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.

show abstract

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

View full text Add to dashboard Cite

show abstract

Modeling of the Cirrhotic Liver Considering the Liver Lobule Structure

Yamaguchi¹,

Hachiya²

1999

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

Three-Dimensional Model of the Scatterer Distribution in Cirrhotic Liver

Yamaguchi

Nakamura

Hachiya

2003

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Contact Info

Product

Resources

About