scholarly journals Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification

2008 ◽  
Author(s):  
Dirk Smeets ◽  
Bert Stijnen ◽  
Dirk Loeckx ◽  
Bart De Dobbelaer ◽  
Paul Suetens
Keyword(s):  
Liver Metastases ◽  
Level Set ◽  
Liver Tumors ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Specific Method ◽  
Pixel Classification ◽  
Spiral Scanning ◽  
Level Set Function ◽  
Scanning Technique

In this paper a specific method is presented to facilitate the semi-automatic segmentation of liver metastases in CT images. Accurate and reliable segmentation of tumors is e.g. essential for the follow-up of cancer treatment. The core of the algorithm is a level set function. The initialization is provided by a spiral-scanning technique based on dynamic programming. The level set evolves according to a speed image that is the result of a statistical pixel classification algorithm with supervised learning. This method is tested on CT images of the abdomen and compared with manual delineations of liver tumors.

Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification

2010 ◽  
Vol 14 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Dirk Smeets ◽  
Dirk Loeckx ◽  
Bert Stijnen ◽  
Bart De Dobbelaer ◽  
Dirk Vandermeulen ◽  
...  
Keyword(s):  
Level Set ◽  
Liver Tumors ◽  
Pixel Classification ◽  
Spiral Scanning ◽  
Level Set Segmentation ◽  
Scanning Technique ◽  
Automatic Level

Detecting subsurface interfaces with a physics-based level-set segmentation and additional geological constraints

2020 ◽  
Author(s):  
Florian Wellmann ◽  
Benjamin Berkels
Keyword(s):  
Level Set ◽  
Gradient Flow ◽  
Field Measurements ◽  
Specific Method ◽  
State Variables ◽  
Image Domain ◽  
Practical Applications ◽  
Level Set Function ◽  
Geological Information ◽  
Geological Constraints

<p>Sharp interfaces often separate regions in the subsurface with distinctively different properties due to processes in geological evolution – and these   interfaces are relevant for a variety of scientific investigations, as well as practical applications.  The delineation of these layers with different properties is commonly attempted on the basis of geological and geophysical data, for example as picks in  prevalent seismic reflectors, interpreted from potential field measurements, and derived from observations in drillholes. </p><p>We evaluate here a specific method to determine the position and shape of such an interface using measurements of state variables related to a physical flow field described with an elliptic PDE. A typical example is the measurement of temperatures related to heat flow through zones with distinctively different thermal conductivities. We use a level-set function to describe the interface and determine the optimal interface shape for a 2-D case. This type of shape inversion has been successfully attempted before, and we extend on this previous work by including additional shape constraints on orientation, interface, and observations of specific segmentation outcomes. These constrains are motivated by geological information that may be available, for example as derived and interpreted from additional geophysical measurements. </p><p>We model this as an image segmentation problem, where we are looking for a segmentation of the image domain whose induced temperature minimizes the squared <em>L<sup>2</sup></em> distance to temperature measurements on a lower dimensional set. From an optimal control perspective, the segmentation is the control and the temperature the state.  Numerically, the segmentation is represented by a level set and the minimization is done using a gradient flow, where the derivative with respect to the level set is computed using dualization. Moreover, we include additional geologically motivated constraints by adding soft penalties to the objective function.</p><p>We test our method with several conceptual examples to determine the feasibility and limitations, especially with regard to different interface shapes and the amount of available information and additional geological constraints, as well as the influence of noise on the detection  accuracy. Results show that these additional constraints help determining an interface. However, measurement noise and a non-homogeneous spatial distribution of physical properties reduces the accuracy of the derived interface. </p>

A Genetic Algorithm-Based Level Set Curve Evolution for Prostate Segmentation on Pelvic CT and MRI Images

2010 ◽  
pp. 127-149 ◽  
Author(s):  
Payel Ghosh ◽  
Melanie Mitchell ◽  
James A. Tanyi ◽  
Arthur Hung
Keyword(s):  
Genetic Algorithm ◽  
Level Set ◽  
Energy Function ◽  
Automatic Segmentation ◽  
Prostate Gland ◽  
Descent Method ◽  
Curve Evolution ◽  
Gradient Descent Method ◽  
Level Set Function ◽  
Set Function

A novel genetic algorithm (GA) is presented here that performs level set curve evolution using texture and shape information to automatically segment the prostate on pelvic images in computed tomography and magnetic resonance imaging modalities. Here, the segmenting contour is represented as a level set function. The contours in a typical level set evolution are deformed by minimizing an energy function using the gradient descent method. In these methods, the computational complexity of computing derivatives increases as the number of terms (needed for curve evolution) in the energy function increase. In contrast, a genetic algorithm optimizes the level-set function without the need to compute derivatives, thereby making the introduction of new curve evolution terms straightforward. The GA developed here uses the texture of the prostate gland and its shape derived from manual segmentations to perform curve evolution. Using these high-level features makes automatic segmentation possible.

scholarly journals Semi-automatic Segmentation of 3D Liver Tumors from CT Scans Using Voxel Classification and Propagational Learning

2008 ◽  
Author(s):  
Jiayin Zhou ◽  
Wei Xiong ◽  
Qi Tian ◽  
Yingyi Qi ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Liver Tumors ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Morphological Operations ◽  
Tumor Region ◽  
Overall Performance ◽  
Intermediate Part ◽  
Voxel Classification

A semi-automatic scheme was developed for the segmentation of 3D liver tumors from computed tomography (CT) images. First a support vector machine (SVM) classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumor-containing slices were processed. The method was tested using 3D CT images with 10 liver tumors and a set of quantitative measures were computed, resulted in an averaged overall performance score of 72.

scholarly journals Retroperitoneal space organ segmentation from ct images based on the level set function

2015 ◽  
Vol 39 (4) ◽  
pp. 592-599 ◽  
Author(s):  
R.V. Eruslanov ◽  
◽  
M.N. Orehova ◽  
V.N. Dubrovin ◽  
◽  
...  
Keyword(s):  
Level Set ◽  
Ct Images ◽  
Retroperitoneal Space ◽  
Level Set Function ◽  
Set Function

Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

2017 ◽  
Vol 83 ◽  
pp. 58-66 ◽  
Author(s):  
Changjian Sun ◽  
Shuxu Guo ◽  
Huimao Zhang ◽  
Jing Li ◽  
Meimei Chen ◽  
...  
Keyword(s):  
Liver Tumors ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct
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