MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information

2007 ◽  
Vol 31 (2) ◽  
pp. 81-90 ◽  
Author(s):  
Julien Milles ◽  
Yue Min Zhu ◽  
Gérard Gimenez ◽  
Charles R.G. Guttmann ◽  
Isabelle E. Magnin
Keyword(s):  
Gray Level ◽  
Nonuniformity Correction ◽  
Intensity Nonuniformity ◽  
Gray Level Histogram

MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information

2004 ◽  
Author(s):  
Julien Milles ◽  
Yue Min Zhu ◽  
Nankuei Chen ◽  
Lawrence P. Panych ◽  
Gerard Gimenez ◽  
...  
Keyword(s):  
Gray Level ◽  
Nonuniformity Correction ◽  
Intensity Nonuniformity ◽  
Gray Level Histogram

Ultrasonic Tissue Characterization with Gray Level Histogram Width

2013 ◽  
pp. 17-17
Author(s):  
Kazuo Maeda ◽  
Masaji Utsu ◽  
Nobuhiro Yamamoto ◽  
Mariko Serizawa
Keyword(s):  
Tissue Characterization ◽  
Gray Level ◽  
Gray Level Histogram

A novel method for evaluating uterine cervical consistency using vaginal ultrasound gray-level histogram

2010 ◽  
Vol 38 (5) ◽  
Author(s):  
Tomoyuki Kuwata ◽  
Shigeki Matsubara ◽  
Nobuyuki Taniguchi ◽  
Akihide Ohkuchi ◽  
Takashi Ohkusa ◽  
...  
Keyword(s):  
Gray Level ◽  
Vaginal Ultrasound ◽  
Novel Method ◽  
Gray Level Histogram

CONNECTIONIST MODEL BINARIZATION

1991 ◽  
Vol 05 (04) ◽  
pp. 629-644 ◽  
Author(s):  
NOBORU BABAGUCHI ◽  
KOJI YAMADA ◽  
KOICHI KISE ◽  
YOSHIKAZU TEZUKA
Keyword(s):  
Back Propagation ◽  
Connectionist Model ◽  
Gray Level ◽  
Threshold Selection ◽  
Back Propagation Algorithm ◽  
Image Binarization ◽  
Multilayer Network ◽  
Binarization Method ◽  
Gray Level Histogram ◽  
Binarized Image

Image binarization is a task to convert gray-level images into bi-level ones. Its underlying notion can be simply thought of as threshold selection. However, the result of binarization will cause significant influence on the process of image recognition or understanding. In this paper we discuss a new binarization method, named CMB (connectionist model binarization), which uses the connectionist model. In the method a gray-level histogram is input to a multilayer network trained with the back-propagation algorithm to obtain a threshold which gives a visually suitable binarized image. From the experimental results, it was verified that CMB is an effective binarization method in comparison with other methods.

Statistical Image Analysis of Psychological Projective Drawings

2005 ◽  
Vol 9 (5) ◽  
pp. 453-460 ◽  
Author(s):  
Kazuhisa Takemura ◽  
◽  
Iyuki Takasaki ◽  
Yumi Iwamitsu ◽  
Keyword(s):  
Image Analysis ◽  
Difference Method ◽  
Gray Level ◽  
Projective Drawings ◽  
Standard Analysis ◽  
Projective Drawing ◽  
Statistical Image Analysis ◽  
Level Difference ◽  
Gray Level Histogram

We propose statistical image analysis for psychological projective drawings to facilitate assessing the reliability of the projective drawing making the determination of its validity difficult. Standard analysis involves (1) drawing a picture, (2) scanning the drawing, (3) dividing the drawing, (4) analyzing the gray level histogram moment (GLHM), (5) applying spatial gray level dependence method (SGLDM), (6) applying the gray level difference method (GLDM) for the drawing, and (7) interpreting the drawing. To demonstrate the proposed procedure, we used the tree test (Baum test). Three adults were presented with blank A4 paper and asked to draw a picture of a tree with fruit on it. Drawings were analyzed by statistical image analysis and results interpreted clinically.

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