208
Biologically Inspired Robotics
clinician about 2 hours on average to review and analyze all of the video
data (Adeler and Gostout 2003). In addition, abnormalities in the GI tract
may be present in only one or two frames of the video, so they might be
missed by physicians due to oversight. Moreover, some abnormalities cannot be detected by the naked eye due to their size, texture, and distribution.
Furthermore, different clinicians may have different findings when viewing
the same images. Such problems motivate researchers to design reliable and
uniform assistive approaches to relieve physicians. However, such a goal
is very challenging because true features associated with diseases are not
exactly known or well defined. Moreover, different diseases have different
symptoms in the digestive tract, and some diseases show great variations in
color and shape.
Due to CE’s wide application, many efforts have been made to develop
computer-aided detection of CE images to decrease the burden on doctors.
Bashar et al. (2008) proposed a method using color and texture to choose
informative frames from the CE video. A novel scheme for choosing MPEG-7
visual descriptors as a feature extractor to recognize several diseases such
as ulcers and bleeding in the GI tract was proposed in Coimbra and Cunha
(2006). Based on this work, they proceeded to develop two approaches to segment the GI tract into four major topographic areas (Cunha et al. 2008), and
the first software that utilizes these approaches aiming for CE examination
was also introduced in this paper. A scheme using color distribution to discriminate stomach, intestine, and colon tissue in CE images was proposed
by Berens, Mackiewicz, and Bell (2005). In Zabulis, Argyros, and Tsakiris
(2008), the authors employed two cues, that is, lumen and illumination highlight, for navigation of active CE. Recently, Bejakovic et al. (2009) proposed
making use of color, texture, and edge features to analyze Crohn’s disease
lesions from CE images. Szczypinski et al. (2009) suggested a novel model of
deformable rings to interpret CE video, which allows a quick review of the
whole video. We have investigated bleeding, ulcer, and tumor region detection for CE images in our previous works (Li and Meng 2009a, 2009b, 2009c,
2009d), which mainly concentrate on features that are suitable to describe
bleeding, ulcers, and tumors in CE images.
Because polyps are common in the GI tract, we focus on polyp CE image
recognition in this chapter. To achieve this goal, we advance a new scheme
that exploits color and shape features. The proposed new feature integrates
a chromaticity histogram with a Zernike moment shape descriptor to differentiate between a normal CE image and a polyp image. Experimental results
from our present data validate this new scheme’s ability to achieve performance for polyp CE image detection when using a multilayer perceptron
neural network as the classifier.
The remainder of this chapter is organized as follows. The color and
shape features for polyp detection are discussed in detail in the following
section. In Section 11.3, experimental results are presented and discussed.
Conclusions are drawn at the end of this chapter.
Biologically Inspired Robotics
clinician about 2 hours on average to review and analyze all of the video
data (Adeler and Gostout 2003). In addition, abnormalities in the GI tract
may be present in only one or two frames of the video, so they might be
missed by physicians due to oversight. Moreover, some abnormalities cannot be detected by the naked eye due to their size, texture, and distribution.
Furthermore, different clinicians may have different findings when viewing
the same images. Such problems motivate researchers to design reliable and
uniform assistive approaches to relieve physicians. However, such a goal
is very challenging because true features associated with diseases are not
exactly known or well defined. Moreover, different diseases have different
symptoms in the digestive tract, and some diseases show great variations in
color and shape.
Due to CE’s wide application, many efforts have been made to develop
computer-aided detection of CE images to decrease the burden on doctors.
Bashar et al. (2008) proposed a method using color and texture to choose
informative frames from the CE video. A novel scheme for choosing MPEG-7
visual descriptors as a feature extractor to recognize several diseases such
as ulcers and bleeding in the GI tract was proposed in Coimbra and Cunha
(2006). Based on this work, they proceeded to develop two approaches to segment the GI tract into four major topographic areas (Cunha et al. 2008), and
the first software that utilizes these approaches aiming for CE examination
was also introduced in this paper. A scheme using color distribution to discriminate stomach, intestine, and colon tissue in CE images was proposed
by Berens, Mackiewicz, and Bell (2005). In Zabulis, Argyros, and Tsakiris
(2008), the authors employed two cues, that is, lumen and illumination highlight, for navigation of active CE. Recently, Bejakovic et al. (2009) proposed
making use of color, texture, and edge features to analyze Crohn’s disease
lesions from CE images. Szczypinski et al. (2009) suggested a novel model of
deformable rings to interpret CE video, which allows a quick review of the
whole video. We have investigated bleeding, ulcer, and tumor region detection for CE images in our previous works (Li and Meng 2009a, 2009b, 2009c,
2009d), which mainly concentrate on features that are suitable to describe
bleeding, ulcers, and tumors in CE images.
Because polyps are common in the GI tract, we focus on polyp CE image
recognition in this chapter. To achieve this goal, we advance a new scheme
that exploits color and shape features. The proposed new feature integrates
a chromaticity histogram with a Zernike moment shape descriptor to differentiate between a normal CE image and a polyp image. Experimental results
from our present data validate this new scheme’s ability to achieve performance for polyp CE image detection when using a multilayer perceptron
neural network as the classifier.
The remainder of this chapter is organized as follows. The color and
shape features for polyp detection are discussed in detail in the following
section. In Section 11.3, experimental results are presented and discussed.
Conclusions are drawn at the end of this chapter.