An Efficient Bottom-Up Image Segmentation Method Based on Region Growing, Region Competition and the Mumford Shah Functional

2006 ◽  
Author(s):  
Yongsheng Pan ◽  
J. Birdwell ◽  
Seddik Djouadi
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Segmentation Method ◽  
Bottom Up ◽  
Growing Region

scholarly journals Bone Cancer Detection from X-Ray and MRI Images through Image Segmentation Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 273-278
Keyword(s):  
Image Segmentation ◽  
Cancer Detection ◽  
Image Interpretation ◽  
Bone Cancer ◽  
Region Growing ◽  
Segmentation Method ◽  
X Ray ◽  
Science Paper ◽  
Biomedical Field ◽  
Edge Based

This paper is based on integration of the biomedical field and computer science. Paper contains the study of bone cancer and features to predict the type of the same. Related work to find cancer in human body using computer vision is discussed in this paper. Image segmentation technique like sobel, prewitt, canny, K-means and Region Growing are described in this paper which can be stimulated for X-Ray and MRI image interpretation. Paper also shows the result of edge based and region based image segmentation techniques applied on X-Ray image to detect osteosarcoma cancer present on bone using MATLAB. Finally, paper concluded by finding best suited segmentation method for grey scaled image with future aspects.

Fabric Defect Detection Based on SRG-PCNN

2010 ◽  
Vol 148-149 ◽  
pp. 1319-1326 ◽  
Author(s):  
Xiao Shu Si ◽  
Hong Zheng ◽  
Xue Min Hu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Defect Detection ◽  
Focal Point ◽  
Region Growing ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Defect Image ◽  
Validation Tests

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

A SELF-DIRECTED METHOD FOR IMAGE SEGMENTATION USING A MODIFIED TOP-DOWN REGION DIVIDING APPROACH

2014 ◽  
Vol 28 (06) ◽  
pp. 1455008
Author(s):  
WALTER S. WEHNER ◽  
FRANK Y. SHIH
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Standard Deviation ◽  
Spatial Structure ◽  
Region Growing ◽  
Connected Components ◽  
Segmentation Method ◽  
Top Down ◽  
Class Information ◽  
Structure Destruction

We present a self-directed method for image segmentation using a modified top-down region dividing (TDRD) approach. The TDRD-based image segmentation method solves some of the issues with histogram and region growing-based segmentation techniques. The process is efficient and achieves proper results without over segmentation or spatial-structure destruction. In this paper, we examine seven user-defined parameters of the method. These parameters are converted from human inputs to values derived from in-class information created by the algorithm allowing for autonomous image segmentation, without the need of human input or feedback. Our new autonomous implementation also reduces the computational complexity of the algorithm. This reduction will produce significant savings for the total number of computations the algorithm needs to perform image segmentation. Experimental results show that the images using these new derived values yield superior results as compared to other methods, including the original TDRD method. We compare our results visually and numerically based on the within-class standard deviation (WCSD) and the number of connected components (NCC).

METHODS AND ALGORITHMS FOR IMAGE SEGMENTATION

2020 ◽  
Vol 2020 (2) ◽  
pp. 11-16
Author(s):  
Karina Jo ◽  
Olga Gerget
Keyword(s):  
Cluster Analysis ◽  
Image Segmentation ◽  
Brain Tumors ◽  
Medical Images ◽  
Region Growing ◽  
Segmentation Method ◽  
Segmentation Accuracy ◽  
Optimal Segmentation ◽  
The Brain

This study aim to find the optimal segmentation method for detecting brain tumors. For this purpose, the main methods from each group were selected: from stochastic-the method of cluster analysis of k-means, from structural-morphological, from mixed – region growing. The study was based on medical images of the brain, the sample includes 10 images. After segmenting the images, you need to find the best result. The result must be justified. As a result of the research, the method of region growing proved to be an effective method. The accuracy of the method is proved by statistical and variance analyses. The segmentation accuracy of the region growing is 89 %.

scholarly journals Perbandingan Metode Segmentasi K-Means Clustering dan Segmentasi Region Growing untuk Pengukuran Luas Wilayah Hutan Mangrove

2019 ◽  
Vol 7 (1) ◽  
pp. 31-37
Author(s):  
Tyas Panorama Nan Cerah ◽  
Oky Dwi Nurhayati ◽  
R. Rizal Isnanto
Keyword(s):  
Image Segmentation ◽  
Satellite Imagery ◽  
Mangrove Forest ◽  
Region Growing ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Segmentation Method ◽  
Area Density ◽  
Segmentation Methods ◽  
Region Growing Segmentation

This study aims to examine the k-means clustering and region growing segmentation methods to identify and measure the area of mangrove forests in the Southeast Sulawesi province. The image of the area of this study used Landsat 8 satellite imagery. The area of mangrove forest was carried out by calculating the number of pixels identified as mangrove forests with an area density of 900 m2/pixel. The accuracy of the two segmentation methods in calculating the area was compared based on the same area calculated by LAPAN. The overall accuracy of k-means clustering segmentation method has better accuracy, which is 59.26%, than region growing with 33.33% of accuracy. Both image segmentation methods, k-means clustering and region growing, can be used to calculate the area of mangrove forests in the Southeast Sulawesi region using Landsat 8 satellite imagery.

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