Comparative Study for Different Color Spaces of Image Segmentation Based on Prewitt Edge Detection Technique


  • Ahmed N. Ismael Management Information Systems Department, College of Administrastion and Economics, University of Basrah.


Image Segmentation, Color Spaces, Prewitt Edge Detection, Structural Similarity Index Matrix


Image segmentation is one of the main step for images processing and analysis. It is the process of
recognizing objects in images and may consist of two related processes, recognition and delineation. Edge
detection removes and reduces the quantity of unnecessary data and information and give its significant
information. In digital images, there are several representations of colors which have its own
characteristics. In this paper, the Prewitt edge detection was applied as segmentation technique of color
images in four different color spaces. These color spaces are RGB, HSV, YCbCr and YIQ respectively.
Structural similarity index matrix (SSIM), entropy and elapsed time are used as measures to compare
between the segmented images of the four color spaces. Experimental results showed that several
differences among segmented images for spaces by Prewitt technique. The RGB images have batter
resulst for SSIM, entropy and elapsed time values when comparing with YCbCr, HSV, and YIQ spaces.
Analysis of the obtained results of the RGB, HSV, YCbCr and YIQ color spaces are given in this paper.


X. Wang, R. Hنnsch, L. Ma, and O. Hellwich, “Comparison of different color spaces for image

segmentation using graph-cut,” in International Conference on Computer Vision Theory and

Applications, 2014, vol. 1, pp. 301–308.

G. Hasting and A. Rubin, “Colour spaces-a review of historic and modern colour models,” African

Vision and Eye Health., vol. 71, no. 3, pp. 133–143, 2012.

C. Paper, J. Wu, Q. Shijie, and R. Zeng, “PCANet for Color Image Classification in Various Color

Spaces" International Conference on Cloud Computing and Security. Springer, Cham, June, 2017.

N. Dhanachandra and Y. J. Chanu, “A Survey on Image Segmentation Methods using Clustering

Techniques,” Asian Journal of Applied Science and Technology (AJAST), vol. 2, no. 1, pp.143-147, 2017.

M. Waseem Khan, “A Survey: Image Segmentation Techniques,” Asian Journal of Applied

Science and Technology (AJAST), vol. 3, no. 2, pp. 89–93, 2014.

[ [6] M. Raza, M. Sharif, M. Yasmin, S. Masood, and S. Mohsin, ” Research Journal of Applied

Sciences, Engineering and Technology., vol. 4, no. 18, pp. 3274–3282, 2012.

T.-F. Bronner, R. Boitard, M. T. Pourazad, P. Nasiopoulos, and T. Ebrahimi, “Evaluation of color

mapping algorithms in different color spaces,” International Society for Optics and Photonics,vol.3, pp.

-11, 2016.

G. G. Mary and M. S. Rani, “A Study on Secret Image Hiding in Diverse Color Spaces,”

International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 5,

pp. 779–783, 2016.

A. Trifan, A. J. R. Neves, and B. Cunha, “Evaluation of color spaces for user-supervised

color classification in robotic vision,” Rev. DO DATA, vol. 4, no. 2, pp. 1–6, 2004

K. Basha, P. Ganesan, V. Kalist, B. S. Sathish, and J. M. Mary, “Comparative Study of Skin Color

Detection and Segmentation in HSV and YCbCr Color Space,” Procedia Computer Science vol. 57, pp.

–48, 2015.

S. Sejpal, “Comparative Performance Analysis of Secured LWT- SVD Based Color Image

Watermarking Technique in YUV , YIQ and YCbCr Color Spaces,” International Journal of Computer

Applications,vol. 147, no. 7, pp. 34–40, 2016.

A. Semmo and D. Limberger, “Image Stylization by Oil Paint Filtering using Color Palettes Image

Stylization by Oil Paint Filtering using Color Palettes ,” J. Biomed. Heal. informatics, vol. 20, no. June,

pp. 615–623, 2015.

[١3] O. M. Can, Y. Ülgen, and A. Akın, “Use of the Color Spaces in Determining the Level of

Hemolysis in Blood Under Storage,” Springer . January, 2015.

H. Nejati, T. Do, and Y. Zhou, “Smartphone and Mobile Image Processing for Assisted Living,”

Signal Processing Magazine. `11July, 2016.

J. Zhu, T. Park, A. A. Efros, B. Ai, and U. C. Berkeley, “Unpaired Image-to-Image Translation

using Cycle-Consistent Adversarial Networks, International Conference on Computer Vision.” pp. 2223–


K. Sirinukunwattana et al., “Gland Segmentation in Colon Histology Images : The GlaS Challenge

Contest,” pp. 1–28, 2016.

G. Larsson, M. Maire, and G. Shakhnarovich, “Learning Representations for Automatic

Colorization,”Springer ,Vol 3 pp. 5–7,2017.

M. Keuper, B. Andres, and T. Brox, “Motion Trajectory Segmentation via Minimum Cost

Multicuts,” International Conference on Computer Vision. 3271–3279, 2015.

A. Horé, “Image quality metrics : PSNR vs . SSIM Image quality metrics : PSNR vs . SSIM,”

International Conference on Pattern Recognition. August, 2010