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.

scholarly journals A novel myocardium segmentation approach based on neutrosophic active contour model

2017 ◽  
Vol 142 ◽  
pp. 109-116 ◽  
Author(s):  
Yanhui Guo ◽  
Guo-Qing Du ◽  
Jing-Yi Xue ◽  
Rong Xia ◽  
Yu-hang Wang
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Contour Model ◽  
Segmentation Approach

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.

Heuristic Initialization for Active Contour Models in CT/MRI Image Processing

2015 ◽  
Vol 15 (03) ◽  
pp. 1550010
Author(s):  
Hao Liu ◽  
Hongbo Qian ◽  
Ning Dai ◽  
Jianning Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Heuristic Method ◽  
Initial Contour ◽  
Active Contour Models ◽  
Contour Model ◽  
Segmentation Approach ◽  
Edge Points ◽  
Contour Models ◽  
Level Set Evolution

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

scholarly journals 3-D Segmentation of Lung Nodules Using Hybrid Levelsets

2019 ◽  
Author(s):  
Hina Shakir
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Lung Nodules ◽  
Geodesic Active Contour ◽  
3D Slicer ◽  
Anisotropic Diffusion Filter ◽  
Contour Model ◽  
Proposed Model

Lung nodule segmentation in CT images and its subsequent volume analysis can help determinethe malignancy status of a lung nodule. While several efficient segmentation schemes have beenproposed, only a few studies evaluated the segmentation’s performance for large nodules. In thisresearch, we contribute a semi-automatic system which is capable of performing robust 3-D segmen-tations on both small and large nodules with good accuracy. The target CT volume is de-noisedwith an anisotropic diffusion filter and a region of interest is selected around the target nodule ona reference slice. The proposed model performs nodule segmentation by incorporating a mean in-tensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptivetechnique using image intensity histogram to estimate the desired mean intensity of the nodule.The proposed system is validated on both lung nodules and phantoms collected from publicly avail-able diverse databases. Quantitative and visual comparative analysis of the proposed work withthe Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented.The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, themean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved resultssuggest that the proposed method can be used for volume estimations of small as well as large-sizednodules.

scholarly journals Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Function ◽  
Contour Model ◽  
Robust Image

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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