1 |
H. Cho, L. Hadjiiski, B. Sahiner, H. P. Chan, M. Helvie, C. Paramagul, et al., "Similarity evaluation in a contentbased image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images," Medical Physics, vol. 38, pp. 1820-1831, Apr 2011.
DOI
|
2 |
P. Espin-Lopez, A. Martellosio, M. Pasian, M. Bozzi, L. Perregrini, A. Mazzanti, et al., "Breast cancer imaging at mm-Waves: Feasibility study on the safety exposure limits," in Microwave Conference (EuMC), 2016 46th European, 2016, pp. 667-670.
|
3 |
H. Cho, L. Hadjiiski, B. Sahiner, H. P. Chan, M. Helvie, C. Paramagul, et al., "A similarity study of content‐based image retrieval system for breast cancer using decision tree," Medical physics, vol. 40, 2013.
|
4 |
W. Yang, S. Zhang, Y. Chen, Y. Chen, W. Li, and H. Lu, "Effective shape measures in malignant risk assessment for breast tumor on sonography," in Computer and Computational Sciences, 2008. IMSCCS'08. International Multisymposiums on, 2008, pp. 51-56.
|
5 |
H. Cho, L. Hadjiiski, B. Sahiner, H. P. Chan, C. Paramagul, M. Helvie, et al., "Interactive content-based image retrieval (CBIR) computezr-aided diagnosis (CADx) system for ultrasound breast masses using relevance feedback," in SPIE, Medical Imaging 2012, 2012, pp. 831509-831509-7.
|
6 |
J. Cui, B. Sahiner, H. P. Chan, A. Nees, C. Paramagul, L. M. Hadjiiski, et al., "A new automated method for the segmentation and characterization of breast masses on ultrasound images," Medical Physics, vol. 36, pp. 1553-1565, May 2009.
DOI
|
7 |
R. M. Haralick, K. Shanmugam, and I. Dinstein, "Texture features for image classification," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, pp. 610-621, 1973.
DOI
|
8 |
S.-H. Cha, "Comprehensive survey on distance/similarity measures between probability density functions," City, vol. 1, p. 1, 2007.
|
9 |
K. Belattar and S. Mostefai, "Similarity measures for Content-Based Dermoscopic Image Retrieval: A comparative study," in 2015 First International Conference on New Technologies of Information and Communication (NTIC), 2015, pp. 1-6.
|
10 |
N. Bouhmala, "How Good is the Euclidean Distance Metric for the Clustering Problem," in 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2016, pp. 312-315.
|
11 |
M. D. Malkauthekar, "Analysis of euclidean distance and Manhattan Distance measure in face recognition," in Computational Intelligence and Information Technology, 2013. CIIT 2013. Third International Conference on, 2013, pp. 503-507.
|
12 |
S. Viriyavisuthisakul, P. Sanguansat, P. Charnkeitkong, and C. Haruechaiyasak, "A comparison of similarity measures for online social media Thai text classification," in 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2015, pp. 1-6.
|