Biomedical Engineering Reference
In-Depth Information
cancer that is ten times that of nonsmokers and accounts for greater than 80%
of lung cancer cases in the United States [21].
One in every 18 women and every 12 men develop lung cancer, making it
the leading cause of cancer deaths. Early detection of lung tumors (visible on
the chest film as nodules) may increase the patient's chance of survival. For
this reason the Jewish Hospital designed a program for early detection with
the following specific aims: A number of lung cancer screening trials have been
conducted in the United States, Japan, and Europe for the purpose of developing
an automatic approach of tummor detection [21].
At the University of Louisville CVIP Lab a long-term effort has been ensued
to develop a comprehensive image analysis system to detect and recognize lung
nodules in low dose chest CT (LDCT) scans. The LDCT scanning was performed
with the following parameters: slice thickness of 8 mm reconstructed every 4
mm and scanning pitch of 1.5. In the following section we highlight our approach
for automatic detection and recognition of lung nodules; further details can be
found in [22].
9.3.1 Lung Extraction
The goal of lung extraction is to separate the voxels corresponding to lung tissue
from those belonging to the surrounding anatomical structures. We assume that
each slice consists of two types of pixels: lung and other tissues (e.g., chest,
ribs, and liver). The problem in lung segmentation is that there are some tissues
in the lung such as arteries, veins, bronchi, and bronchioles having gray level
close to the gray level of the chest. Therefore, in this application if we depend
only on the gray level we lose some of the lung tissues during the segmentation
process. Our proposed model which depends on estimating parameters for two
processes (high-level process and low-level process) is suitable for this appli-
cation because the proposed model not only depend on the gray level but also
takes into consideration the characterization of spatial clustering of pixels into
regions.
We will apply the approach that was described in Section 9.2.4 on lung CT.
Figure 9.4 shows a typical CT slice for the chest. We assume that each slice
consists of two types of tissues: lung and other tissues (e.g., chest, ribs, and
liver). As discussed above, we need to estimate parameters for both low-level
process and high-level process. Table
9.1 presents the results of applying the
