Zero capacity region of multidimensional run length constraints

2002 ◽  
Author(s):  
H. Ito ◽  
A. Kato ◽  
Z. Nagy ◽  
K. Zeger
Keyword(s):  
Capacity Region ◽  
Run Length ◽  
Zero Capacity

scholarly journals Zero Capacity Region of Multidimensional Run Length Constraints

10.37236/1465 ◽  
1999 ◽  
Vol 6 (1) ◽  
Author(s):  
Hisashi Ito ◽  
Akiko Kato ◽  
Zsigmond Nagy ◽  
Kenneth Zeger
Keyword(s):  
Binary Sequence ◽  
Capacity Region ◽  
Run Length ◽  
One Dimensional ◽  
Coordinate Axis ◽  
Length Constraint ◽  
Zero Capacity ◽  
The One ◽  
Constrained Capacity

For integers $d$ and $k$ satisfying $0 \le d \le k$, a binary sequence is said to satisfy a one-dimensional $(d,k)$ run length constraint if there are never more than $k$ zeros in a row, and if between any two ones there are at least $d$ zeros. For $n\geq 1$, the $n$-dimensional $(d,k)$-constrained capacity is defined as $$C^{n}_{d,k} = \lim_{m_1,m_2,\ldots,m_n\rightarrow\infty} {{\log_2 N_{m_1,m_2,\ldots ,m_n}^{(n; d,k)}} \over {m_1 m_2\cdots m_n}} $$ where $N_{m_1,m_2,\ldots ,m_n}^{(n; d,k)}$ denotes the number of $m_1\times m_2\times\cdots\times m_n$ $n$-dimensional binary rectangular patterns that satisfy the one-dimensional $(d,k)$ run length constraint in the direction of every coordinate axis. It is proven for all $n\ge 2$, $d\ge1$, and $k>d$ that $C^{n}_{d,k}=0$ if and only if $k=d+1$. Also, it is proven for every $d\geq 0$ and $k\geq d$ that $\lim_{n\rightarrow\infty}C^{n}_{d,k}=0$ if and only if $k\le 2d$.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Line interpolation algorithm based on diagonal run-length

2009 ◽  
Vol 28 (9) ◽  
pp. 2270-2273
Author(s):  
Xiao-tong YE ◽  
Yun DENG
Keyword(s):  
Interpolation Algorithm ◽  
Run Length
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