A Segmentation Method of Coronary Angiograms Based on Multi-scale Filtering and Region-Growing

An Automated Lung Nodule Segmentation Method Based On Nodule Detection Network and Region Growing

Proceedings of the ACM Multimedia Asia on ZZZ ◽

10.1145/3338533.3366604 ◽

2019 ◽

Author(s):

Yanhao Tan ◽

Ke Lu ◽

Jian Xue

Keyword(s):

Lung Nodule ◽

Region Growing ◽

Segmentation Method ◽

Nodule Detection

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An unsupervised multi-scale segmentation method based on automated parameterization

Arabian Journal of Geosciences ◽

10.1007/s12517-016-2683-4 ◽

2016 ◽

Vol 9 (15) ◽

Cited By ~ 4

Author(s):

Chao Wang ◽

Wei Xu ◽

Xiao-fang Pei ◽

Xiao-yan Zhou

Keyword(s):

Segmentation Method ◽

Multi Scale

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Study of Segmentation Threshold Based on Wavelet Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3855 ◽

2013 ◽

Vol 756-759 ◽

pp. 3855-3859

Author(s):

Jian Yi Li ◽

Hui Juan Wang

Keyword(s):

Wavelet Transform ◽

Digital Image ◽

Region Growing ◽

Detection Methods ◽

Mathematical Tool ◽

Matlab Simulation ◽

Segmentation Method ◽

Transform Method ◽

Threshold Segmentation ◽

Digital Image Enhancement

Based on the research of the four kinds of algorithms of digital image segmentation, based on edge detection methods, based on region growing method, threshold segmentation method and digital image threshold segmentation method based on wavelet transform, using MATLAB simulation of all digital image enhancement and segmentation process, the obtained results are analyzed, proving the threshold segmentation wavelet transform method has unparalleled advantages in information extraction in medical image. Wavelet transform is a mathematical tool widely used in recent years, compared with the Fu Liye transform, the window of Fu Liye transform, wavelet transform is the local transform of space and frequency, it can be very effective in extracting information from the signal [[1.

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Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽

10.3390/s18103347 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3347 ◽

Cited By ~ 14

Author(s):

Zhishuang Yang ◽

Bo Tan ◽

Huikun Pei ◽

Wanshou Jiang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Laser Scanning ◽

Laser Scanner ◽

Region Growing ◽

Point Clouds ◽

Classification Method ◽

Multi Scale ◽

Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

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