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

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

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

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 5329-5340
Author(s):  
Xiaoxia Yang ◽  
Ziyu Zhao ◽  
Zhongmin Wang ◽  
Zhedong Ge ◽  
Yucheng Zhou
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bp Neural Network ◽  
Active Contour ◽  
Active Contour Model ◽  
Support Vector ◽  
Binary Images ◽  
Contour Model ◽  
Hardwood Species ◽  
Geometric Active Contour

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.

Extraction of Dermo-Epidermal Surface from 3D Volumetric Images of Human Skin

2003 ◽  
Vol 03 (04) ◽  
pp. 589-608 ◽  
Author(s):  
Jiahui Wang ◽  
Hideo Saito ◽  
Shinji Ozawa ◽  
Tomohiro Kuwahara ◽  
Toyonobu Yamashita ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Image Force ◽  
Three Dimensions ◽  
Surface Model ◽  
Initial Surface ◽  
Constraint Force ◽  
Microscopic Images ◽  
Contour Model ◽  
Epidermal Surface

Analysis of the dermo-epidermal surface in three-dimensions has great value in evaluating cosmetics. One approach is based on the active contour model, which is used extensively in computer vision and image processing applications, particularly for local object boundaries with closed curve form. The dermo-epidermal surface, however, is a plane with open form. We have developed a method of automatically extracting the dermo-epidermal surface from volumetric confocal microscopic images, as well as constructing a 3D visual model of the surface by using the geometric information contained in the control points. Our method is a 3D extension of the active contour model, so we call it the active open surface model (AOSM). The initial surface for AOSM is an open curve plane, guided by a 3D internal force, a 3D external constraint force, and a 3D image force, which pull it towards the objective surface. The proposed tecnique has been applied to extract actual dermo-epidermal surface in the given volumetric confocal microscopic images.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
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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