Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

I. Cruz-Aceves; J. G. Avina-Cervantes; J. M. Lopez-Hernandez; M. G. Garcia-Hernandez; M. Torres-Cisneros; H. J. Estrada-Garcia; A. Hernandez-Aguirre

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Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

机译：使用主动轮廓和单变量边际分布的自动图像分割

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This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

机译：本文提出了一种基于主动轮廓模型理论和分布估计算法的自动图像分割方法。所提出的方法使用单变量边际分布模型来推断不同活动轮廓上控制点之间的统计依赖性。这些轮廓是通过参考形状先验的对齐过程生成的，目的是提高有关不同交互式分割技术的探索和开发能力。提出的方法应用于光子晶体纤维显微图像中的空心核的分割，并且还用于分别从计算机断层扫描和磁共振图像的数据集中分割人的心脏和心室区域。此外，为了评估与专家概述的区域相比医学图像分割的性能，已采取了一套相似性措施。实验结果表明，提出的图像分割方法在分割精度和稳定性方面均优于传统的主动轮廓模型和交互式Tseng方法。

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《Mathematical Problems in Engineering》 |2013年第14期|419018.1-419018.9|共9页
作者
I. Cruz-Aceves; J. G. Avina-Cervantes; J. M. Lopez-Hernandez; M. G. Garcia-Hernandez; M. Torres-Cisneros; H. J. Estrada-Garcia; A. Hernandez-Aguirre;
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Division de Ingenierias Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico;

Division de Ingenierias Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico;

Division de Ingenierias Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico;

Division de Ingenierias Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico;

Division de Ingenierias Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico;

Division de Ingenierias Campus Irapuato-Salamanca, Universidad de Guanajuato, Carretera Salamanca-Valle de Santiago Km 3.5+1.8 Km Comunidad de Palo Blanco, 36885 Salamanca, GTO, Mexico;

Centro de Investigation en Matemdticas (CIMAT), A.C. Jalisco S/N, Col. Valenciana, 36000 Guanajuato, GTO, Mexico;

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1. Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution [J] . I.Cruz-Aceves, J. G.Avina-Cervantes, J. M.Lopez-Hernandez, Mathematical Problems in Engineering: Theory, Methods and Applications . 2013,第5期

机译：使用主动轮廓的单变量边际分布自动图像分割
2. Toward an automatic tool for oligoclonal band detection in cerebrospinal fluid and tears for multiple sclerosis diagnosis: lane segmentation based on a ribbon univariate open active contour [J] . Medical and Biological Engineering and Computing: Journal of the International Federation for Medical and Biological Engineering . 2020,第5期

机译：朝向脑脊髓液中的寡晶频带检测的自动工具及多发性硬化诊断的泪水：基于带非变动开放活性轮廓的车道分割
3. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model [J] . Hu Yuzhou, Guo Yi, Wang Yuanyuan, Medical Physics . 2019,第1期

机译：使用扩张的完全卷积网络与活动轮廓模型结合使用扩张的全卷积网络自动肿瘤分割
4. Brain Tumor Semi-automatic Segmentation on MRI T1-weighted Images using Active Contour Models [C] . Ahmad Habbie Thias, Abdullah Faqih Al Mubarok, Astri Handayani, International Conference on Mechatronics, Robotics and Systems Engineering . 2019

机译：使用主动轮廓模型对MRI T1加权图像进行脑肿瘤半自动分割
5. An active contour approach for segmentation of intra-retinal layers in Optical Coherence Tomography images. [D] . Yazdanpanah, Azadeh. 2010

机译：一种用于在光学相干断层扫描图像中分割视网膜内层的主动轮廓方法。
6. Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method [O] . Shiju Joseph, Asif Adnan, David Adlam 2016

机译：使用基于透射率的管腔强度增强的血管内光学相干断层扫描图像并基于局部水平集的主动轮廓线方法自动分割冠状动脉形态
7. Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution [O] . I. Cruz-Aceves, J. G. Avina-Cervantes, J. M. Lopez-Hernandez, 2013

机译：使用具有单变量边缘分布的活动轮廓自动图像分割

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

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