3-D Medical Image Region-Growing based Segmentation Techniques, Challenges and Open Issues

2014 ◽  
Author(s):  
Omobayo A. Esan ◽  
Tranos Zuva ◽  
Seleman M. Ngwira ◽  
Moses Olaifa
Keyword(s):  
Medical Image ◽  
Region Growing ◽  
Image Region ◽  
Open Issues

Adaptive image region-growing

1994 ◽  
Vol 3 (6) ◽  
pp. 868-872 ◽  
Author(s):  
Yian-Leng Chang ◽  
Xiaobo Li
Keyword(s):  
Region Growing ◽  
Image Region

scholarly journals CAD System for Lung Cancer and its Stages Detection using Image Processing Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1612-1615
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
False Positive ◽  
False Negative ◽  
Region Growing ◽  
High Rate ◽  
Support Vector ◽  
Image Region ◽  
Image Processing Techniques ◽  
Processing Techniques

The Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

2013 ◽  
Vol 760-762 ◽  
pp. 1552-1555 ◽  
Author(s):  
Jing Jing Wang ◽  
Xiao Wei Song ◽  
Mei Fang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Region Growing ◽  
Challenging Problem ◽  
Correct Judgment ◽  
Threshold Segmentation ◽  
Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

scholarly journals Coronary Arteries Segmentation Based on the 3D Discrete Wavelet Transform and 3D Neutrosophic Transform

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Shuo-Tsung Chen ◽  
Tzung-Dau Wang ◽  
Wen-Jeng Lee ◽  
Tsai-Wei Huang ◽  
Pei-Kai Hung ◽  
...  
Keyword(s):  
Coronary Arteries ◽  
Medical Image ◽  
Region Growing ◽  
Ground Truth ◽  
Discrete Wavelet ◽  
Segmentation Method ◽  
3D Dwt ◽  
Initial Segmentation ◽  
3D Discrete Wavelet Transform ◽  
3D Region Growing

Purpose. Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies.Methods. The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries.Results. Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed.Conclusion. Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

scholarly journals Anatomically Based Geometric Modelling Using Medical Image Data: Methods and Programs

2015 ◽  
Vol 9 (1) ◽  
pp. 126-131
Author(s):  
Monan Wang ◽  
Lei Sun ◽  
Yuming Liu
Keyword(s):  
Geometric Modeling ◽  
Medical Image ◽  
Structural Information ◽  
Geometric Model ◽  
Three Dimensional ◽  
Image Data ◽  
Region Growing ◽  
Reconstruction Algorithm ◽  
Medical Image Segmentation ◽  
Image Browsing

Geometric modeling software that can realize two-dimensional medical image browsing, preprocessing, and three-dimensional (3D) reconstruction is designed for modeling human organs. This software performs medical image segmentation using a method that combines the region growing and the interactive segmentation methods. The Marching Cubes surface reconstruction algorithm is used to obtain a 3D geometric model. The program is compiled using Visual Studio 2010. The software is employed to obtain the geometric model of the human femur, hipbone, and muscle. The geometric modeling results can accurately express the structural information of the skeleton and muscle.

Research and Improvement of Live-Wire Interactive Algorithm for Medical Image Segmentation

2012 ◽  
Vol 182-183 ◽  
pp. 1065-1068 ◽  
Author(s):  
Ji Ying Li ◽  
Jian Wu Dang
Keyword(s):  
Image Segmentation ◽  
Anisotropic Diffusion ◽  
Medical Image ◽  
Optimal Path ◽  
Region Growing ◽  
Medical Image Segmentation ◽  
Interactive Segmentation ◽  
Diffusion Filtering ◽  
Interactive Algorithm ◽  
Region Growing Segmentation

Traditional Live Wire algorithm distinguished the strength edge of objectives uneasily and executive speed of algorithm is slow. For these problems, an improved Live-Wire algorithm is proposed. First it implements anisotropic diffusion filtering to images and constructs a new expense function, then combined with region growing segmentation algorithm, it implements interactive segmentation to medical images. Improved algorithm avoids the shortcomings of the traditional Live-wire algorithm which is sensitive to noise and can not effectively distinguish the edge of the strength, also reduces the time and blindness of dynamic programming to find the optimal path and improves the accuracy and implementation efficiency of the image segmentation.

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