Weitere Kapitel dieses Buchs durch Wischen aufrufen
The advancement in medical technology has resulted in a huge number of medical images saved in a data-base. Content Based Medical Image Retrieval (CBMIR) mechanisms help the radiologist in retrieving the required medical images from an immense database. This paper envisages an effective content based procedure in which the region of the image is taken into account by determining the borders of the image region using gray level gradient method instead of considering the image as a whole. Later, the content within the boundary region of the image is described through the steerable filter in different orientations followed by extracting the second-order statistical components as feature vectors. Medical images correlated to the query image are retrieved by computing the Euclidean distance as a similarity measure between database images and the query image. To enhance the accuracy of the medical retrieval system, Instant Based Relevance Feedback has been used. In this procedure, the user interacts with the system and selects the most relevant image for searching again. The above search procedure is repeated for finding out more precise images by sorting out the first search and the second search similarity distances. Eventually, the corresponding top ranked images are displayed. These results reveal that the proposed algorithm outperforms by of increasing Recall Rate and reducing Rate of Error.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Müller H, Michoux N, Bandon D, and Geissbuhler A. A review of content-based image Retrieval systems in medical applications-clinical benefits and future directions. Medical Informatics. 1, 73 (2004).
L.A. Khoo, P. Taylor, and R.M. Given-Wilson, “Computer-Aided Detection in the United Kingdom National Breast Screening Programme Prospective Study,” Radiology, vol. 237, pp. 444–449, 2005.
Young Deok Chun, Nam Chul Kim, Ick Hoon Jang, Content-based image retrieval using multiresolution color and texture features, IEEE Transactions on Multimedia 10 (6) (2008) 1073–1084.
Sourav Samanta, SK. Saddam Ahmed, Mohammed Abdul-Megeed, M, Salem, Siddhartha Sankar Nath, Nilanjan Dey, and Sheli Sinha Chowdhury, “Haralick Features Based Automated Glaucoma Classification Using Back Propagation Neural Network.” Springer international publishing Switzerland 2015, vol. 1, Advances in intelligent system and computing, 327, DOI: 10.1007/978-3-319-11933-5-38.
Krit Somkantha, Nipon Theera-Umpon, “Boundary Detection in Medical Images Using Edge Following Algorithm Based on Intensity Gradient and Texture Gradient Features,” in Proc. IEEE transactions on biomedical engineering, vol. 58, no. 3, March 2011, pp. 567–573.
Dr. (Mrs) Ananthi Sheshasaayee, Jasmine. C, “Relevance Feedback Techniques Implemented in CBIR: Current Trends and Issues”, International Journal of Engineering Trends and Technology (IJETT), Volume 10 Number 4, Apr 2014.
Darshana Mistry, “Survey of Relevance Feedback methods in Content Based Image Retrieval”, Darshana Mistry/International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 1 No. 2, pp 32–40, ISSN: 2229-3345.
Miguel Arevalillo-Herráez, Juan Domingo, Francesc J. Ferri, Combining similarity measures in content-based image retrieval,” Pattern Recognition Letters 29 (2008) 2174–2181.
Ms. S. Veeralakshmi, Mrs. S. Vanitha Sivagami, Ms. V. Vimala Devi, Ms. R. Udhaya “Boundary Exposure Using Intensity and Texture Gradient Features. IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727, Volume 8, Issue 1 (Nov., Dec. 2012), pp. 28–33 www.iosrjournals.org.
Kriti, Jitendra Virmani, Nilanjan Dey, Vinod Kumar, PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification, Chapter, Applications of Intelligent Optimization in Biology and Medicine Volume 96 of the series Intelligent Systems Reference Library, pp. 159–180.
Soaya Cheriguene, Nabiha Azizi, Nawel Zemmal, Nilanjan Dey, Hayet Djellali, Nadir Farah, “Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms”, Medicine, Volume 96 of the series Intelligent Systems Reference Library, pp. 289–307.
I. El-Naga, Y. Yang, N.P. Galatsanos, R.M. Nishikawa, and M.N. Wernick, “A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography,” IEEE Trans. Medical Imaging, vol. 23, no. 10, pp. 1233–1244, Oct. 2004.
B. Jyothi, Y. Madhavee Latha, P.G. Krishna Mohan, Multidimensional Feature Vector Space for an Effective Content Based Medical Image Retrieval 5th IEEE International Advance Computing Conference (IACC-2015), BMS College of engineering Bangalore, June 12 to 13, 2015.
A.S. Syed navaz1, T. Dhevi Sri and Pratap Mazumder, Face Recognition using Principal Component Analysis and neural networks” International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC) ISSN 2250-1568 Vol. 3, Issue 1, Mar 2013, 245–256.
B. Jyothi, Y. MadhaveeLatha, P.G. Krishna Mohan, Steerable Texture Descriptor for Effective Content Based Medical Image Retrieval System Using PCA. 2nd International conference on Computer & Communication Technologies (IC3T-2015) published by proceedings of IC3T-2015, Springer-Advanced in Intelligent System and Computing Series 11156, vol 379, 380–381.
- An Improved Content Based Medical Image Retrieval System Using Integrated Steerable Texture Components and User Interactive Feedback Method
Y. Madhavee Latha
P. G. Krishna Mohan
- Springer India
Neuer Inhalt/© ITandMEDIA