Hybrid Algorithm Edge Detected DICOM Image Enhancement and Analysis based on Genetic Algorithm for Evolution and Best Fit Value


  • Chetan S Dr. Ambedkar Institute of Technology
  • H S Sheshadri PES college of Engineering, Mandya, INDIA
  • V Lokesha Vijayanagara Sri Krinshnadevaraya University, Ballari, INDIA




DICOM Image, Ant Colony Optimization (ACO), Hybrid Ant Colony Optimization-Critical Path Methodology (ACO-CPM), Genetic Algorithm (GA), Image Enhancement


The segmentation of a DICOM standard medical image is a necessary technique which is essential for feature extraction, object edge detection and classification of the segments of the image. The DICOM image is partitioned based on the Hybrid ACO-CPM algorithm, based on the edges in the image, for analysis. The edges are seen as the boundaries within the image which differentiates different regions in the image. The factors that links to the boundary discontinuities that co-exists between the pixels of DICOM image, like texture, intensity and gradient are rendered redundant and are taken care with the application of the Hybrid ACO-CPM algorithm. DICOM image features correspond to that of meta-heuristic characteristics, which are considered during the application of Hybrid ACO-CPM algorithm. The results obtained from this non-deterministic behavior needs to be optimized over a large space called as the search space, wherein the lists of all possible solutions are provided. Each solution is to be marked as a value fit to be termed problematic and needs to be synthesized for an optimized solution. Among various techniques that provide solutions in obtaining an equitable optimization solution, Genetic Algorithms (GA) corroborates as one of the persuasive techniques in a large search space.
In this paper we propose an efficient and effective workflow based on a methodology, that provides an overview of the image enhancement and object classification for a DICOM image using Genetic Algorithm (GA). The edge detected medical standard DICOM image obtained from the Hybrid ACO-CPM algorithm is modified with respect to critical edge data. With the application of GA methodology, the process of enhancing the image ultimately suffices by rendering an image suitable for a specific application with an improved visual quality of the segmented image. A Figure-of-Merit is constructed to differentiate between the image metrics and their best fit values obtained for the images with respect to the Ant Colony Optimization (ACO) algorithm and proposed Hybrid ACO-CPM algorithm, upon enhancing the images using GA

Author Biographies

Chetan S, Dr. Ambedkar Institute of Technology

Assistant Professor

Department of Electronics and Communication

H S Sheshadri, PES college of Engineering, Mandya, INDIA

Professor and Dean

PES college of Engineering

V Lokesha, Vijayanagara Sri Krinshnadevaraya University, Ballari, INDIA

Special Officer and Associate Professor,

Department of Mathematics


Davis, L., Ed. (1987), Genetic Algorithms and Simulated Annealing, Pitman, London.

Goldberg, D .E. (1989), Genetic Algorithms: Search, Optimization and Machine Learning. Addison Wesley, Reading, MA.

Whitley D. A genetic algorithm tutorial. Statistics and computing. 1994 Jun 1;4(2):65-85.

Derrac J, García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical

tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation. 2011 Mar 31;1(1):3-18.

S. Chetan, H.S. Sheshadri, V. Lokesha, "A Hybrid Critical path methodology - ABCP (As Built Critical Path); its Implementation and Analysis with ACO for Medical Image Edge Detection", International Journal of Computer Science, Information technology and Control Engineering (IJCSITCE), Vol.2, No.1/2, April 2015, pp.27 - 42, ISSN: 2394-7527.

Dorigo M, Birattari M, Stutzle T. Ant colony optimization. IEEE computational intelligence magazine. 2006 Nov;1(4):28-39.

Ankerbrandt, C.A., B .P . Unckles and F.E. Petry (1990) Scene recognition using genetic algorithms with semantic nets, Pattern Recognition Left , 11, 285-293.

Siedlecki W. and I . Sklansky (1989), A note on genetic algorithms for large-scale feature selection. Pattern Recognition Lett. 10. 335-347.

Proceedings of the Fourth Internal. Conf. on Genetic Algorithms (1991), University of California, San Diego.

Pal SK, Bhandari D, Kundu MK. Genetic algorithms for optimal image enhancement. Pattern Recognition Letters. 1994 Mar 1;15(3):261-71.

Shivangini Shrivastava, Arvind Upadhyay, “Image Enhancement using Genetic Algorithm”, Internaltional Journal of Engineering Research & Technology (IJERT), Vol.3, Issue 5, May 2014, pp.1768-1772. ISSN: 2278-0181.

Palanikumar S, Sasikumar M, Rajeesh J. Entropy optimized palmprint enhancement using genetic algorithm and histogram equalization. International Journal of Genetic Engineering. 2012;2(2):12-8.

Hole KR, Gulhane VS, Shellokar ND. Application of genetic algorithm for image enhancement and segmentation. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). 2013 Apr 28;2(4):pp-1342.

Kaur A, Jindal G. Overview of tumor detection using genetic algorithm. International Journal Of Innovations In Engineering &Technology (IJIET) Vol. 2013 Apr;2.

Katkovnik V, Egiazarian K, Astola J. Local approximation techniques in signal and image processing. Bellingham: SPIE.




How to Cite

S, C., Sheshadri, H. S., & Lokesha, V. (2017). Hybrid Algorithm Edge Detected DICOM Image Enhancement and Analysis based on Genetic Algorithm for Evolution and Best Fit Value. Journal of Biomedical Engineering and Medical Imaging, 4(4), 01. https://doi.org/10.14738/jbemi.44.3412