ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

Fig.1 Modules of the proposed capsule’s architecture.
4.2 Narrow Band Imaging (NBI)
It is most appropriate, by the principle of NBI, to choose 415nm (blue) to observe
capillaries on the surface and 540 nm (green) for thicker vessels. Deeper blood vessels
are reproduced in 600nm image (red). In this project, we decided to use white light and
NBI filters in the blue and green wavelengths as some tests were made, and we got good
results in these frequencies. Early stage cancer develops on the superficial layer and
changes the blood structure there, so the using 600nm in NBI would not contribute
much. NBI shows cancer areas, having a brown appearance, so the contour between
normal mucosa and a lesion can be easily identified. Through white light, it is difficult
to observe this contour, being easier to recognize a superficial red cancer. The idea of
using AFI was eliminated as it needs the ingestion of a fluorescent drug and NBI seems
to be a better option [12].
Figure 2 shows a test made in bleeding small intestine with the PillCam SB. With the
blue and green filters, the mucosa looks in the color of the filter and blood appears
black. Blue filter enhances the superficial capillaries and green filter improves the
visualization of subepithelial vessels. NBI enhances the contrast between the normal
and abnormal mucosa, being very useful to dysplasia detection and differentiation. It is
very good to detect polyps, which are hardly seen with white light. Vascular pattern
intensity is an additional feature in differentiating dysplastic and non-dysplastic lesions.
Early detection of GI lesions is very important as it could reduce mortality of people
due to these disorders. To objectively document detection and removal of GI polyps for
quality purposes, and to facilitate real-time detection of polyps in the future, a
computer-based research program that analyzes video files created during the capsule
trajectory must be created. For computer-based detection of polyps, texture based
techniques and neural networks are proposed.
Fig. 2- Small intestine with white light, blue filter and green filter.