Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems

Tunghai University Institutional Repository > 工學院 > 資訊工程學系所 > 期刊論文 > Item 310901/21472

Please use this identifier to cite or link to this item: http://140.128.103.80:8080/handle/310901/21472

Title:	Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems
Authors:	Huang, Y.-L.a, Kuo, S.-J.b, Chang, C.-S.c, Liu, Y.-K.a, Moon, W.K.d, Chen, D.-R.
Contributors:	Department of Computer Science, Tunghai University
Keywords:	Breast cancer;Computer-aided diagnosis;Image retrieval;Principal component analysis;Textural analysis;Ultrasound
Date:	2005
Issue Date:	2013-05-14T09:07:09Z (UTC)
Abstract:	Objectives: We present a computer-aided diagnostic (CAD) system with textural features and image retrieval strategies for classifying benign and malignant breast tumors on various ultrasonic systems. Effective applications of CAD have used different types of texture analysis. Nevertheless, most approaches performed in a specific ultrasonic machine do not indicate whether the technique functions satisfactorily for other ultrasonic systems. This study evaluated a series of pathologically proven breast tumors using various ultrasonic systems. Methods: Altogether, 600 ultrasound images of solid breast nodules comprising 230 malignant and 370 benign tumors were investigated. All ultrasound images were acquired from four diverse ultrasonic systems. The suspicious tumor area in the ultrasound image was manually chosen as the region-of-interest (ROI) subimage. Textural features extracted from the ROI subimage are supported in classifying the breast tumor as benign or malignant. However, the textural feature always behaves as a high-dimensional vector. In practice, high-dimensional vectors are unsatisfactory at differentiating breast tumors. This study applied the principal component analysis (PCA) to project the original textural features into a lower dimensional principal vector that summarized the original textural information. The image retrieval techniques were employed to differentiate breast tumors, according to the similarities of the principal vectors. The query ROI subimages were identified as malignant or benign tumors according to characteristics of retrieved images from the ultrasound image database. Results: Using the proposed CAD system, historical cases could be directly added into the database without a retraining program. The area under the receiver-operating characteristics curve for the system was 0.970 ? 0.006. Conclusion: The CAD system identified solid breast nodules with comparatively high accuracy in the different ultrasound systems investigated. Copyright ? 2005 ISUOG. Published by John Wiley & Sons, Ltd.
Relation:	Ultrasound in Obstetrics and Gynecology Volume 26, Issue 5, October 2005, Pages 558-566
Appears in Collections:	[資訊工程學系所] 期刊論文

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