Biomedical Engineering Reference
In-Depth Information
clique coefficients, where each clique coefficient is represented by 4 bits (note
that for first-order system, we estimate only five parameters, and the remaining
clique's coefficient will be zero, but for the second-order system we will estimate
ten parameters).
Fitness Function:
Since our goal is to select the high-level process
X
that
maximize Eq. 9.5, we can use Eq. 9.5 as the fitness function.
High-level parameters estimation algorithm:
1. Generate the first generation which consists of 30 chromosomes.
2. Apply the Metropolis algorithm for each chromosome on each image and
then compute the fitness function as shown in Eq. 9.5.
3. If the fitness values for all chromosomes do not change from one popula-
tion to another population, then stop and select the chromosome, which
gives maximum fitness value. (If there are two chromosomes that give
the same fitness value, we select the chromosome which represents lower
order system.) Otherwise go to step 2.
Using the results obtained by this algorithm, we will repeat the estimation
of low-level process and high-level process. We will stop when the difference
between the current parameters and previous parameters is small.
9.3 Applications
Lung Cancer remains the leading cause of mortality cancer. In 1999, there were
approximately 170 000 new cases of lung cancer [21]. The 5-year survival rate
from the diseases is 14% and has increased only slightly since the early 1970s
despite extensive and expensive research work to find effective therapy. The
disparity in survival between early and late-stage lung cancer is substantial,
with a 5-year survival rate of approximately 70% in stage 1A disease compared
to less than 5% in stage IV disease according to the recently revised lung cancer
staging criteria [21]. The disproportionately high prevalence of and mortality
from lung cancer has encouraged attempts to detect early lung cancer with
screening programs aimed at smokers. Smokers have an incidence rate of lung
