Pulmonary Nodule Segmentation Method of CT Images Based on 3D-FCN

2018 ◽  
pp. 134-141
Author(s):  
Yan Nie ◽  
Deyun Zhuo ◽  
Guanghui Song ◽  
Shiting Wen
Keyword(s):  
Pulmonary Nodule ◽  
Ct Images ◽  
Segmentation Method

scholarly journals Intelligent Algorithms-Based CT Image Segmentation in Patients with Cardiovascular Diseases and Realization of Visualization Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianhua Huang
Keyword(s):  
Cardiovascular Diseases ◽  
Blood Vessel ◽  
Ct Images ◽  
Ct Image ◽  
Segmentation Method ◽  
Intelligent Algorithms ◽  
Curved Planar Reformation ◽  
Cylinder Model ◽  
Visualization Algorithms ◽  
Coronary Artery Tree

The study focused on the intelligent algorithms-based segmentation of computed tomography (CT) images of patients with cardiovascular diseases (CVD) and the realization of visualization algorithms. The first step was to design a method for precise segmentation under the cylinder model based on the coronary body data of the coarse segmentation, and then the principles of different visualization algorithms were discussed. The results showed that the precise segmentation method can effectively eliminate most of the branches and calcified lesions; curved planar reformation (CPR) and straightened CPR can display the entire blood vessel on one image; and spherical CPR can display the complete coronary artery tree on an image, so that a problem with a certain blood vessel can be quickly found. In conclusion, the precise segmentation of CT images of CVD and visualization algorithm based on the cylinder model have clinical significance in the diagnosis of CVD.

scholarly journals An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing

2020 ◽  
Vol 10 (1) ◽  
pp. 233-242 ◽  
Author(s):  
He Ren ◽  
Lingxiao Zhou ◽  
Gang Liu ◽  
Xueqing Peng ◽  
Weiya Shi ◽  
...  
Keyword(s):  
Pulmonary Nodule ◽  
Region Growing ◽  
Segmentation Method

Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images

2004 ◽  
Author(s):  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Hironobu Ohmatsu ◽  
Masahiro Kusumoto ◽  
Ryutaro Kakinuma ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Pulmonary Nodule ◽  
Ct Images ◽  
Image Guided ◽  
Thoracic Ct ◽  
Nodule Classification

Pulmonary Nodule Detection using Chest Ct Images

2003 ◽  
Vol 44 (3) ◽  
pp. 252-257 ◽  
Author(s):  
D.-Y. Kim ◽  
J.-H. Kim ◽  
S.-M. Noh ◽  
J.-W. Park
Keyword(s):  
Pulmonary Nodule ◽  
Ct Images ◽  
Chest Ct ◽  
Nodule Detection ◽  
Pulmonary Nodule Detection

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 Study on threshold segmentation of multi-resolution 3D human brain CT image

2018 ◽  
Vol 11 (06) ◽  
pp. 1850037
Author(s):  
Ling-ling Cui ◽  
Hui Zhang
Keyword(s):  
Human Brain ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Pathological Diagnosis ◽  
Ct Image ◽  
Segmentation Method ◽  
Signal To Noise ◽  
Threshold Segmentation ◽  
Edge Pixel ◽  
Brain Ct

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

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