Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine

Because of the diversity of vessel pores in different hardwood species, they are important for wood species identification. In this paper, a Micro CT was used to collect wood images. The experiment was based on six wood types, Pterocarpus macrocarpus, Pterocarpus erinaceus, Dalbergia latifolia, Dalbergia frutescens var. tomentosa, Pterocarpus indicus, and Pterocarpus soyauxii. One-thousand cross-sectional images of 2042 px × 1640 px were collected for each species. One pixel represents 1.95 µm of the real physical dimension. The level set geometric active contour model was used to obtain the contour of the vessel pores. Combined with a variety of morphological processing methods, the binary images of the vessel pores were obtained. The features of the binary images were extracted for classification. Classifiers such as BP neural network and support vector machine were used, the number, roundness, area, perimeter, and other characteristic parameters of the vessel pores were classified, and the accuracy rate was more than 98.9%. The distribution and arrangement of the vessel pores of six kinds of hardwood were obtained through the level set geometric active contour model and image morphology. Then BP neural network and support vector machine were used for realizing the classification of hardwood species.

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Identifikasi Citra berdasarkan Gigitan Ular menggunakan Metode Active Contour Model dan Support Vector Machine

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v3i4.1409 ◽

2019 ◽

Vol 3 (4) ◽

pp. 299

Author(s):

Dhiya Ulhaq Dewangga ◽

Adiwijaya Adiwijaya ◽

Dody Qori Utama

Keyword(s):

Support Vector Machine ◽

Active Contour ◽

Active Contour Model ◽

World Health ◽

Support Vector ◽

Identification System ◽

Venomous Snake ◽

Tropical Countries ◽

Contour Model ◽

Snake Bites

Tropical countries have a warm and humid climate are suitable habitat for the lives of reptile animals, especially snakes. Snakes are a type of reptile animal that is widely found in tropical countries, especially in Indonesia. The worst thing that happens when meeting a snake is the bite of snake. If the bite comes from a venomous snake it can cause a more serious problem than the bite from non-venomous snake is, which can cause paralysis, disability, and the worst is death. According to the WHO (World Health Organization) an estimated 5.4 million people are bitten by snakes each year with almost 2.7 million being bitten by venomous snakes and get affected symptoms. Around 81,000 to 138,000 people die every year. This research uses image processing technic to make the identification system of snake bites whether venomous or non-venomous. The method used in this system is Active Contour Model and Support Vector Machine. By using these methods, the highest accuracy is obtained in the best of SVM kernel, on RBF kernel and Polynomial kernel.

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Detection of Pulmonary Nodules in CT Images Based on Fuzzy Integrated Active Contour Model and Hybrid Parametric Mixture Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/515386 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 8

Author(s):

Bin Li ◽

Kan Chen ◽

Lianfang Tian ◽

Yao Yeboah ◽

Shanxing Ou

Keyword(s):

Mixture Model ◽

Active Contour ◽

Active Contour Model ◽

Pulmonary Nodules ◽

Ground Glass Opacity ◽

Ct Images ◽

Support Vector ◽

Cad System ◽

Knowledge Based ◽

Contour Model

The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and “weak” local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.

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Automatic Screening of Lung Diseases by 3D Active Contour Method for Inhomogeneous Motion Estimation in CT Image Pairs

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.10573 ◽

2021 ◽

Vol 18 (12) ◽

Author(s):

Pikul VEJJANUGRAHA ◽

Kazunori KOTANI ◽

Waree KONGPRAWECHNON ◽

Toshiaki KONDO ◽

Kanokvate TUNGPIMOLRUT

Keyword(s):

Neural Network ◽

Active Contour ◽

Lung Diseases ◽

Velocity Vector ◽

Active Contour Model ◽

Contour Method ◽

Support Vector ◽

Surface Model ◽

Parametric Surface ◽

Contour Model

Lung diseases are now the third leading cause of death worldwide because of the many risk factors we are exposed to daily, such as air pollution, tobacco use, viruses (such as COVID-19), and bacteria. This work introduces a new approach of the 3D Active Contour Model (3D ACM) to estimate an inhomogeneous motion of lungs, which can be used to analyze lung disease patterns using a hierarchical predictive model. The biophysical model of lungs consists of End Expiratory (EE) and End Inspiratory (EI) models, generated by high-resolution computed tomography images (HRCT). A proposed technique uses the 3D ACM to estimate the velocity vector by using the corresponding points on the parametric surface model of the EE model to the EI model. The external energy from the EI models is the external force that pushes the 3D parametric surface to reach the boundary. The external forces, such as the balloon force and Gradient Vector Flow (GVF), were adjusted adaptively based on the which was calculated from the ratio of the maximum value of EI to EE on the Z axis. Next, the feature representation is studied and evaluated based on the lung structure, separated into five lobes. The stepwise regression, Support Vector Machine (SVM), and Artificial Neural Network (ANN) techniques are applied to classify the lung diseases into normal, obstructive lung, and restrictive lung diseases. In conclusion, the inhomogeneous motion pattern of lungs integrated with medical-based knowledge can be used to analyze lung diseases by differentiating normal and abnormal motion patterns and separating restrictive and obstructive lung diseases. HIGHLIGHTS Inhomogeneous motion analysis from the expanding and shrinking lungs of HRCT pair Adaptive 3D Active Contour Model (ACM) for detecting the shape of the lung by balancing the balloon force with the stopping condition Lung lopes separation using oblique fissure and anatomical location Structure the velocity vector map of lung motion using bag of words of the magnitude Neural Network model for predicting obstructive and restrictive lung diseases GRAPHICAL ABSTRACT

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A New Computer-Aided Detection System for Pulmonary Nodule in CT Scan Images of Cancerous Patients

Journal of Shahid Sadoughi University of Medical Sciences ◽

10.18502/ssu.v28i4.3770 ◽

2020 ◽

Author(s):

Seyed Soheil Mazarzadeh ◽

Hassan Masoumi ◽

Ali Rafiee

Keyword(s):

Active Contour ◽

Active Contour Model ◽

Detection System ◽

Pulmonary Nodules ◽

Repeated Measurements ◽

Support Vector ◽

Computer Aided Detection ◽

False Positive Error ◽

Contour Model ◽

Computer Aided

Introduction: In the lung cancers, a computer-aided detection system that is capable of detecting very small glands in high volume of CT images is very useful.This study provided a novelsystem for detection of pulmonary nodules in CT image. Methods: In a case-control study, CT scans of the chest of 20 patients referred to Yazd Social Security Hospital were examined. In the two-dimensional and three-dimensional feature analysis algorithm, which were suspicious areas of pulmonary nodules and automatic diagnosis for evaluation, and the area segmentation results by active contour model, were compared with the results of the donation by the physician. Finally, to categorize the areas into two groups of cancerous and non-cancerous helping the MATLAB software Ver. 2014 b using Support Vector Machine (SVM) with three linear kernels, cubic polynomial and a kernel of the radial base function and repeated measurements test were analyzed at level of P≤0.05. Results: The mean error for 10 cancer patients and 10 healthy individuals was 0.023 and 0.453, respectively and the best results were obtained using the RBF (Radial Basis Function) kernel algorithm and the σ = 0.28 parameter for it. Using the local area-based active contour model, the zoning time was reduced from 18.66 to 5 seconds on average and the calculated distances were calculated to be less than or equal to 0.75 mm; which indicates an increase in the speed of identification of high-precision pulmonary nodules. Conclusion: In the proposed algorithm, the amount of false positive error and the time of identifying the nodules were significantly reduced and all areas suspected of being cancerous were identified with high accuracy and speed.

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

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114811 ◽

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

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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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