scholarly journals An Image Processing Workflow to Quantify Penetration of Blob-like Structures into an Arbitrary Region of Interest

2016 ◽  
Vol 22 (S3) ◽  
pp. 2068-2069
Author(s):  
Trevor W. Lancon
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Arbitrary Region

scholarly journals Demineralization Depth Using QLF and a Novel Image Processing Software

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Jun Wu ◽  
Zachary R. Donly ◽  
Kevin J. Donly ◽  
Steven Hackmyer
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Polarized Light ◽  
Region Of Interest ◽  
Linear Regression Method ◽  
Percent Loss ◽  
Significant Linear Correlation ◽  
Processing Software ◽  
Image Processing Software ◽  
Imagej Software

Quantitative Light-Induced fluorescence (QLF) has been widely used to detect tooth demineralization indicated by fluorescence loss with respect to surrounding sound enamel. The correlation between fluorescence loss and demineralization depth is not fully understood. The purpose of this project was to study this correlation to estimate demineralization depth. Extracted teeth were collected. Artificial caries-like lesions were created and imaged with QLF. Novel image processing software was developed to measure the largest percent of fluorescence loss in the region of interest. All teeth were then sectioned and imaged by polarized light microscopy. The largest depth of demineralization was measured by NIH ImageJ software. The statistical linear regression method was applied to analyze these data. The linear regression model wasY=0.32X+0.17, whereXwas the percent loss of fluorescence andYwas the depth of demineralization. The correlation coefficient was 0.9696. The two-tailed t-test for coefficient was 7.93, indicating theP-value=.0014. TheFtest for the entire model was 62.86, which shows theP-value=.0013. The results indicated statistically significant linear correlation between the percent loss of fluorescence and depth of the enamel demineralization.

scholarly journals Perbandingan Metode Cropping pada Sebuah Citra untuk Pengambilan Motif Tertentu pada Kain Songket Sumatera Barat

KOMTEKINFO ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 97-107
Author(s):  
Yuhandri
Keyword(s):  
Image Processing ◽  
Data Processing ◽  
Programming Language ◽  
Region Of Interest ◽  
West Sumatra ◽  
Matlab Programming ◽  
Polygon Method

At the time of image processing where we only need a certain part of an image according to the needs called the Region of Interest (ROI), in order to obtain that, the processing is carried out in a cropping process. Cropping is mostly done by researchers, especially those who research in the field of image processing in order to do data processing on an image, the results of cropping process on an image are usually done to make it easier for researchers to focus on something that is needed only. In this study is to compare existing cropping methods to get a motif found in an image of West Sumatra songket fabric. In this study using the method of cropping rectangle, square, circle, ellipse, polygon and tested using the Matlab programming language. The results of comparison of 5 cropping methods for taking certain motifs on the songket image with 5 different songket image samples, shows that the best results are obtained by using the polygon method. Polygon method can reach certain coordinate points in a songket image, so that the results of cropping are better and other motives that are carried along during the cropping process can be reduced.

scholarly journals Experiment-Based Synthetic Structural Analysis Combining Digital Image Processing and a Strong Form Meshfree Method

2020 ◽  
Vol 10 (22) ◽  
pp. 8053 ◽  
Author(s):  
Junwon Park ◽  
Kyeong-Hwan Kim ◽  
Young-Cheol Yoon ◽  
Sang-Ho Lee
Keyword(s):  
Image Processing ◽  
Structural Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Region Of Interest ◽  
Meshfree Method ◽  
Strong Form ◽  
Bending Test ◽  
Target Area ◽  
Tracking Point

This paper presents an experiment-based synthetic structural analysis method that combines digital image processing (DIP) and the particle difference method (PDM), which is a strong form-based meshfree method. The proposed method uses images to determine the displacement of deformed specimens, interpolates the displacement onto nodes of the PDM model without meshes or grids, and calculates the kinematic variables. Furthermore, the pixel extraction method for the target area and the method of setting the region of interest for expediting DIP were used during the synthetic structural analysis. A method for effectively expanding the number of tracking points and an improved method for labeling tracking points are also presented. To verify the performance of the analysis, the experimental and numerical analysis results of a three-point bending test on a rubber beam were compared in terms of various mechanical variables as well as with the PDM results of a simulated bending test. It was found that tracking point expansion and adjusting the radius of the domain of influence are advantageous for performing an accurate calculation without losing computational efficiency. It was demonstrated that the synthetic structural analysis effectively overcomes the shortcomings of the conventional experiments and the limitations of pure simulations.

Research of Image Processing Algorithm Based on Region of Interest

2013 ◽  
Vol 798-799 ◽  
pp. 814-817
Author(s):  
Fang Wang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Attention ◽  
Region Of Interest ◽  
Processing Algorithm ◽  
Human Information Processing ◽  
Visual Attention Model ◽  
Attention Model ◽  
Starting Point ◽  
Image Zooming

With the further development of modern scientific study, it promotes the research of the image based on region of interest. By doing these studies, it satisfies the pressing needs in many fields such as military, production and living areas, etc. meanwhile, it is also the key problem in the fields of computer vision, image processing, artificial intelligence, video communication. Visual attention plays a very important role in the human information processing of the psychological adjustment mechanism. It is a conscious activity which chooses the useful information from large amounts of information. It owns the high efficiency and reliability in the process of human visual perception. Visual attention model, which is based on the visual attention and combined with the computer vision, builds a spatial feature of visual attention architecture. It is helpful not only to find out the visual cognition rule, but also to solve the problem of interested area selection and focus on improving the efficiency of the computer image processing. It has important application value in areas such as image extraction and image zooming. The paper has carried out the deeply study in the interested image region. With the improved visual attention model as a starting point, it combines with graph processing algorithm. And it uses the image extraction algorithm and image zooming algorithm to improve the visual attention model and detect the interested area.

scholarly journals Evaluating the performance of various segmentation techniques in industrial radiographs

2014 ◽  
Vol 14 (1) ◽  
pp. 161-171
Author(s):  
Mythili Thirugnanam ◽  
S. Margret Anouncia
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Non Destructive Testing ◽  
Gray Scale ◽  
Destructive Testing ◽  
Radiographic Images ◽  
Segmentation Algorithms ◽  
Industrial Inspection ◽  
Pros And Cons ◽  
Non Destructive

Abstract At present, image processing concepts are widely used in different fields, such as remote sensing, communication, medical imaging, forensics and industrial inspection. Image segmentation is one of the key processes in image processing key stages. Segmentation is a process of extracting various features of the image which can be merged or split to build the object of interest, on which image analysis and interpretation can be performed. Many researchers have proposed various segmentation algorithms to extract the region of interest from an image in various domains. Each segmentation algorithm has its own pros and cons based on the nature of the image and its quality. Especially, extracting a region of interest from a gray scale image is incredibly complex compared to colour images. This paper attempts to perform a study of various widely used segmentation techniques in gray scale images, mostly in industrial radiographic images that would help the process of defects detection in non-destructive testing.

scholarly journals Optimization of Region of Interest (ROI) Image of Malaria Parasites

2018 ◽  
Vol 3 (2) ◽  
pp. 87-95 ◽  
Author(s):  
Rika Rosnelly ◽  
Linda Wahyuni ◽  
Jani Kusanti
Keyword(s):  
Image Processing ◽  
Malaria Parasite ◽  
Region Of Interest ◽  
Malaria Parasites ◽  
The North ◽  
North Sumatra

The stage of region of interest (ROI) is the determining part to the next stage in image processing. ROI is a process of taking certain parts or regions in an image. ROI can be done by manual and automatic cropping. Some previous studies still use cropping manually for detection of malaria parasites. This study uses cropping automatically for detection of malaria parasites. The types of malaria parasites used were falciparum, vivax and malariae with ring stages, tropozoite, schizon and gametocytes. Data from malaria parasites were obtained at the North Sumatra Provincial Health Laboratory. The results show that the ROI image can crop the malaria parasite region. Keyword - malaria parasite, ROI.

scholarly journals An Enhanced Approach for Automation the Diagnosis of Iron Deficiency Anemia Based on Quantitative Analysis of Red Blood Cells in Intestine Villi Tissue

2018 ◽  
Vol 12 (12) ◽  
pp. 65
Author(s):  
Manar Rizik Al-Sayyed ◽  
Faten Hamad ◽  
Rizik Al-Sayyed ◽  
Hussam N. Fakhouri
Keyword(s):  
Image Processing ◽  
Quantitative Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Region Of Interest ◽  
Noise Removal ◽  
Successful Outcome ◽  
Deficiency Anemia ◽  
Microscopic Images ◽  
The Status

Recent years have witnessed a huge revolution in developing automated diagnosis for different diseases such as cancer using medical image processing. Many researchers have been conducted in this field. Analyzing medical microscopic images provide pathology medical track with large information about the status of the patients and the progress of the diseases and help in detecting any pathological changes in tissues. Automation of the diagnosis of these images will lead to a better, faster and enhanced diagnosis for different hematological and histological images. This paper proposes an automated approach for analyzing blood smear microscopic images to help in diagnosing anemia using quantitative analysis of red blood cells in intestine villi tissue. The diagnoses depends on counting the number of blue and red stained blood cells that contain iron in each villi separately, then, it calculates the percentage of blue cells and red cells in the experimented image. The experimental results have shown that using digital image processing techniques through processing the image into different stages as including noise removal, image sharpening, enhancing contrast, find region of interest, isolating color, removing edges, and counting cells leads to a successful outcome and the diagnose of anemia.

scholarly journals Image processing for early detection of Alzheimer’s disease using Iridology

2020 ◽  
Vol 11 (4) ◽  
pp. 5555-5559
Author(s):  
Asuntha A ◽  
Sai Kalyan Reddy R ◽  
Vamshikrishna K ◽  
Premsagar N
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Region Of Interest ◽  
Colour Pattern ◽  
Training Methods ◽  
Iris Image ◽  
Alternative Research ◽  
The Brain

Alzheimer's disease is caused by genetics, personal lifestyle and other environmental factors. It is an irreversible disease that slowly destroys the brain memory cells. There are no specific methods for the detection of Alzheimer's disease. The primary symptoms of Alzheimer's disease are memory loss, difficulty in thinking, a problem in writing and speaking and others. Iridology is alternative research that has gained more popularity in recent years, which studies the alterations of the iris in correspondence with the organs of the human body. The combination of digital image processing with Iridology gives an excellent opportunity to explore and learn about different neuronal diseases, specifically Alzheimer's disease. In this work, MATLAB software is applied to determine the colour, pattern and other factors that show the existence of Alzheimer's disease. The noise in the iris image is removed by the Gaussian filter, followed by histogram analyses and cropping. The Hough circle transform is used to identify the region of interest and to convert the circular iris image into rectangle form. In the training methods, the SVM and CNN classifiers are used to classify whether the person has Alzheimer's disease. Finally, the results are compared with the real-time images.

scholarly journals An algorithm to measure unsymmetrical circle shape of intravascular ultrasound image using image processing techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 508-515
Author(s):  
Suhaili Beeran Kutty ◽  
Rahmita Wirza O. K. Rahmat ◽  
Sazzli Shahlan Kassim ◽  
Hizmawati Madzin ◽  
Hazlina Hamdan
Keyword(s):  
Image Processing ◽  
Intravascular Ultrasound ◽  
Ultrasound Image ◽  
Region Of Interest ◽  
Processing Technique ◽  
Human Interaction ◽  
Image Processing Technique ◽  
Border Detection ◽  
Cross Sectional ◽  
Minimum Diameter

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape. 

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