scholarly journals Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 263
Author(s):  
Xin Chen ◽  
Hong Zhao ◽  
Ping Zhou
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Region Growing ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Normal Vector ◽  
Lung Lobe ◽  
Surface Classification ◽  
Lobe Segmentation ◽  
Lung Lobes

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

Fully automatic lung lobe segmentation using V/Q SPECT/CT images

2020 ◽  
Author(s):  
A Király ◽  
S Urbán ◽  
Z Besenyi ◽  
L Pávics ◽  
N Zsótér ◽  
...  
Keyword(s):  
Ct Images ◽  
Lung Lobe ◽  
Fully Automatic ◽  
Lobe Segmentation

scholarly journals Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenghao Shi ◽  
Jiejue Ma ◽  
Minghua Zhao ◽  
Yonghong Liu ◽  
Yaning Feng ◽  
...  
Keyword(s):  
Region Growing ◽  
Disease Diagnosis ◽  
Correction Method ◽  
Ct Images ◽  
Segmentation Method ◽  
Lung Segmentation ◽  
Multiple Strategies ◽  
Simple Strategy ◽  
Growing Strategy ◽  
Ct Slices

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

scholarly journals PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3703
Author(s):  
Dongyang Cheng ◽  
Dangjun Zhao ◽  
Junchao Zhang ◽  
Caisheng Wei ◽  
Di Tian
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Noise Removal ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Optimization Strategy ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Run Speed

Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

Atlas-driven lung lobe segmentation in volumetric x-ray CT images

2003 ◽  
Author(s):  
Li Zhang ◽  
Eric A. Hoffman ◽  
Joseph M. Reinhardt
Keyword(s):  
Ct Images ◽  
X Ray ◽  
Lung Lobe ◽  
Lobe Segmentation

scholarly journals Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images

2009 ◽  
Vol 28 (2) ◽  
pp. 202-214 ◽  
Author(s):  
S. Ukil ◽  
J.M. Reinhardt
Keyword(s):  
Ct Images ◽  
X Ray ◽  
Lung Lobe ◽  
Lobe Segmentation

scholarly journals A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yang Yang ◽  
Ming Li ◽  
Xie Ma
Keyword(s):  
Point Cloud ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Principal Component ◽  
Cluster Center ◽  
Normal Vector ◽  
Fuzzy C Means ◽  
Feature Information ◽  
Point Cloud Simplification

To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

High-resolution computed tomography evaluation of the bronchial lumen to vertebral body diameter and pulmonary artery to vertebral body diameter ratios in anesthetized ventilated normal cats

2016 ◽  
Vol 19 (10) ◽  
pp. 1007-1012 ◽  
Author(s):  
Tekla M Lee-Fowler ◽  
Robert C Cole ◽  
A Ray Dillon ◽  
D Michael Tillson ◽  
Rachel Garbarino ◽  
...  
Keyword(s):  
Young Adult ◽  
High Resolution ◽  
Pulmonary Artery ◽  
Vertebral Body ◽  
Ct Images ◽  
Lung Lobe ◽  
Body Diameter ◽  
Bronchial Lumen ◽  
Pulmonary Artery Diameter ◽  
Lung Lobes

Objectives Bronchial lumen to pulmonary artery diameter (BA) ratio has been utilized to investigate pulmonary pathology on high-resolution CT images. Diseases affecting both the bronchi and pulmonary arteries render the BA ratio less useful. The purpose of the study was to establish bronchial lumen diameter to vertebral body diameter (BV) and pulmonary artery diameter to vertebral body diameter (AV) ratios in normal cats. Methods Using high-resolution CT images, 16 sets of measurements (sixth thoracic vertebral body [mid-body], each lobar bronchi and companion pulmonary artery diameter) were acquired from young adult female cats and 41 sets from pubertal female cats. Results Young adult and pubertal cat BV ratios were not statistically different from each other in any lung lobe. Significant differences between individual lung lobe BV ratios were noted on combined age group analysis. Caudal lung lobe AV ratios were significantly different between young adult and pubertal cats. All other lung lobe AV ratios were not significantly different. Caudal lung lobe AV ratios were significantly different from all other lung lobes but not from each other in both the young adult and pubertal cats. Conclusions and relevance BV ratio reference intervals determined for individual lung lobes could be applied to both young adult and pubertal cats. Separate AV ratios for individual lung lobes would be required for young adult and pubertal cats. These ratios should allow more accurate evaluation of cats with concurrent bronchial and pulmonary arterial disease.

scholarly journals Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yuanyuan Peng ◽  
Hualan Zhong ◽  
Zheng Xu ◽  
Hongbin Tu ◽  
Xiong Li ◽  
...  
Keyword(s):  
Left Lung ◽  
Region Of Interest ◽  
Ground Truth ◽  
Ct Images ◽  
Lung Lobe ◽  
Lung Anatomy ◽  
Lobe Segmentation ◽  
The Right ◽  
Anatomy Knowledge ◽  
High Median

In computed tomography (CT) images, pulmonary lobe segmentation is an arduous task due to its complex structures. To remedy the problem, we introduce a new framework based on lung anatomy knowledge for lung lobe segmentation. Firstly, the priori knowledge of lung anatomy is used to identify the fissure region of interest. Then, an oriented derivative of stick filter is applied to isolate plate-like structures from clutters for lobar fissure verification. Finally, a surface fitting model is employed to complete the incomplete fissure surface for lung lobe segmentation. Compared with manually segmented fissure references, the designed approach obtained a high median F1-score of 0.8865 in the left lung and obtained a high median F1-score of 0.9200 in the right lung. The average percentages of the segmented lung lobes in the lung lobe ground truth are 0.960, 0.989, 0.973, 0.920, and 0.985 for the left upper, left lower, right upper, right middle, and right lower lobes, respectively. The perfect performance of the proposed scheme is tested by visual inspection and quantitative evaluation.

scholarly journals LOCALIZATION OF PATHOLOGY ON COMPLEX ARCHITECTURE BUILDING SURFACES

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 617-621 ◽  
Author(s):  
A. A. Sidiropoulos ◽  
K. N. Lakakis ◽  
V. K. Mouza
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Normal Vector ◽  
Northern Greece ◽  
Complex Building ◽  
Building Surfaces ◽  
Definition Of ◽  
Complex Architecture

The technology of 3D laser scanning is considered as one of the most common methods for heritage documentation. The point clouds that are being produced provide information of high detail, both geometric and thematic. There are various studies that examine techniques of the best exploitation of this information. In this study, an algorithm of pathology localization, such as cracks and fissures, on complex building surfaces is being tested. The algorithm makes use of the points’ position in the point cloud and tries to distinguish them in two groups-patterns; pathology and non-pathology. The extraction of the geometric information that is being used for recognizing the pattern of the points is being accomplished via Principal Component Analysis (PCA) in user-specified neighborhoods in the whole point cloud. The implementation of PCA leads to the definition of the normal vector at each point of the cloud. Two tests that operate separately examine both local and global geometric criteria among the points and conclude which of them should be categorized as pathology. The proposed algorithm was tested on parts of the Gazi Evrenos Baths masonry, which are located at the city of Giannitsa at Northern Greece.

Sign in / Sign up

Export Citation Format

Share Document