| Title |
A Study of CBIR(Content-based Image Retrieval) Computer-aided Diagnosis System of Breast Ultrasound Images using Similarity Measures of Distance |
| Authors |
김민정(Kim, Min-jeong) ; 조현종(Cho, Hyun-chong) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.8.1272 |
| Keywords |
Computer-aided Diagnosis(CADx) ; Breast Cancer ; Ultrasound Images ; Similarity Measures |
| Abstract |
To assist radiologists for the characterization of breast masses, Computer-aided Diagnosis(CADx) system has been studied. The CADx system can improve the diagnostic accuracy of radiologists by providing objective information about breast masses. Morphological and texture features were extracted from the breast ultrasound images. Based on extracted features, the CADx system retrieves masses that are similar to a query mass from a reference library using a k-nearest neighbor (k-NN) approach. Eight similarity measures of distance, Euclidean, Chebyshev(Minkowski family), Canberra, Lorentzian(F_2 family), Wave Hedges, Motyka(Intersection family), and Cosine, Dice(Inner Product family) are evaluated by ROC(Receiver Operating Characteristic) analysis. The Inner Product family measure used with the k-NN classifier provided slightly higher performance for classification of malignant and benign masses than those with the Minkowski, F_2, and Intersection family measures. |