A SEGMENTATION FRAMEWORK FOR PHASE CONTRAST AND FLUORESCENCE MICROSCOPY IMAGES

2014 ◽  
Vol 28 (07) ◽  
pp. 1460013 ◽  
Author(s):  
MITCHEL ALIOSCHA-PEREZ ◽  
RONNIE WILLAERT ◽  
HICHEM SAHLI
Keyword(s):  
Active Contour ◽  
Phase Contrast ◽  
Active Contour Model ◽  
Region Of Interest ◽  
Energy Functional ◽  
Living Cells ◽  
Shape Fitting ◽  
Contour Model ◽  
Microscopy Images

The noninvasive imaging of unstained living cells is a widely used technique in biotechnology for determining biological and biochemical role of proteins, since it allows studying living specimens without altering them. Usually, fluorescence and contrast (or transmission) images are both used complementarily, as their combination allows possible better outcomes. However, segmentation of contrast images is particularly difficult due to the presence of defocused scans, lighting/shade-off artifacts and cells overlapping. In this work, we investigate the optical properties intervening during the image formation process, and propose different segmentation strategies that can benefit from these properties. The proposed scheme (i) combines the estimated phase and the fluorescence information in order to obtain initial markers for a latter segmentation stage; and (ii) use the shear oriented polar snakes, an active contour model that implicitly involves phase information on its energy functional. The obtained contour can be used as region of interest estimation, as data for a latter shape-fitting process, or as smart markers for a more detailed segmentation process (i.e. watershed). Experimental results provide a comparison of the different segmentation schemes, and confirm the suitability of the proposed strategy and model for cell images segmentation.

IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS

2012 ◽  
Vol 12 (03) ◽  
pp. 1250019 ◽  
Author(s):  
LIHE ZHANG ◽  
ZHENZHEN LIU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Level Set Methods ◽  
Energy Functional ◽  
Image Statistics ◽  
Contour Model ◽  
Contour Models ◽  
Homogeneous Regions ◽  
The Given ◽  
Edge Indicator Function

In this paper, we propose a novel cosegmentation algorithm based on active contour model which utilizes local and global image statistics. Many localized region-based active contour models have been proposed to solve a challenging problem of the property (such as intensity, color, texture, etc.) inhomogeneities that often occurs in real images, but these models usually cannot reasonably evolve the curve in this situation that some center points along the curve are in homogeneous regions and their local regions are far away from the object. In order to overcome the difficulties we selectively enlarge the driven force of some points and introduce the edge indicator function to avoid the curve over-shrinking or over-expanding on the salient boundaries. In addition, we introduce global image statistics to better the curve evolution and try to avoid the given energy functional converging to a local minimum. Practical experiments show that our algorithm can obtain better segmentation results.

scholarly journals Fused Second-Order MR Image Feature Framework for Region of Interest Delineation

2021 ◽  
Author(s):  
Shoaib Amin Banday ◽  
Samiya Khan ◽  
A. H. Mir
Keyword(s):  
Active Contour ◽  
Feature Fusion ◽  
Active Contour Model ◽  
Region Of Interest ◽  
Second Order ◽  
Image Feature ◽  
Feature Maps ◽  
Contour Model ◽  
Segmentation Approach ◽  
Critical Phases

Abstract Healthcare infrastructure relies on technology-driven solutions such as CAD systems for improving the overall efficiency of its procedures and processes. Image segmentation is one of the most critical phases for such systems in view of the fact that accuracy of this phase determines the efficacy of the later phases, to a large extent. Extensive research is underway to develop segmentation techniques that can achieve highest accuracy with some suggestions directed towards an information fusion based approach within the machine learning paradigm. This research paper proposes a fused second-order statistical image feature framework for Region of Interest delineation. It is a feature fusion-based segmentation approach (ACM-FT) that fuses texture driven feature maps from GLCM , GLRLM and Gabor filters. The proposed approach is then compared with Active Contour Model with classical edge detection method (ACM-ED) and Active Contour Model without edges (ACM-WE) using Overlap Index (OI) and Jackard’s Similarity Co-efficient (JSI). The proposed approach achieves an average accuracy of 92.17% and 93.19% for JSI and OI, respectively, demonstrating significant improvements.

A Local Statistical Information Active Contour Model for Image Segmentation

2014 ◽  
Vol 6 (2) ◽  
pp. 33-49 ◽  
Author(s):  
Shigang Liu ◽  
Yali Peng ◽  
Guoyong Qiu ◽  
Xuanwen Hao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Statistical Information ◽  
Intensity Inhomogeneity ◽  
Image Domain ◽  
Contour Model ◽  
Proposed Model ◽  
Spatially Varying ◽  
Level Set Formulation

This paper presents a local statistical information (LSI) active contour model. Assuming that the distribution of intensity belonging to each region is a Gaussian distribution with spatially varying statistical information, and defining an energy function, the authors integrate the entire image domain. Then, this energy is incorporated into a variational level set formulation. Finally, by minimizing the energy functional, a curve evolution equation can be obtained. Because the image local information is considered, the proposed model can effectively deal with the image with intensity inhomogeneity. Experimental results on synthetic and real images demonstrate that the proposed model can effectively segment the image with intensity inhomogeneity.

EFFICIENT ACTIVE CONTOUR MODEL FOR MULTIPHASE SEGMENTATION WITH APPLICATION TO BRAIN MR IMAGES

2013 ◽  
Vol 27 (01) ◽  
pp. 1355001 ◽  
Author(s):  
YUNYUN YANG ◽  
YI ZHAO ◽  
BOYING WU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Energy Model ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Contour Model ◽  
New Energy ◽  
Brain Mr Images

In this paper, we propose an efficient active contour model for multiphase image segmentation in a variational level set formulation. By incorporating the globally convex segmentation idea and the split Bregman method into the multiphase formulation of the local and global intensity fitting energy model, our new model improved the original local and global intensity fitting energy model in the following aspects. First, we propose a new energy functional using the globally convex segmentation method to guarantee fast convergence. Second, we incorporate information from the edge into the energy functional by using a non-negative edge detector function to detect boundaries more easily. Third, instead of a constant value to control the influence of the local and global intensity fitting terms, we use a weight function varying with the locations of the image to balance the weights between the local and the global fitting terms dynamically. Lastly, the special structure of our energy functional enables us to apply the split Bregman method to minimize the energy much more efficiently. We have applied our model to synthetic images and real brain MR images with promising results. Experimental results demonstrate the efficiency and superiority of our model.

Research on Automatic Identification of Substation Circuit Breaker Based on Shape Priors

2014 ◽  
Vol 511-512 ◽  
pp. 923-926 ◽  
Author(s):  
Ting Wang ◽  
Chen Yao ◽  
Jun Liu ◽  
Long Zhang Chao ◽  
Gang Hu Shao ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Circuit Breaker ◽  
Functional Model ◽  
Energy Functional ◽  
Automatic Identification ◽  
Shape Priors ◽  
Shape Prior ◽  
Shape Information ◽  
Contour Model

High-pressure circuit breakers are very important and it undertakes the disconnection and connection control of high voltage transmission lines. It is one of the equipment of substation daily inspection. State of breakers are judged by open and close characters label, so a shape-prior active contour model to realize state automatic recognition of breaker images collected by inspection robot is presented in this paper. Shape-prior active contour model combines the shape information with CV model to build energy functional model, then set up initial position curve by a priori knowledge and drives the curve evolution in minimize energy functional process, the curve position is the character label contour when energy functional shows minimum. We do experiment for the algorithm on different images, demonstrate that the algorithm based on known character contour, have good segmentation results of circuit breaker in the image character recognition accuracy and applicability when the circuit breaker character is actually partial occlusion, local deformation, scale changes.

scholarly journals Local- and Global-Statistics-Based Active Contour Model for Image Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Boying Wu ◽  
Yunyun Yang
Keyword(s):  
Image Segmentation ◽  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Split Bregman ◽  
Distribution Fitting ◽  
Total Variation Norm ◽  
Contour Model ◽  
Global Statistics

This paper presents a local- and global-statistics-based active contour model for image segmentation by applying the globally convex segmentation method. We first propose a convex energy functional with a local-Gaussian-distribution-fitting term with spatially varying means and variances and an auxiliary global-intensity-fitting term. A weight function that varies dynamically with the location of the image is applied to adjust the weight of the global-intensity-fitting term dynamically. The weighted total variation norm is incorporated into the energy functional to detect boundaries easily. The split Bregman method is then applied to minimize the proposed energy functional more efficiently. Our model has been applied to synthetic and real images with promising results. With the local-Gaussian-distribution-fitting term, our model can also handle some texture images. Comparisons with other models show the advantages of our model.

scholarly journals Segmentation of Intensity-Corrupted Medical Images Using Adaptive Weight-Based Hybrid Active Contours

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Asif Aziz Memon ◽  
Shafiullah Soomro ◽  
Muhammad Tanseef Shahid ◽  
Asad Munir ◽  
Asim Niaz ◽  
...  
Keyword(s):  
Weight Function ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Intensity Level ◽  
Contour Model ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Intensity Functions

Segmentation accuracy is an important criterion for evaluating the performance of segmentation techniques used to extract objects of interest from images, such as the active contour model. However, segmentation accuracy can be affected by image artifacts such as intensity inhomogeneity, which makes it difficult to extract objects with inhomogeneous intensities. To address this issue, this paper proposes a hybrid region-based active contour model for the segmentation of inhomogeneous images. The proposed hybrid energy functional combines local and global intensity functions; an incorporated weight function is parameterized based on local image contrast. The inclusion of this weight function smoothens the contours at different intensity level boundaries, thereby yielding improved segmentation. The weight function suppresses false contour evolution and also regularizes object boundaries. Compared with other state-of-the-art methods, the proposed approach achieves superior results over synthetic and real images. Based on a quantitative analysis over the mini-MIAS and PH2 databases, the superiority of the proposed model in terms of segmentation accuracy, as compared with the ground truths, was confirmed. Furthermore, when using the proposed model, the processing time for image segmentation is lower than those when using other methods.

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