scholarly journals Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen

2013 ◽  
pp. 443-462 ◽  
Author(s):  
Eleftheria A. Mylona ◽  
Michalis A. Savelonas ◽  
Dimitris Maroulis
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Active Contours ◽  
Medical Image Segmentation

Proposal of Fast Implicit Level Set Scheme for Medical Image Segmentation Using the Chan and Vese Model

2011 ◽  
Vol 103 ◽  
pp. 695-699 ◽  
Author(s):  
Hui Min Lu ◽  
Serikawa Seiichi ◽  
Yu Jie Li ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Social Life ◽  
Medical Image ◽  
High Speed ◽  
Active Contours ◽  
Operator Splitting ◽  
Medical Image Segmentation ◽  
Proposed Model ◽  
The Social

People living in the information age, are more and more attention to their lives. It is also said, social life is more important in present and future. The social life contains three fields. In this paper, we propose a new model for active contours to detect objects in a given medical image, in order to facilitate people to have medical treatment. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, our model uses the level set which are corresponding to ordinary differential equation (ODE). Our model has more improved characteristics than traditional models, such as: less sensibility of noise; unnecessary of re-initialization and high speed by the simplified ordinary differential function. Finally, we validate the proposed model by numerical synthetic and real images. The experimental results demonstrate that our model is at least two times more efficient than the widely used methods.

scholarly journals Self-parameterized active contours based on regional edge structure for medical image segmentation

SpringerPlus ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 424 ◽  
Author(s):  
Eleftheria A Mylona ◽  
Michalis A Savelonas ◽  
Dimitris Maroulis
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Active Contours ◽  
Medical Image Segmentation ◽  
Edge Structure

scholarly journals Interactive Medical Image Segmentation Using PDE Control of Active Contours

2013 ◽  
Vol 32 (11) ◽  
pp. 2127-2139 ◽  
Author(s):  
Peter Karasev ◽  
Ivan Kolesov ◽  
Karl Fritscher ◽  
Patricio Vela ◽  
Phillip Mitchell ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Active Contours ◽  
Medical Image Segmentation ◽  
Pde Control

Homogeneity- and density distance-driven active contours for medical image segmentation

2011 ◽  
Vol 41 (5) ◽  
pp. 292-301 ◽  
Author(s):  
Phan Tran Ho Truc ◽  
Tae-Seong Kim ◽  
Sungyoung Lee ◽  
Young-Koo Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Active Contours ◽  
Medical Image Segmentation

scholarly journals Medical Image Segmentation with Knowledge-guided Robust Active Contours

Radiographics ◽  
2002 ◽  
Vol 22 (2) ◽  
pp. 437-448 ◽  
Author(s):  
Riccardo Boscolo ◽  
Matthew S. Brown ◽  
Michael F. McNitt-Gray
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Active Contours ◽  
Medical Image Segmentation

Optimal Parameter Setting of Active-Contours Using Differential Evolution and Expert-Segmented Sample Image

2012 ◽  
Vol 16 (6) ◽  
pp. 677-686 ◽  
Author(s):  
Arman Darvish ◽  
◽  
Shahryar Rahnamayan
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Active Contours ◽  
Optimal Parameter ◽  
Current Approach ◽  
Medical Image Segmentation ◽  
Control Parameters ◽  
External Energy ◽  
Processing Chain

Generally, tissue extraction (segmentation) is one of the most challenging tasks in medical image processing. Inaccurate segmentation propagates errors to the subsequent steps in the image processing chain. Thus, in any image processing chain, the role of segmentation is in fact critical because it has a significant impact on the accuracy of the final results, such as those of feature extraction. The appearance of variant noise types makes medical image segmentation a more complicated task. Thus far, many approaches for image segmentation have been proposed, including the well-known active contour (snake) model. This method minimizes the energy associated with the target’s contour, which is the sum of the internal and external energy. Although this model has strong characteristics, it suffers from sensitivity to its control parameters. Finding the optimal parameter values is not a trivial task, because the parameters are correlated and problem-dependent. To overcome this problem, this paper proposes a new approach for setting snake’s optimal parameters, which utilizes an expertsegmented gold (ground-truth) image and an optimization algorithm to determine the optimal values for snake’s seven control parameters. The proposed approach was tested on three different medical image test suites: prostate ultrasound (33 images), breast ultrasound (30 images), and lung X-Ray images (48 images). In the current approach, the DE algorithm is employed as a global optimizer. The scheme introduced in this paper is general enough to allow snake to be replaced by any other segmentation algorithm, such as the level set method. For experimental verification, 111 images were utilized. In a comparison with the prepared gold images, the overall error rate is shown to be less than 3%. We explain the proposed approach and the experiments in detail.

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