Signal Analysis Research (SAR) Group - RNet - Ryerson University

images contain localized heterogeneous texture elements,
whereas normal images are smooth (uniform). Utilization of
the SIDWT allowed for the extraction of consistent (i.e. shiftinvariant)
features. Furthermore, due to the scale-invariant
basis functions of the DWT, pathologies of varying sizes
were captured within one transformation (i.e. the features
were scale-invariant).
The system is relatively robust to the different camera
angles by design. Since the viewing angle is different from
image to image, features were collected at various angles (0 ◦ ,
45 ◦ , 90 ◦ , 135 ◦ ) in the respective subbands in order to account
for the texture properties, regardless of the orientation. The
feature set thus offered a semi-rotational invariant representation
which could account for oriented textural properties at
various angles within the gastrointestinal tract.
Since this is the first work in the area of small bowel
image classification, the results are promising and show
great potential for applications such as CAD and CBIR. This
is especially true since all features were extracted in a fullyautomated
manner without any intervention or assistance
from a gastroenterologist. This means that such a system
could in fact be used as a tool which could either (1) sort
the 8 hours of film and highlight suspicious regions or (2)
automatically retrieve a specific region or mass, without
having to use text annotations.
Although the classification results are high, any misclassification
can be accounted to cases where there is a lack
of statistical differentiation between the texture uniformity
of the abnormal and normal small bowel images. Additionally,
normal tissue can sometimes assume the properties
of abnormal regions; for example, consider a normal small
bowel image which has more than the average amount of
folds. This may be characterized as non-uniform texture and
consequently would be misclassified.
Another important consideration arises from the sizes of
the databases. As was stated in Section II-E, the number of
images used for classification can determine the accuracy
of the estimated classifier parameters. Since only a modest
number of images were used, misclassification could result
due to the lack of proper estimation of the classifiers parameters
(although the scheme tried to combat this with LOOM).
Additionally, finding the right trade off between number of
features and database size is an ongoing research topic and
has yet to be perfectly defined [8].
A last point for discussion is the fact that features were
successfully extracted from the compressed domain. Since
many forms of multi-media are being stored in lossy formats,
it is important that classification systems may also be
successful when utilized in the compressed domain.
Fig. 2. System block diagram for the classification of small bowel images.
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IV. CONCLUSIONS
A unified feature extraction and classification scheme was
developed using the DWT for small bowel images and this
is the first reported work in the area. Textural features
were extracted from the wavelet domain in order to obtain
localized numerical descriptors of the relative homogeneity
of the small bowel images. To ensure the DWT representation
was suitable for the consistent extraction of features, a shiftinvariant
discrete wavelet transform (SIDWT) was computed.
To combat the small database size, a small number of
features and LDA classification were used in conjunction
with the LOOM to gain a more accurate approximation of
the classifier’s parameters.
Seventy-five abnormal and normal bowel images were
correctly classified at rate of 85% specificity and 85%
sensitivity. The success of the system can be accounted
to the semi-rotational invariant, scale-invariant and shiftinvariant
features, which permitted the extraction of discriminating
features for multiple camera angles and various sized
pathologies. Due to the success of the proposed work, it may
be used in a CAD scheme or a CBIR application, to assist the
gastroenterologists to diagnose and sort 8 hours of footage.
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