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

Research and Implementation of a Fast Image Segmentation Algorithm Based on SRG

2014 ◽  
Vol 621 ◽  
pp. 594-598
Author(s):  
Chun Yin Hu ◽  
Wan Cheng Tang ◽  
Bang Yan Ye ◽  
Li Dong Liang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Virus Infection ◽  
Region Growing ◽  
The Other ◽  
Image Region ◽  
Image Scanning ◽  
Time Performance ◽  
Seeded Region Growing ◽  
Image Segmentation Algorithm

In order to improve the real-time performance and accuracy of the traditional SRG(Seeded Region Growing) algorithm in image processing, this paper proposes a intellective and rapid image segmentation by imitating the process of the virus infection in nature, and then implement it on vc++6 platform. On one hand , the algorithm can detecting automatically detect the seeds in image region and can be adapt for uneven-light image by adjusting the parameters based on the brightness of the background; On the other hand, only by one of the image scanning, it can segment and mark the objects from the background. The experimental results show that compared with the traditional SRG algorithm, this algorithm can improve the segmentation speed in different background with higher accuracy.

scholarly journals Classification of Liver Tumor on Ct Images using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11336-11338
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Region Of Interest ◽  
Region Growing ◽  
Ct Images ◽  
Svm Classifier ◽  
Intensity Level ◽  
Image Region ◽  
Occurrence Matrix

Liver tumor is one of the most severe types of cancerous diseases which is responsible for the death of many patients. CT Liver tumor images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from CT images because of several anatomical changes in different patients. The tumor is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from CT image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of CT liver tumor image is studied. This will be useful for physician in better automatic diagnosis of liver tumor from CT images.

scholarly journals Determination of the FIFO-stack size for the algorithm of the image region growing

2019 ◽  
pp. 59-63
Author(s):  
V. Yu. Tsviatkou
Keyword(s):  
Region Growing ◽  
Segmentation Algorithm ◽  
Shift Register ◽  
Image Size ◽  
Image Region ◽  
Maximum Loading

The aim of the work is to derive an expression that allows determining the size of the FIFO-stack for storing the coordinates of adjacent pixels depending on the image size for the segmentation algorithm based on region growing. The FIFO stack, organized on the principle of a ring multi-bit shift register, is considered. The conditions for maximum loading of the FIFO stack are formulated, for which an expression is obtained that allows one to accurately determine the required size of the FIFO stack, which provides memory savings.

Fast image region growing

1995 ◽  
Vol 13 (7) ◽  
pp. 559-571 ◽  
Author(s):  
Yian-Leng Chang ◽  
Xiaobo Li
Keyword(s):  
Region Growing ◽  
Image Region

scholarly journals REGION HOMOGENEITY IN THE LOGARITHMIC IMAGE PROCESSING FRAMEWORK: APPLICATION TO REGION GROWING ALGORITHMS

2019 ◽  
Vol 38 (1) ◽  
pp. 43
Author(s):  
Guillaume Noyel ◽  
Michel Jourlin
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
State Of The Art ◽  
Region Growing ◽  
Segmentation Method ◽  
Image Region ◽  
Logarithmic Image Processing ◽  
Image Component ◽  
Processing Framework ◽  
Component Tree

In order to create an image segmentation method robust to lighting changes, two novel homogeneity criteria of an image region were studied. Both were defined using the Logarithmic Image Processing (LIP) framework whose laws model lighting changes. The first criterion estimates the LIP-additive homogeneity and is based on the LIP-additive law. It is theoretically insensitive to lighting changes caused by variations of the camera exposure-time or source intensity. The second, the LIP-multiplicative homogeneity criterion, is based on the LIP-multiplicative law and is insensitive to changes due to variations of the object thickness or opacity. Each criterion is then applied in Revol and Jourlin’s (1997) region growing method which is based on the homogeneity of an image region. The region growing method becomes therefore robust to the lighting changes specific to each criterion. Experiments on simulated and on real images presenting lighting variations prove the robustness of the criteria to those variations. Compared to a state-of the art method based on the image component-tree, ours is more robust. These results open the way to numerous applications where the lighting is uncontrolled or partially controlled.

Dense stereo matching by region growing based on disparity space sampling

2009 ◽  
Vol 29 (10) ◽  
pp. 2690-2692
Author(s):  
Bao-hai YANG ◽  
Xiao-li LIU ◽  
Dai-feng ZHA
Keyword(s):  
Stereo Matching ◽  
Region Growing ◽  
Dense Stereo Matching ◽  
Disparity Space
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