Computer Aided Diagnosis of Parotid Gland Lesions Using Ultrasonic Multi-Feature Tissue Characterization
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- 作者：Stefan Siebers ; Johannes Zenk ; Aless ; ro Bozzato ; Nils Klintworth ; Heinrich Iro ; Helmut Ermert
- 关键词：Multi-feature classification ; Ultrasonic tissue characterization ; Tissue typing ; Sonohistology ; Parotid gland tumors ; Spectral backscatter and attenuation estimation ; Texture analysis ; Contour analysis
- 刊名：Ultrasound in Medicine and Biology
- 出版年：2010
- 出版时间：September 2010
- 年：2010
- 卷：36
- 期：9
- 页码：1525-1534
- 全文大小：696 K
文摘
In this article, an ultrasound based system for computer aided characterization of biologic tissue and its application to differential diagnosis of parotid gland lesions is proposed. Aiming at an automated differentiation between malignant and benign cases, the system is based on a supervised classification using tissue-describing features derived from ultrasound radio-frequency (RF) echo signals and image data. Standard diagnostic ultrasound equipment was employed to acquire ultrasound RF echo data from parotid glands of 138 patients. Lesions were manually demarcated as regions-of-interest (ROIs) in the B-mode images. Spectral ultrasound backscatter and attenuation parameters are estimated from diffraction corrected RF data, yielding spatially resolved parameter images. Histogram based statistical measures derived from the parameters distributions inside the ROI are used as tissue describing features. In addition, texture features and shape descriptors are extracted from demodulated ultrasound image data. The features are processed by a maximum likelihood classifier. An optimal set of 10 features was chosen by a sequential forward selection algorithm. The classifier’s performance is evaluated using total cross validation and receiver operating characteristic (ROC) analysis. As a reference method, postoperative pathohistologic analysis was conducted and proved malignancy or prospective malignancy in 51 patients. The classification using the proposed system yielded an area under the ROC curve of 0.91, proving significant potential for differentiating between malignant and benign parotid gland lesions. (E-mail: stefan.siebers@rub.de)