Anomalies in the Hough Transform of Kikuchi Bands in EBSD

The Hough Transform is widely used to detect linear features in image processing techniques. However, the features sought in the EBSD patterns are typically Kikuchi bands from low-index crystal planes, which are not lines but are bands of above average intensity bordered by dark bands. This gives rise to a characteristic shaped peak in the Hough transform that has been called the “butterfly” shape. in most cases, to reduce noise, the Hough Transform is convoluted with a mask having a matching “butterfly” shape. Unfortunately, this method sacrifices resolution through averaging the intensity of neighboring pixels. We have been concerned to use the Hough transform to locate the position of the Kikuchi bands with the highest possible precision. in order to achieve this goal we have looked into the way the Hough transform works on Kikuchi bands in detail, and found some anomalies that must be considered if accuracy is to be achieved.

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Detection of Microbubbles Using the Hough Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.378.478 ◽

2013 ◽

Vol 378 ◽

pp. 478-482

Author(s):

Yoshihiro Mitani ◽

Toshitaka Oki

Keyword(s):

Image Processing ◽

Error Rate ◽

Hough Transform ◽

Detection Method ◽

Experimental Results ◽

Average Error ◽

Image Processing Techniques ◽

Average Error Rate ◽

Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

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Lanelok: Detection Of Lane Boundaries And Vehicle Tracking Using Image-Processing Techniques - Part I: Hough-Transform, Region-Tracing And Correlation Algorithms

10.1117/12.969885 ◽

1990 ◽

Cited By ~ 5

Author(s):

Surender K. Kenue

Keyword(s):

Image Processing ◽

Hough Transform ◽

Vehicle Tracking ◽

Image Processing Techniques ◽

Processing Techniques

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Three-Dimensional Structure of Rotavirus-Fab Complex

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100179944 ◽

1990 ◽

Vol 48 (1) ◽

pp. 238-239

Author(s):

B.V.V. Prasad ◽

E. Marietta ◽

J.W. Burns ◽

M.K. Estes ◽

W. Chiu

Keyword(s):

Image Processing ◽

Smooth Surface ◽

Three Dimensional ◽

Outer Shell ◽

Dimensional Structure ◽

Structural Studies ◽

Cell Penetration ◽

Electron Cryomicroscopy ◽

Image Processing Techniques ◽

Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

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A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

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

10.32628/cseit2063211 ◽

2020 ◽

pp. 967-973

Author(s):

V. Deepika ◽

T. Rajasenbagam

Keyword(s):

Image Processing ◽

Brain Tumor ◽

Soft Tissues ◽

Tumor Detection ◽

Tumor Classification ◽

Normal Brain ◽

Image Processing Techniques ◽

Normal Brain Function ◽

Processing Techniques ◽

The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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