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).

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.

scholarly journals Guided lazy snapping for long thin object selection

2018 ◽  
Vol 189 ◽  
pp. 10026
Author(s):  
Nam H. Tran ◽  
Dongsun Seo ◽  
Henry T.H. Tu
Keyword(s):  
Image Segmentation ◽  
Connected Components ◽  
Segmentation Method ◽  
User Interactions ◽  
Object Selection ◽  
Segmentation Methods ◽  
Background Image ◽  
Lazy Snapping

We show a novel way to select long thin objects in an image by enhancing the output of the existing foreground/background image segmentation methods. Most superpixel-based methods fail to select the long thin details, such as legs and whiskers, and extended curves from the main objects. We observe, however, the output without long thin details, can be used as the guided information to obtain the connected components. Based on this observation, our Guided Lazy Snapping method overcomes the limitation of the Lazy Snapping methods (or other alternatives superpixel-based segmentation method) to select long thin objects. The results show that connected components in the image can be selected without having a lot of user interactions (mouse clicks) on each extended parts of the object.

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 %.

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