Computer Aided Image Processing to Facilitate Determination of Congruence in Manual Classification of Mitochondrial Morphologies in Toxoplasma gondii Tissue Cysts

2021 ◽  
Author(s):  
Brooke C Place ◽  
Cortni Troublefield ◽  
Robert D. Murphy ◽  
Anthony P. Sinai ◽  
Abhijit Patwardhan
Keyword(s):  
Image Processing ◽  
Toxoplasma Gondii ◽  
Computer Aided ◽  
Manual Classification

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. 

scholarly journals Status, issues, and future of computer-aided part orientation for additive manufacturing

2021 ◽  
Author(s):  
Yuchu Qin ◽  
Qunfen Qi ◽  
Peizhi Shi ◽  
Paul J. Scott ◽  
Xiangqian Jiang
Keyword(s):  
Additive Manufacturing ◽  
Objective Function ◽  
Future Research ◽  
Research Directions ◽  
Computer Aided ◽  
Future Research Directions ◽  
The Common ◽  
Part Orientation

AbstractPart orientation is a critical task in the process of additive manufacturing product realisation. Recently, various computer-aided methods for this task have been presented in the literature. The coexistence of different methods generates a series of questions: What are the common characteristics of these methods? What are the specific characteristics of each method? What are the main issues in computer-aided part orientation for additive manufacturing currently? What are the potential research directions in this field in the future? To approach these questions, a review of the existing computer-aided part orientation methods for additive manufacturing is presented in this paper. This review starts with a clarification of a part orientation problem and a classification of the existing methods into two categories according to their process of solving the problem. An overview of the representative methods in each category is then carried out from the aspects of approaches for orientation search, generation, or selection, estimation of build orientation factors, determination of weights of factors, establishment of overall objective function, and demonstration of effectiveness. After that, a discussion about the main issues in computer-aided part orientation for additive manufacturing is documented based on the overview. Finally, a suggestion of some future research directions in this field is reported.

scholarly journals Automatic detection of petiole border in plant leaves

2020 ◽  
pp. 002029402091770
Author(s):  
Abdullah Elen ◽  
Emre Avuçlu
Keyword(s):  
Image Processing ◽  
Plant Species ◽  
Experimental Studies ◽  
Boundary Line ◽  
Plant Leaves ◽  
The Czech Republic ◽  
Binary Format ◽  
Trees And Shrubs

Plants are our source of oxygen and nutrients on earth. Therefore, conservation of biodiversity is vital for the survival of other species. With the developing technology, plant species can be examined more closely. Image processing, which is a subject of computer science, has an important role in this field. In this study, an image processing–based method has been developed to automatically separate the petiole region of the plant leaves. To determine the boundary line of the petiole region, the cumulative pixel distributions of the input images in binary format according to the X- and Y-axis are analyzed. Accordingly, optimum thresholds and petiole boundary points are determined. The proposed method was tested on 795 leaf images from 90 different plant species that grow both as trees and shrubs in the Czech Republic. According to the results obtained in experimental studies, it is thought that the proposed method will make an important contribution especially in studies such as automatic classification of plants and leaves and determination of plant species in botanical science.

scholarly journals BI-RADS Breast Tumor Classification Through Image Mining

2019 ◽  
Author(s):  
Valesca J. S. Da Silva ◽  
Mateus M. R. Da Silva ◽  
Marcelino P. S. Silva ◽  
Joana R. C. Nogueira
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Tumor ◽  
Diagnostic System ◽  
Tumor Classification ◽  
Image Mining ◽  
Computer Aided ◽  
Breast Tumor Classification

In this article, a computer aided diagnostic system for BI-RADS classification of breast cancer is proposed. The approach involves image processing capabilities to extract features from tumors in mammography and image mining to classify them as BI-RADS 2, BI-RADS 3, BI-RADS 4C or BI-RADS 5. Images from the BCDR repository were used for the experiments. The results showed the efficacy of the proposed method, which classified tumors with considerable accuracy in four BI-RADS categories.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

The quantitative comparison of experimental and simulated diffraction contrast images

1995 ◽  
Vol 53 ◽  
pp. 102-103
Author(s):  
Stuart McKernan
Keyword(s):  
Image Processing ◽  
Personal Computer ◽  
Close Agreement ◽  
Quantitative Comparison ◽  
Image Simulation ◽  
Computing Power ◽  
Diffraction Contrast ◽  
Simulated Images ◽  
Made In

For many years the concept of quantitative diffraction contrast experiments might have consisted of the determination of dislocation Burgers vectors using a g.b = 0 criterion from several different 2-beam images. Since the advent of the personal computer revolution, the available computing power for performing image-processing and image-simulation calculations is enormous and ubiquitous. Several programs now exist to perform simulations of diffraction contrast images using various approximations. The most common approximations are the use of only 2-beams or a single systematic row to calculate the image contrast, or calculating the image using a column approximation. The increasing amount of literature showing comparisons of experimental and simulated images shows that it is possible to obtain very close agreement between the two images; although the choice of parameters used, and the assumptions made, in performing the calculation must be properly dealt with. The simulation of the images of defects in materials has, in many cases, therefore become a tractable problem.

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