A Multi-Functional Interactive Image Processing Tool for Lung CT Images

An important requirement for clinical diagnosis and treatment is a platform for medical image processing technique that is more flexible and accurate. An interactive image processing method that can be used as a Computer Aided Detection (CAD) tool for medical image processing is proposed in this paper. The tool is developed in MATLAB to read images of different formats like tif, jpg, DICOM etc. The tool is also capable of displaying information about the loaded image of the selected format, read and save images from and to workspace. In addition to this user can use functional tools like determining the value of pixel in the image, obtain histogram, horizontal and vertical profiles of selected lines of the image, color maps of CT window that include CT bone, CT spine, CT mediastinal, auto adjustment of global intensity and selective intensity, image smoothening, manual and auto thresholding. All the mentioned functions are integrated in Graphical User Interface which is user friendly.

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A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183863 ◽

2018 ◽

pp. 194-200

Author(s):

J. Magelin Mary ◽

Chitra K. ◽

Y. Arockia Suganthi

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Processing Technique ◽

Input Image ◽

Image Processing Technique ◽

Computational Time ◽

Image Mining ◽

The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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Medical image processing and computer-aided detection/diagnosis (CAD)

2012 International Conference on Computerized Healthcare (ICCH) ◽

10.1109/icch.2012.6724473 ◽

2012 ◽

Cited By ~ 2

Author(s):

H. Fujita ◽

F. Nogata ◽

H. Jiang ◽

S. Kido ◽

T. Feng ◽

...

Keyword(s):

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Computer Aided Detection ◽

Computer Aided

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Review on 2D and 3D MRI Image Segmentation Techniques

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405613666171123160609 ◽

2019 ◽

Vol 15 (2) ◽

pp. 150-160 ◽

Cited By ~ 4

Author(s):

S. Shirly ◽

K. Ramesh

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Medical Image ◽

Medical Diagnostics ◽

Processing Technique ◽

Image Features ◽

Medical Image Segmentation ◽

Image Processing Technique ◽

Edge Based ◽

Mri Image

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

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A New Image Processing Technique for STEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100052171 ◽

1974 ◽

Vol 32 ◽

pp. 432-433

Author(s):

Yasushi Kokubo ◽

Hirotami Koike ◽

Teruo Someya

Keyword(s):

Electron Microscopy ◽

Image Processing ◽

Scanning Electron Microscopy ◽

Elastic Scattering ◽

Field Emission ◽

Processing Technique ◽

Image Processing Technique ◽

Energy Analyzer ◽

Scanning Microscope ◽

Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

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An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0107 ◽

2017 ◽

Vol 7 (7) ◽

pp. 408

Author(s):

Yashpal Jitarwal ◽

Tabrej Ahamad Khan ◽

Pawan Mangal

Keyword(s):

Image Processing ◽

Texture Features ◽

Automatic Classification ◽

Processing Technique ◽

Image Processing Technique ◽

Advance Technique ◽

Time Required ◽

Fruit Sorting ◽

Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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