Computer Simulation of Ultrasonic Images with Self-Similarity of Tissue Structures

This paper proposes a novel algorithm to estimate a log-compressed K distribution parameter and presents an algorithm to discriminate breast tumors in ultrasonic images. We computed a total of 208 features for discrimination, including those based on a parameter of a log-compressed K-distribution, which quantifies the homogeneity of the echo pattern in the tumor, but is influenced by compression parameters in the ultrasonic device. The proposed algorithm estimates the parameter of the log-compressed K-distribution in a manner free from this influence. To quantify irregularities in tumor shape, pattern-spectrum-based features were newly developed in this paper. The discrimination process uses an ensemble classifier trained by a multiclass AdaBoost learning algorithm (AdaBoost.M2), combined with a sequential feature-selection process. A 10-fold cross-validation test validated the performance, and the results were compared with those of a Mahalanobis distance-based classifier and a multiclass support vector machine. A total of 200 carcinomas, 50 fibroadenomas, and 50 cysts were used in the experiments. This paper demonstrates that the combination of a classifier trained by AdaBoost.M2 and features based on the estimated parameter of a log-compressed K-distribution, as well as those of the pattern spectrum, are useful for the discrimination of tumors.

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Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection

Takemura

Shimizu

Hamamoto

2010

IEEE Trans. Med. Imaging

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Modeling of the Cirrhotic Liver Considering the Liver Lobule Structure

Yamaguchi¹,

Hachiya²

1999

Jpn. J. Appl. Phys.

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Three-Dimensional Model of the Scatterer Distribution in Cirrhotic Liver

Yamaguchi

Nakamura

Hachiya

2003

Jpn. J. Appl. Phys.

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Computer Simulation of Ultrasonic Images with Self-Similarity of Tissue Structures

Cited by 3 publications

References 13 publications

Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection

Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection

Modeling of the Cirrhotic Liver Considering the Liver Lobule Structure

Three-Dimensional Model of the Scatterer Distribution in Cirrhotic Liver

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