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Segmentation of Satellite and Medical Imagery using Homomorphic Filtering based Level Set Evolution


  • Department of Electronics and Communication Engineering, Acharya Nagarjuna University, Guntur – 522510, India
  • Kakatiya Institute of Technology and Science, Warangal – 506015, India


Objectives: The objective of this paper is to detection of the tissues and tumors, from medical images and oil spill regions and cloud regions from SAR Images respectively. Methods/Statistical Analysis: A novel region- based segmentation method for satellite and medical imagery using Homomorphic Filtering based Level Set Evolution (HLSE) approach. In real world images intensity inhomogeneity occurs, Segmentation of such images is a considerable challenge in image processing. Region based segmentation algorithms are widely used for intensity homogeneity of the Region of Interest (ROI). These images are still a tedious task and cumbersome due to weak contrast and poor resolution of images etc. The automatic segmentation of such images is very difficult. The main reason is a large amount of inhomogeneity present in the background and foreground of real world image. The conventional methods like C-V model and Distance Regularized Level Set (DRLS) method lead to getting improper segmentation with unconvinced results. Finding: We proposed an efficient segmentation method on satellite and medical using Homomorphic Filtering based Level Set Evolution (HLSE) approach. In the pre-processing step, we extract the illumination and reflectance components from the original image with the help of homomorphic decomposition process. Later, in the post- processing step, the illumination and reflectance images are applied to the level set model for accurate and robust segmentation. Improvements/Applications: The proposed segmentation results are effectiveness, superior and accurate compared to conventional methods. This new approach is very helpful for detection of the white matter and gray matter, cancerous cells in brain and bone in medical images. Similarly for SAR images detection of the oil slick, cloud regions etc.


Biomedical Images, K-means Clustering, Level Sets and Image Segmentation, SAR Images.

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  • Li C, Goldgof DB, Hall LO. Knowledge-based classification and tissue labeling of MR images of human brain. IEEE Transaction of Medical Imaging. 1993; 12:740–50.
  • Bauer S, Wiest R, Nolte LP, Reyes M. A survey of MRI-based medical image analysis for brain tumor studies. Physics in Mediine and Biology. 2013; 58:97–129.
  • Reddy GR, Ramudu K, Zaheeruddin S, Rameshwar Ra.Image segmentation using kernel fuzzy C-means clustering on level set method on noisy images. IEEE Conference, RAICS 2011, 978-1-4244-9799-7/11/$26.00 ©20 11 IEEE.P. 522–26
  • Chan T, Vese L. Active contours without edges. IEEE Transaction on Image Processing. 2001; 10(2):266–77
  • Caselles V, Kimmel R, Sapiro G. Geodesic active contours.In: Processing of IEEE International Conference on Computer Vision’95, Boston, MA; 1995.
  • Dervieux A, Thomasset F. A finite element method for the simulation of Rayleigh-Taylor instability. Lecture Notes in Mathematics. 1980; 771:145–158.
  • Mumford D, Shah J. Optimal approximation by piecewise smooth function and associated variational problems.Communication on Pure and Applied Mathematics. 1989; 42:577–685.
  • Osher S, Sethian J. Fronts propagating with curvaturedependent Speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics. Nov 1988; 79(1):12–49.
  • Zhongbin Li, Zhizhao Liu, Wenzhong Shi. A fast level set algorithm for building roof recognition from high spatial resolution from panchromatic images. IEEE Geoscience and Remote Sensing Letters. Apr 2014; 11(4):743–47.
  • Mekhmoukh A, Mokrani K. Improved fuzzy C-means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Computer Methods and Programs in Biomedicine. 2015; 122:266–81.
  • Li, Xu C, Gui C, Fox MD. Distance regularized level set evolution and its application to image segmentation. IEEE Transaction on Image Process. Dec. 2010; 19(12):3243– 3254.
  • Zhang K, Zhang L, Song H, Zhou W. Active contours with Selective local or global segmentation: A new formulation and level set method. Image and Vision Computing. Apr 2010; 28(4):668–76.
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