Full-Automatic Optic Disc Boundary Extraction Based on Active Contour Model with Multiple Energies

2018 ◽  
Vol E101.A (3) ◽  
pp. 658-661 ◽  
Author(s):  
Yuan GAO ◽  
Chengdong WU ◽  
Xiaosheng YU ◽  
Wei ZHOU ◽  
Jiahui WU
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Boundary Extraction ◽  
Contour Model

scholarly journals Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1459 ◽  
Author(s):  
Ying Sun ◽  
Xinchang Zhang ◽  
Xiaoyang Zhao ◽  
Qinchuan Xin
Keyword(s):  
High Resolution ◽  
Active Contour ◽  
Active Contour Model ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Contour Model ◽  
Optical Images ◽  
Building Footprint ◽  
Salt And Pepper

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.

scholarly journals A Probabilistic Approach for Breast Boundary Extraction in Mammograms

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Hamed Habibi Aghdam ◽  
Domenec Puig ◽  
Agusti Solanas
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Probability Model ◽  
Probabilistic Approach ◽  
Local Binary Patterns ◽  
Extraction Process ◽  
Morphological Operations ◽  
Boundary Extraction ◽  
Contour Model ◽  
Starting Point

The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images

2016 ◽  
Vol 36 (6) ◽  
pp. 795-809 ◽  
Author(s):  
Maitreya Maity ◽  
Dev Kumar Das ◽  
Dhiraj Manohar Dhane ◽  
Chandan Chakraborty ◽  
Anirudhha Maiti
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Fundus Images ◽  
Contour Model ◽  
Color Fundus Images

An empirical study on optic disc segmentation using an active contour model

2015 ◽  
Vol 18 ◽  
pp. 19-29 ◽  
Author(s):  
M. Caroline Viola Stella Mary ◽  
Elijah Blessing Rajsingh ◽  
J. Kishore Kumar Jacob ◽  
D. Anandhi ◽  
Umberto Amato ◽  
...  
Keyword(s):  
Empirical Study ◽  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Model

scholarly journals Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Wei Zhou ◽  
Yugen Yi ◽  
Yuan Gao ◽  
Jiangyan Dai
Keyword(s):  
Optic Disc ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Glaucoma Diagnosis ◽  
Contour Model ◽  
Novel Structure ◽  
Optic Cup ◽  
The One

Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.

Sign in / Sign up

Export Citation Format

Share Document