Metamorphic Testing of Image Region Growth Programs in Image Processing Applications

2018 ◽  
Author(s):  
Chao Jiang ◽  
Song Huang ◽  
Zhan-wei Hui
Keyword(s):  
Image Processing ◽  
Image Region ◽  
Metamorphic Testing ◽  
Region Growth

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

scholarly journals Detection And Restoration of Cracked Digitized Paintings and Manuscripts Using Image Processing

2018 ◽  
Vol 7 (2.34) ◽  
pp. 39
Author(s):  
Nawafil Abdulwahab Farajalla Ali ◽  
Imad Fakhri Taha Al-Shaikhli ◽  
Raini Hasan
Keyword(s):  
Image Processing ◽  
Median Filter ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
Drying Up ◽  
Weighted Median ◽  
Mechanical Factors ◽  
Region Growth

Ancient paintings are cultural heritage that can be preserved via computer aided analysis and processing. These paintings deteriorate due to undesired cracks, which are caused by aging, drying up of painting material, and mechanical factors. These heritages need to be restored to their respective original or near-original states. There are different techniques and methodologies that can be used to conserve and restore the overall quality of these images. The main objective of this study is to analyze techniques and methodologies that have been developed for the detection, classification of small patterns, and restoration of cracks in digitized old painting and manuscripts. The purpose of the developed algorithm is to identify cracks using the thresholding operation, which was the output of the top-hat transform morphology. Afterwards, the breaks, which were wrongly identified as cracks, were separated for utilization in a semi-automatic procedure based on region growth. Finally, both the median filter and weighted median techniques were applied to fill the cracks and enhance image quality. 

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.

Metamorphic Relations Based Test Oracles for Image Processing Applications

Biometrics ◽  
2017 ◽  
pp. 892-906
Author(s):  
Tahir Jameel ◽  
Mengxiang Lin ◽  
Liu Chao
Keyword(s):  
Image Processing ◽  
Relative Effectiveness ◽  
Automatic Test ◽  
Effective Technique ◽  
General Technique ◽  
Metamorphic Testing ◽  
Domain Experts ◽  
Test Oracle ◽  
Implementation Under Test

Evaluation of output images carrying visual semantics is a challenging task which is carried out by domain experts through visual inspection. Automatic test oracle is required to augment the test oracle problem and to eliminate the manual efforts. Metamorphic testing is an effective technique to alleviate these problems. In this paper, the authors have demonstrated that how inherent properties of implementation under test can be used to generate an automatic test oracle for image processing applications. Metamorphic testing is a general technique in which follow-up test cases are generated using a transformation function and the anticipated output is evaluated. They have used some general metamorphic relations and also designed some algorithm specific metamorphic relations for morphological image operations. Selection of metamorphic relations is the most important step and the authors have analyzed relative effectiveness of different metamorphic relations using mutation analysis. The results show metamorphic testing is a very effective technique to automate output images evaluation and to alleviate oracle problem.

Color Image Region Growth Segmentation Integration of Normalized Cut

2010 ◽  
Vol 143-144 ◽  
pp. 139-142
Author(s):  
Xiao Ying Wu ◽  
Li Juan Ma ◽  
Zhao Feng Li ◽  
Shi Tao Yan
Keyword(s):  
Color Image ◽  
Image Features ◽  
Human Vision ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Human Visual Perception ◽  
Normalized Cut ◽  
Image Region ◽  
Region Growth ◽  
Selection Of

This paper solves that image segmentation result is not consistent with human visual perception or too broken. First of all, based on the continuity of image features, appropriate human vision, calculated the similarity of color image pixel as Eq.2 in HSV space to grow region, then made the regional merge, using normalized-cut segmentation method as Eq.4 and Eq.5 to eliminate over-segmentation phenomenon. In this paper, experimental results shows that the segmentation can be achieved very good results as Fig.1, and parts of the method can be applied in other segmentation to solve over segmentation. This method on color images as the research object is different from other methods on gray images, the selection of seeds and achieves these automatic that differ from general algorithms, presents a new implementation to solve over-segmentation.

Medical Image Region Extraction and 3D Modeling Based on Approximating Curves

2011 ◽  
Vol 222 ◽  
pp. 285-288 ◽  
Author(s):  
Katrina Bolochko ◽  
Aleksandrs Sisojevs ◽  
Aleksandrs Glazs ◽  
Ardis Platkajis
Keyword(s):  
Image Processing ◽  
3D Modeling ◽  
Medical Image ◽  
3D Visualization ◽  
Medical Image Processing ◽  
Region Of Interest ◽  
Medical Diagnostics ◽  
Image Region ◽  
Different Types ◽  
Region Extraction

This work describes several methods that intend to solve such medical image processing tasks as extraction and 3D visualization of the region of interest (ROI). The proposed methods were tested on the medical images of a brain acquired by computer tomography and proven to be applicable to different types of ROI, resulting in a possible visualization of several ROI at once, i.e. pathology and the head of a patient. The results can be used to provide practical improvements to the reliability of medical diagnostics.

Metamorphic Relations Based Test Oracles for Image Processing Applications

2016 ◽  
Vol 4 (1) ◽  
pp. 16-30 ◽  
Author(s):  
Tahir Jameel ◽  
Mengxiang Lin ◽  
Liu Chao
Keyword(s):  
Image Processing ◽  
Relative Effectiveness ◽  
Automatic Test ◽  
Effective Technique ◽  
General Technique ◽  
Metamorphic Testing ◽  
Domain Experts ◽  
Test Oracle ◽  
Implementation Under Test

Evaluation of output images carrying visual semantics is a challenging task which is carried out by domain experts through visual inspection. Automatic test oracle is required to augment the test oracle problem and to eliminate the manual efforts. Metamorphic testing is an effective technique to alleviate these problems. In this paper, the authors have demonstrated that how inherent properties of implementation under test can be used to generate an automatic test oracle for image processing applications. Metamorphic testing is a general technique in which follow-up test cases are generated using a transformation function and the anticipated output is evaluated. They have used some general metamorphic relations and also designed some algorithm specific metamorphic relations for morphological image operations. Selection of metamorphic relations is the most important step and the authors have analyzed relative effectiveness of different metamorphic relations using mutation analysis. The results show metamorphic testing is a very effective technique to automate output images evaluation and to alleviate oracle problem.

scholarly journals ADAPTIVE ALGORITHM BASED ON NONLOCAL MEANS IN IMAGE PROCESSING

2018 ◽  
pp. 79-86
Author(s):  
T. V. Zhertunova ◽  
E. S. Yanakova
Keyword(s):  
Image Processing ◽  
Standard Method ◽  
Adaptive Algorithm ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Basic Structure ◽  
Problem Situation ◽  
Image Region ◽  
Nonlocal Means

This article describes the existing problem situation associated with the absence of resource-lights denoising algorithms, capable to produce good-quality output images in the different intensity noise conditions without blurring the boundaries, contours and basic structure. The adaptive algorithm proposed in the article allows to solve this problem due to the developed algorithms of splitting the search region into two sets of similar and points different from the pixel and adapting of the kernel type to the image region, depending on the presence or detection of structural and smooth pixels. The results of the proposed algorithm and the standard method of nonlocal means are compared with the metrics of the peak signal-to-noise ratio and structural similarity. It is found out that the developed adaptive algorithm is surpass by far than the standard method both on numerical results and on the quality of the image processing.

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.

Sign in / Sign up

Export Citation Format

Share Document