Early Skin Tumor Detection from Microscopic Images through Image Processing

  • Ayesha Aamir Siddiqui Department of Telecommunication Engineering, Dawood University of Engineering & Technology, Karachi
  • Ghous Baksh Narejo
  • Areeba Mehmood Khan
  • Mashal Tariq

Abstract

The research is done to provide appropriate detection technique for skin tumor detection. The work is done by using the image processing toolbox of MATLAB. Skin tumors are unwanted skin growth with different causes and varying extent of malignant cells. It is a syndrome in which skin cells mislay the ability to divide and grow normally. Early detection of tumor is the most important factor affecting the endurance of a patient. Studying the pattern of the skin cells is the fundamental problem in medical image analysis. The study of skin tumor has been of great interest to the researchers. DIP (Digital Image Processing) allows the use of much more complex algorithms for image processing, and hence, can offer both more sophisticated performance at simple task, and the implementation of methods which would be impossibly by analog means. It allows much wider range of algorithms to be applied to the input data and can avoid problems such as build up of noise and signal distortion during processing. The study shows that few works has been done on cellular scale for the images of skin. This research allows few checks for the early detection of skin tumor using microscopic images after testing and observing various algorithms. After analytical evaluation the result has been observed that the proposed checks are time efficient techniques and appropriate for the tumor detection. The algorithm applied provides promising results in lesser time with accuracy. The GUI (Graphical User Interface) that is generated for the algorithm makes the system user friendly.

Published
Oct 1, 2017
How to Cite
SIDDIQUI, Ayesha Aamir et al. Early Skin Tumor Detection from Microscopic Images through Image Processing. Mehran University Research Journal of Engineering & Technology, [S.l.], v. 36, n. 4, p. 6, oct. 2017. ISSN 2413-7219. Available at: <http://publications.muet.edu.pk/index.php/muetrj/article/view/41>. Date accessed: 17 dec. 2017.