Segmentation of biomedical images using active contour model with robust image feature and shape prior

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.511-512.923 ◽

2014 ◽

Vol 511-512 ◽

pp. 923-926 ◽

Cited By ~ 1

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.

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Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Neurocomputing ◽

10.1016/j.neucom.2019.04.052 ◽

2019 ◽

Vol 357 ◽

pp. 215-230 ◽

Cited By ~ 2

Author(s):

Yanmin Niu ◽

Lan Qin ◽

Xuchu Wang

Keyword(s):

Active Contour ◽

Active Contour Model ◽

Cross Entropy ◽

Shape Prior ◽

Contour Model

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A Novel Image Segmentation Algorithm Based on Active Contour Model and Retinex Model

Proceedings of the 2019 3rd International Conference on Advances in Image Processing ◽

10.1145/3373419.3373451 ◽

2019 ◽

Author(s):

Jin Liu ◽

Jianqiao Wang ◽

Qi Li ◽

Miaohua Shi

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Active Contour Model ◽

Segmentation Algorithm ◽

Contour Model ◽

Image Segmentation Algorithm

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

Journal of X-Ray Science and Technology ◽

10.3233/xst-210879 ◽

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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Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽

10.3390/electronics10020192 ◽

2021 ◽

Vol 10 (2) ◽

pp. 192

Author(s):

Umer Sadiq Khan ◽

Xingjun Zhang ◽

Yuanqi Su

Keyword(s):

Object Detection ◽

Fourier Transformation ◽

Active Contour ◽

Active Contour Model ◽

Salient Object Detection ◽

Fast Fourier Transformation ◽

Salient Object ◽

Active Contour Models ◽

Contour Model ◽

Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

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