Brain Tumor Analysis Using Cuckoo Search Optimization – Matlab

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Description

Brain Tumor Analysis Using Cuckoo Search Optimization

 

A combination of airborne and satellite-based remote sensing is currently used for operational oil-spill monitoring worldwide. Spaceborne satellite-based synthetic aperture radar (SAR) images provide an overview of large ocean areas, and surveillance aircraft can be directed to check possible oil-spill locations to verify the spill and catch the polluter. Oil-spill detection is most effectively performed on a large scale using SAR images due to its all-weather capabilities (given wind speeds in the range of 2? 14 m/s) and good coverage.? In this paper by using a neural network the oil spill regions have been extracted in the radar image. Brain Tumor Analysis Using Cuckoo Search Optimization – Matlab


Brain Tumor Analysis Using Cuckoo Search Optimization

Existing Method

  • Principal Component Analysis
  • Local binary pattern and shape features
  • KNN and FNN classifier

Drawbacks of Existing method

  • High Computational load and poor discriminatory power.
  • LBP doesn’t differentiate the local texture region.
  • FNN is slow training for a large feature set.
  • Less accuracy in classification

Proposed Method

  • DRLBP and GLCM
  • Neural Network classifier

Methodologies

  • Color Space Conversion
  • GLCM Features Extraction
  • DRLBP (Discriminative Robust Local Binary Pattern)
  • NN Training and Classification
  • Fuzzy c-means clustering

Advantages

  • DRLBP has better discriminatory power
  • NN is fast and better compatible in classification.
  • Low computational complexity

Application

  • Surveillance aircraft
  • Oil-spill monitoring

Software Requirement

  • Matlab2014a and above versions

REFERENCES:

[1] R. Mahr and C. R. Chase, BOil spill detection technology for early warning spill prevention,[ in Proc. MTS/IEEE OCEANS Conf., 2009, pp. 1? 8.

[2] A. Dierks, V. L. Asper, R. Highsmith, M. Woolsey, S. Lohrenz, K. McLetchie, A. Gossett, M. Lowe, D. Joung, L. McKay, S. Joye, and A. Teske, BNIUSTVDeepwater horizon oil spill response cruise,[ in Proc. OCEANS, 2010, DOI: 10.1109/OCEANS.2010. 5664443

[3 D. Kim, W. Moon, and Y.-S. Kim, application of TerraSAR-X data for emergent oil-spill monitoring,[ IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2, pp. 852? 863, Feb. 2010.

[4] ] I. Keramitsoglou, C. Cartalis, and C. Kiranoudis, automatic identification of oil spills on satellite images,[ Environ. Model. Softw., vol. 21, no. 5, pp. 640? 652, 2006

[5] ] D. Casciello, T. Lacavat, N. Pergolat, and V. Tramutoli, BRobust satellite techniques (RST) for oil spill detection and monitoring,[ in Proc. Int. Workshop Anal. Multi-Temporal Remote Sens. Images, 2007, DOI: 10.1109/ MULTITEMP.2007.4293040

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