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Title:
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ESTIMATION OF A LOG-COMPRESSED NAKAGAMI DISTRIBUTION AND APPLICATION TO BREAST TUMOR DISCRIMINATION IN ULTRASONIC IMAGES |
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Author(s):
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Atsushi Takemura |
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ISBN:
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978-972-8924-97-3 |
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Editors:
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Hans Weghorn and Pedro Isaías |
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Year:
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2009 |
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Edition:
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V II, 2 |
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Keywords:
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Ultrasonic image, breast tumor, log-compressed Nakagami distribution, geodesic active contour, AdaBoost |
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Type:
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Short Paper |
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First Page:
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191 |
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Last Page:
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196 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper proposes a new method for estimation of a Nakagami distribution parameter from a log-compressed ultrasonic
image. This paper also describes effective features and classifiers for accurate discrimination of breast tumors in
ultrasonic images. Total 127 features were defined corresponding to diagnostic observations used by physicians, such as
internal echo, shape, and boundary echo. These features included novel features based on the estimated parameter of the
log-compressed Nakagami distribution. Furthermore, this paper proposes a method for discrimination of breast tumors
using a geodesic active contour model, and an ensemble classifier trained by the multi-class AdaBoost learning algorithm
combined with a sequential feature selection process. Validation testing using 200 carcinomas, 50 fibroadenomas, and 50
cysts showed the high performance of the proposed method of segmentation and discrimination. |
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