Computer-Aided Diagnosis

Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound

[+] Author Affiliations
Haixia Liu

Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands

University of Nottingham Malaysia Campus, School Of Computer Science, Room BB79, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia

Tao Tan

Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands

Jan van Zelst

Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands

Ritse Mann

Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands

Nico Karssemeijer

Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands

Bram Platel

Radboud University Medical Centre, Department of Radiology and Nuclear Medicine, 6525 GA Nijmegen, The Netherlands

J. Med. Imag. 1(2), 024501 (Jul 25, 2014). doi:10.1117/1.JMI.1.2.024501
History: Received March 4, 2014; Revised June 22, 2014; Accepted June 26, 2014
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Abstract.  We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features (p<0.001).

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Haixia Liu ; Tao Tan ; Jan van Zelst ; Ritse Mann ; Nico Karssemeijer, et al.
"Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound", J. Med. Imag. 1(2), 024501 (Jul 25, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.2.024501


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