Microstructure identification based on vessel pores feature extraction of high-value hardwood species

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

Automation in Construction ◽

10.1016/j.autcon.2017.01.019 ◽

2017 ◽

Vol 78 ◽

pp. 51-61 ◽

Cited By ~ 36

Author(s):

Gang Li ◽

Xiaoxing Zhao ◽

Kai Du ◽

Feng Ru ◽

Yubo Zhang

Keyword(s):

Support Vector Machine ◽

Active Contour ◽

Active Contour Model ◽

Support Vector ◽

Greedy Search ◽

Contour Model

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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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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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