Two techniques for extracting tissues from the male cadaver dataset were
compared. The first involved high-level decision-making algorithms. A
cryosection image was traversed using an image template. Compared pixels
were selected based on color value, position, and color variance. The
second technique utilized an artificial intelligence approach. A
feed-forward neural network provided a robust structure to detect
texture. This customized neural net retained information about the
color values and spatial location of a region in a cryosection image. A
back-propagation algorithm trained the net by adjusting for optimal
The decision-making algorithms produced satisfactory results for the
extraction of multiple tissue types: fat, bone, and muscle. Most of the
desired tissue was acquired although unwanted tissue was taken as well.
The neural net produced better results in differentiating between a
desired tissue type and all other tissue types in an image. The neural
net generated images with sharper edges and fewer regions of unwanted
tissue. The selection of unwanted tissue resulted from color
similarities among tissues in the body and the absence of texture
patterns in the images. The neural network offered a flexibility in
distinguishing different tissues that was not apparent in the results of
the decision-making algorithm. Although both methods use similar
traversal techniques through a cryosection image, the neural network
required more computation per pixel than the decision-making algorithms;
therefore, the decision-making methods were more efficient. The neural
network technique required more background processing and memory in
order to train weights, but the two methods used comparable amounts of
memory during the tissue extraction computations. The techniques were
not used together, but improved results may be generated if the methods
are combined or used in succession.
This work has shown that texture differences can be used successfully in
isolating tissues from cryosection images. Two very different approaches
to tissue segmentation were compared using minimal computing power on
readily accessible workstations.
In conclusion, we have shown that texture is a very strong key for
identifying tissue type, that it is possible to isolate tissue from cryosection images based on texture differences, and that
this can be done without the use of parallel processors or the
commitment of large amounts of memory.
Tammy Williams is majoring in computer science and mathematics at Gustavus Adolphus College. Tammy participated in the REU program for the Visible Human Project at UW-L during the summers of 1997 and 1998. She is hoping to attend graduate school after her graduation in May of 1999. Her interests include computational theory and several branches of mathematics. Tammy enjoys spending her free time on the golf course and in competition as a member of the Gustavus golf team.
Please send e-mail to Jennifer Casper or Tammy Williams.
The Visible Human Project
Marching Through the Visible Man
Volume Rendering of the Visible Human Data
Statistical Geometrical Texture Description
An Introduction to Neural Networks
What is a Neural Network?