Elektronika 2009-11.pdf - Instytut Systemów Elektronicznych

Summary
The image set of recognition data, which has been used in
order to determine in percentage figures the efficiency of
a correct recognition of the size of stenoses in coronary arteries,
included 16 different spatial reconstructions obtained
for patients with heart disease (mostly ischemia). In this set,
we considered image sequences of patients previously
analysed at the stage of the grammar construction and the
recognising analyser. In order to avoid analysing identical reconstructions
we selected separate images occurring after
slight positions rotation (different projection) the ones used
originally (from spatial helical CT scans). The remaining images
in the test data have been obtained for a new group of
patients. The objective of an analysis of these data was to determine
in percentage the efficiency of the correct recognition
of artery stenosis and to determine their size with the use of
the grammar introduced. The recognition of such stenoses,
including the determination of their locations, lumens of the
artery, and the types (concentric or eccentric), was conducted
in such a way that while reasoning out the grammar for the
graph representation of the coronary vascularization, particular
edges of the graph determined the actual beginnings and
ends of particular sections of coronary arteries. During the
grammar reasoning and the course of the transform of embedding
graph representations on the actual images, the corresponding
sections of arteries were analysed with regard to
the presence of potential stenoses in them. The method of
this analysis also consisted in applying a context-free sequential
grammar to detect stenoses in 2D coronarography
images. Such a grammar has been defined in publications
[7-8]. Applying such a grammar to analyse particular sections
of arteries in the obtained spatial reconstructions turned out to
be quite effective, as it allowed the unanimous On the image
data tested, the efficiency of recognition amounted to 85%.
The value of the efficiency of recognition is determined by the
percentage fraction of the accurately recognized and measured
vessel stenoses compared to the number of all images
analyzed in the test. The recognition itself meant locating and
defining the type of stenosis, e.g. concentric or eccentric.
This work has been supported by the Ministry of Science and
Higher Education, Republic of Poland, under project number
N519 007 32/0978
References
[1] Ferrarini L. i in.: GAMEs: Growing and adaptive meshes for fully
automatic shape modeling and analysis. Medical Image Analysis,
11, 2007, 302-314.
[2] Seghers D. i in.: Minimal shape and intensity cost path segmentation.
IEEE Trans. on Medical Imaging, 26, 2007, 1115-
1129.
[3] Loeckx D. i in.: Temporal subtraction of thorax CR images using
a statistical deformation model. IEEE Trans. on Medical Imaging,
22, 2003, 1490-1504.
[4] Higgins W.E. i in.: System for analyzing true three-dimensional
angiograms. IEEE Trans. Med. Imag. 15, 1996, 377-385.
[5] Skomorowski M.: A Syntactic-Statistical Approach to Recognition
of Distorted Patterns. Jagiellonian University, Krakow, 2000.
[6] Tadeusiewicz R., Flasiński M.: Pattern Recognition. Warsaw,
1991.
[7] Ogiela M. R., Tadeusiewicz R.: Modern Computational Intelligence
Methods for the Interpretation of Medical Images.
Springer-Verlag, Berlin Heidelberg, 2008.
[8] Tadeusiewicz R., Ogiela M. R.: Medical Image Understanding
Technology. Springer Verlag, Berlin-Heidelberg, 2004.
Average individual classification probability function
(Przeciętna indywidualna funkcja prawdopodobieństwa klasyfikacji)
dr inż. MAREK LANDOWSKI 1,2 , prof. dr hab. inż. ANDRZEJ PIEGAT 2
1 Quantitative Methods Institute, Szczecin Maritime University, Szczecin
2 Faculty of Computer Science and Information Systems, West Pomeranian University of Technology, Szczecin
In everyday life people use different words to make various
mental calculations. Therefore the proper description of
a word is a crucial problem. There are two types of models of
linguistic concept: possibilistic model and probabilistic model.
In the possibilistic theory models of linguistic concept are described
by the membership function [1], whereas in probability
theory models of linguistic concept are described by
classification probability function [2,3]. Possibilistic models described
by the membership function are mainly used in fuzzy
control and in modeling input-output relation of an object like
y = f(x 1 , x 2 , …, x n ). Probabilistic models describing linguistic
concepts by classification probability function seem to perform
better in automatic thinking and decision making theory. Fuzzy
sets theory and probability theory are complementary and not
exclusive theories. These theories can work together though
they model semantic concepts differently [4].
The article focuses on probabilistic model of linguistic concept
[5], in particular on finding the average individual classification
probability function. According to the authors’ knowledge
average individual classification probability function are
the novelty in the word literature.
In the article there is the following order. Firstly the two
kinds of models of linguistics concepts are presented. Next
the interpretation is given as well as the methods of identification
average individual classification probability function.
Then the features of average individual classification probability
function received from the experiments are introduced.
Finally the conclusions are presented.
Probabilistic models of linguistic concept
from the group of people (population)
We distinguish two kinds of models of linguistic concept received
from the group of people (population):
• group (population) model,
• average, individual model of an average person of the group.
12 ELEKTRONIKA 11/2009