Sharma Aditya, Kaur Maninder, International Journal of Advance Research, Ideas and Innovations in Technology. © 2017, IJARIIT, All Rights Reserved Page | 75 ISSN: 2454-132X Impact factor: 4.295 (Volume3, Issue1) Available online at: www.ijariit.com Enhanced False Coloring in Medical Image Processing Aditya Sharma Computer Science Engineering/PTU aditya11dec@gmail.com Maninder Kaur Computer Science Engineering/GNDU maninderecediet@gmail.com Abstract— our study on false colouring encompasses its technological value in medical application. The extensive features of MATLAB have been utilized via its false colouring. The X-Ray medical images carry number of overlapping attributes which in one sight is not clearly like bones, ligaments, muscles, tissues and etc. Grey scale has number of similar variations and our eye can able to map things with corresponding colours. With use of false colouring images we can track such attributes and can able to plot the histogram and stats. Entropy and PSNR are taken as mandatory parameters which are in fact preliminary to evaluate the image’s processing level by level. Keywords—X-Ray scanned images converted to Gray scale. I. INTRODUCTION The introduction of Medical images has created breakthrough in medical field. Moreover, with the help of MATLAB things get more flexible and better of processing Medical images. Since it has been known that in X- Ray images only perceive grey shades can be perceived. Normally, our retina is subjected to 40 shades of grey colour. Also our eyes has easy read in detecting edges of dark hues like Violet, Indigo, Blue and Green. Organs and tissues within the body contain magnetic properties. MRI, or magnetic resonance imaging, combines a powerful magnet with radio waves (instead of X-rays) and a computer to manipulate these magnetic elements and create highly detailed images of structures in the body. Images are viewed as cross sections or “slices” of the body part being scanned. There is no radiation involved as with X-rays. MRI scans are frequently used to diagnose bone and joints’ problems. Demonstrating the false colouring algorithm and plotting histograms, equalized alike parameters and generating false colouring figures with applying median filters. When former steps of non-allocating paged information with clear command is performed then that image has to be read and converted to grey scale. Size is calculated by assigning values in three variables. Now the matrix is assigned with these variables and the traversing is done by inner looping. Correspondingly, the pixel’s range is examined and accordingly the assigning of grey shade is performed. Then it is followed by plotting of figures with equalized histogram is performed. Also entropy and PSNR is calculated for three medical images and the comparison with the base paper’s results is drawn.