Abstract: Growth and remodeling of biological structures have
gained lots of attention over the past decades. Determining the
response of living tissues to mechanical loads is necessary for a wide
range of developing fields such as prosthetics design or computerassisted
surgical interventions. It is a well-known fact that biological
structures are never stress-free, even when externally unloaded. The
exact origin of these residual stresses is not clear, but theoretically,
growth is one of the main sources. Extracting body organ’s shapes
from medical imaging does not produce any information regarding
the existing residual stresses in that organ. The simplest cause of such
stresses is gravity since an organ grows under its influence from
birth. Ignoring such residual stresses might cause erroneous results in
numerical simulations. Accounting for residual stresses due to tissue
growth can improve the accuracy of mechanical analysis results. This
paper presents an original computational framework based on gradual
growth to determine the residual stresses due to growth. To illustrate
the method, we apply it to a finite element model of a healthy human
face reconstructed from medical images. The distribution of residual
stress in facial tissues is computed, which can overcome the effect of
gravity and maintain tissues firmness. Our assumption is that tissue
wrinkles caused by aging could be a consequence of decreasing
residual stress and thus not counteracting gravity. Taking into
account these stresses seems therefore extremely important in
maxillofacial surgery. It would indeed help surgeons to estimate
tissues changes after surgery.
Abstract: Human face has a fundamental role in the appearance
of individuals. So the importance of facial surgeries is undeniable.
Thus, there is a need for the appropriate and accurate facial skin
segmentation in order to extract different features. Since Fuzzy CMeans
(FCM) clustering algorithm doesn’t work appropriately for
noisy images and outliers, in this paper we exploit Possibilistic CMeans
(PCM) algorithm in order to segment the facial skin. For this
purpose, first, we convert facial images from RGB to YCbCr color
space. To evaluate performance of the proposed algorithm, the
database of Sahand University of Technology, Tabriz, Iran was used.
In order to have a better understanding from the proposed algorithm;
FCM and Expectation-Maximization (EM) algorithms are also used
for facial skin segmentation. The proposed method shows better
results than the other segmentation methods. Results include
misclassification error (0.032) and the region’s area error (0.045) for
the proposed algorithm.