Title: Contrast enhancement of digital mammograms using a novel walking ant histogram equalisation
Authors: Abubacker Kaja Mohideen; Kuttiannan Thangavel
Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore – 641004, Tamil Nadu, India
Department of Computer Science, Periyar University, Salem-636011, Tamil Nadu, India
Abstract: Contrast enhancement of medical images could improve the quality of its visual contents for better diagnosis. In this paper, a novel walking ant histogram equalisation (WAHE) is proposed to improve the contrast of digital mammograms. The proposed method is hybridisation between the histogram equalisation with a novel ant colony optimisation (ACO) algorithm. Histogram equalisation is the concept of transferring input intensities to desired output intensities while improving the visual content of the images. Here we have used ACO for constructing the transfer functions. A fitness function is employed to control the transfer function construction in a balanced way. The experiments are carried out with the digital mammograms received from MIAS database, and results indicate the latent performance of the proposed contrast enhancement approach over the existing with finest results on all the contrast enhancement measures and the classification accuracy.
Keywords: digital mammograms; ant colony optimisation; ACO; histogram equalisation; contrast enhancement; computational intelligence; medical images; breast cancer; image enhancement.
Int. J. of Computational Vision and Robotics, 2015 Vol.5, No.2, pp.181 - 201
Available online: 19 Mar 2015