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Image retrieval with principal component analysis for breast cancer diagnosis on various ultrasonic systems
Author(s) -
Huang Y.L.,
Kuo S.J.,
Chang C.S.,
Liu Y.K.,
Moon W. K.,
Chen D.R.
Publication year - 2005
Publication title -
ultrasound in obstetrics and gynecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.202
H-Index - 141
eISSN - 1469-0705
pISSN - 0960-7692
DOI - 10.1002/uog.1951
Subject(s) - principal component analysis , region of interest , ultrasonic sensor , artificial intelligence , medicine , computer science , ultrasound , computer aided diagnosis , feature (linguistics) , cad , pattern recognition (psychology) , breast ultrasound , feature vector , breast cancer , computer vision , radiology , cancer , mammography , engineering drawing , philosophy , engineering , linguistics
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.