CONTENTS
11.1 Introduction ................................................................................................ 206
11.2 Color and Shape Feature Analysis .......................................................... 209
11.2.1 Color Feature .................................................................................. 210
11.2.2 Shape Feature ................................................................................. 212
11.3 Experimental Results ................................................................................ 213
11.4 Conclusions ................................................................................................. 216
Acknowledgment ................................................................................................ 216
References ............................................................................................................. 216
11
Bowel Polyp Detection in Capsule Endoscopy
Images with Color and Shape Features
Baopu Li and Max Q.-H. Meng
The Chinese University of Hong Kong
Hong Kong, China
Abstract
Capsule endoscopy (CE) has been widely applied in hospitals because
it can be used to directly view the whole small intestine in the human
body. However, a major drawback of this technology is the tedious
review process of about 50,000 images produced in each examination.
To relieve physicians and provide support for their decision making,
computerized detection of disease is highly desired. In this chapter, we
put forward a novel scheme for bowel polyp detection for CE images
that integrates color and shape information, which are important visual
clues for physicians. An illumination-invariant color feature built upon
a chromaticity histogram is suggested. Combining it with Zernike
moments that are scale, translation, and rotation invariant, we exploit
the integrated information as color and shape features to discriminate
polyp CE images from normal ones. By using a multilayer percetron
neural network and support vector machine as classifiers, we perform
experimental results on our collected CE data, illustrating encouraging
performance of detection for polyp CE images.
205
11.1 Introduction ................................................................................................ 206
11.2 Color and Shape Feature Analysis .......................................................... 209
11.2.1 Color Feature .................................................................................. 210
11.2.2 Shape Feature ................................................................................. 212
11.3 Experimental Results ................................................................................ 213
11.4 Conclusions ................................................................................................. 216
Acknowledgment ................................................................................................ 216
References ............................................................................................................. 216
11
Bowel Polyp Detection in Capsule Endoscopy
Images with Color and Shape Features
Baopu Li and Max Q.-H. Meng
The Chinese University of Hong Kong
Hong Kong, China
Abstract
Capsule endoscopy (CE) has been widely applied in hospitals because
it can be used to directly view the whole small intestine in the human
body. However, a major drawback of this technology is the tedious
review process of about 50,000 images produced in each examination.
To relieve physicians and provide support for their decision making,
computerized detection of disease is highly desired. In this chapter, we
put forward a novel scheme for bowel polyp detection for CE images
that integrates color and shape information, which are important visual
clues for physicians. An illumination-invariant color feature built upon
a chromaticity histogram is suggested. Combining it with Zernike
moments that are scale, translation, and rotation invariant, we exploit
the integrated information as color and shape features to discriminate
polyp CE images from normal ones. By using a multilayer percetron
neural network and support vector machine as classifiers, we perform
experimental results on our collected CE data, illustrating encouraging
performance of detection for polyp CE images.
205