ISSN E 2409-2770
ISSN P 2521-2419

Segmentation of Images with Inhomogeneous Intensity Multi-Objects


Rahman Ullah, Noor Badshah, Mati Ullah, Muhammad Arif


Vol. 8, Issue 03, PP. 112-117, March 2021

DOI

Keywords: Variational model, Fuzzy sets, Image segmentation, Intensity inhomogeneity

Download PDF


Computer vision is an influential area in which methodologies are generated to analyze and know about the charactristics and construction of a digital image and output is some meaningful information. Image processing comprises five main branches i.e image segmentation, image denoising, image registration, image inpainting and image deblurring. Image segmentation is our focus research work in context of fuzzy sets theory. The pivotal element to fuzzy sets [11] is fuzzy membership V, which acts like region descriptor, must satisfy the restriction Level set method (LSM) [9] is used, which is responsible to distribute and allogate the evolution curve C, which is a better way to carry out image segmentation process. In our research work we developed a model for segmenting images with inhomogeneous intensity multi objects background having maximum, minimum, average intensities. For such achievement we changed Krinidis and Chartiz [13] fitting term by linear term in fuzzy setup. Experimental result of our model justify that our model will show better performance in those images which are suffering from intensity inhomogeneity multi objects.


  1. Rahman Ullah, rahmanktk344@gmail.com, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.
  2. Noor Badshah, noor2knoor@gmail.com, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.
  3. Mati Ullah, , Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.
  4. Muhammad Arif, rfktk1@gmail.com, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan.

Rahman Ullah Noor Badshah Mati Ullah Muhammad Arif “Segmentation of Images with Inhomogeneous Intensity Multi-Objects” International Journal of Engineering Works Vol. 8 Issue 03 PP. 112-117 March 2021 https://doi.org/10.34259/ijew.21.803112117.


  1. S.Krinidis, V. Chatzis, Fuzzy energy-based active contours, IEEE Transactions on Image Processing 18, (2009) 73-87.
  2. M. James and J. Douglas Incorporating fuzzy membership functions into the perceptron algorithm , IEEE Transactions on Pattern Analysis and Machine Intelligence 6, (1985) 693-699.
  3. P. Adrian, M. Robert Fuzzy sets ,Information and control 8, (1965) 338-353.
  4. S. Patra, R. Gautam, A. Singla, A novel context sensitive multilevelthresholding for image segmentation, Appl. Soft Comput. 12, (2014) 2-127.
  5. Z.Ji, Q.Sun, Fuzzy c-means clustering withweighted image patch for image segmentation, Appl. Soft Computing 12, (2012) 1659-1667.
  6. D. Mumford, J. Shah Optimal approximation by piecewise smooth functions and associated variational problems , Pure Appl. Math 14, (1989) 577-685.
  7. F. Chan, L. Vese Active contours without edges,IEEE Trans. Image Process 2, (2001) 266-27
  8. Y. Wu, C. He A convex variational level set model for image segmentation, Signal Process 106, (2015) 123-133.
  9. S. Osher, J. Sethian Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulations, J. Comput. Phys 79, (1988) 12-49.
  10. N. Badshah, K. Chen, H.Ali, G. Murtaza A coefficient of variation based image selective segmentation model using active contours, East Asian J. Appl. Math 2, (2012) 150-169.
  11.  L.A. Zadeh Fuzzy sets, Inf. Control 8, (1965) 338-353.
  12. N.Badshah, Ali On segmentation of images having multi-regions using Guassian type radial basis kernel in fuzzy set framework, Applied Soft Computing 64, (2018) 480-496.
  13. S. Krinidis, V. Chatzis, Fuzzy energy based active contours, IEEE Trans. Image Process. 18(12), (2009), 62747–62755.
  14. H. Asad, Tranchage et prolongement des courants positifs fermes, Math. Ann. 507, (1991) 673-687.