hanif Ahmad Nizar ◽  
Maryam Kamarun Nisham ◽  
Belinda Pingguan-Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Bone Age ◽  
Image Feature ◽  
Qualitative Assessment ◽  
Carpal Bone ◽  
Radiographic Images ◽  
Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

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