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2020 | 30 | 2 | 96-103
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Optimizing Space Complexity using Color Spaces in CBIR Systems for Medical Diagnosis

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Content based Image Retrieval systems are now used in various automated systems because they largely produce accurate results as they do not depend on the metadata for telling what the image is but rather define it on the basis contents of the image like color, shape, texture and spatial locations. Content based Image retrieval systems have a repository of similar images and when a query image is presented to system it matches similar images in the database. This process aids in various applications like security checks to medical diagnosis. But all CBIR systems in common have to store the images which take a huge space. Here in this work, a unique approach is being devised to reduce the space complexity for a CBIR system used for detecting cervical cancer. When it comes to medical image it is not the question of how to reduce space, but along with it, the original contents of the image also has to be preserved.
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  • [1] B.V. Dhandra, R. Hegadi, M. Hangarge, V.S. Malemath. Analysis of Abnormality in Endoscopic images using Combined HSI Color Space and Watershed Segmentation. 18th International Conference on Pattern Recognition (ICPR'06). IEEE Xplore: 18 September 2006. DOI:10.1109/ICPR.2006.268
  • [2] Su Zhang, Wei Yang, Yi-Lun Wu, Rui Yao, Shi-Dan Cheng, Abnormal region detection in gastroscopic images by combining classifiers on neighboring patches. Machine Learning and Cybernetics 2009 International Conference on, vol. 4, pp. 2374-2379, 2009.
  • [3] Shuqiong Wu, Megumi Nakao, Tetsuya Matsuda, Automatic GrabCut based lung extraction from endoscopic images with an initial boundary. Signal Processing (ICSP) 2016 IEEE 13th International Conference on, pp. 1374-1378, 2016.
  • [4] Vasileios Charisis, Leontios J. Hadjileontiadis, Christos N. Liatsos, Christos C. Mavrogiannis, George D. Sergiadis, Abnormal pattern detection in Wireless Capsule Endoscopy images using nonlinear analysis in RGB color space. Engineering in Medicine and Biology Society (EMBC) 2010 Annual International Conference of the IEEE, pp. 3674-3677, 2010.
  • [5] Sareena, Ajay Mittal, Manvjeet Kaur, Computer-aided-diagnosis in colorectal cancer: A survey of state of the art techniques. Inventive Computation Technologies (ICICT) International Conference on, vol. 1, pp. 1-6, 2016.
  • [6] Corina Barbalata, Leonardo S. Mattos, Laryngeal Tumor Detection and Classification in Endoscopic Video. Biomedical and Health Informatics IEEE Journal of, vol. 20, no. 1, pp. 322-332, 2016
  • [7] Seung-Hwan Bae, Kuk-Jin Yoon, Polyp Detection via Imbalanced Learning and Discriminative Feature Learning. Medical Imaging IEEE Transactions on, vol. 34, no. 11, pp. 2379-2393, 2015.
  • [8] Masoud S. Nosrati, Rafeef Abugharbieh, Jean-Marc Peyrat, Julien Abinahed, Osama Al-Alao, Abdulla Al-Ansari, Ghassan Hamarneh, Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery. Medical Imaging IEEE Transactions on, vol. 35, no. 1, pp. 1-12, 2016.
  • [9] Ming Liu, Jue Jiang, Zenan Wang, Colonic Polyp Detection in Endoscopic Videos With Single Shot Detection Based Deep Convolutional Neural Network. Access IEEE, vol. 7, pp. 75058-75066, 2019.
  • [10] Yaqub Jonmohamadi, Yu Takeda, Fengbei Liu, Fumio Sasazawa, Gabriel Maicas, Ross Crawford, Jonathan Roberts, Ajay K. Pandey, Gustavo Carneiro, Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning. Access IEEE, vol. 8, pp. 51853-51861, 2020.
  • [11] J. Bernal, J. Sánchez, F. Vilariño. Towards automatic polyp detection with a polyp appearance model. Pattern Recognition, vol. 45(9), pp. 3166, 2012.
  • [12] Chia Hsiang Wu, Mei Yun Su, Specular Highlight Detection from Endoscopic Images for Shape Reconstruction. Applied Mechanics and Materials, vol. 870, pp. 357, 2017.
  • [13] Masoud S. Nosrati, Jean-Marc Peyrat, Julien Abinahed, Osama Al-Alao, Abdulla Al-Ansari, Rafeef Abugharbieh, Ghassan Hamarneh, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, vol. 8674, pp. 324, 2014.
  • [14] Shipra Suman, Nicolas Walter, Fawnizu Azmadi Hussin, Aamir Saeed Malik, Shiaw Hooi Ho, Khean Lee Goh, Ida Hilmi. Neural Information Processing, vol. 9489, pp. 373, 2015.
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