A Histogram-Based Heuristic for an Adaptive Active Contours Color Image Segmentation

Authors

  • Yamina Boutiche Research Center In Industrial Technologies
  • Abdelhamid Abdesselam Department of Computer Science, College of Science, Sultan Qaboos University
  • Naim Ramou
  • Nabil Chetih Research Center in Industrial Technologies CRTI
  • Mohammed Khorchef Research Center in Industrial Technologies CRTI

DOI:

https://doi.org/10.5566/ias.3039

Keywords:

Segmentation, Active contours, Color images, Color spaces, Adaptive weights

Abstract

The fidelity to data (external energy) term in energy-based segmentation of scalar (single channel) images requires setting scalar values defining the weights assigned to the inside and outside energy functional. These values are often determined empirically, which is a tedious and time consuming task. When it comes to color images (multi-channel), the weights become vectors, which further complicates the process of identifying the appropriate weights. In this work, a new interpretation of the weight vector is introduced. It is seen as representing the contribution of each channel in the energy functional, that is equivalent to search an optimum color space. We propose a heuristic formula for estimating the values of the weight vector. It is based on the ratio of the height to the width of the color components histograms. We have applied the proposed formulation to Piecewise Constant Vector Valued (PCVV) model of Chan and Vese in both biphase and multiphase frameworks. Results of the experiments demonstrate the advantages of the proposed model over the commonly used trial and error setting of weights and the model based on color spaces mixing.

References

Alpert S, Galun M, Basri R, Brandt A (2007). Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Proc. Cvpr. IEEE.

Boutiche Y, Abdesselam A (2017). Fast algorithm for hybrid region-based active contours optimisation. Iet Image Process 11:200–9.

Brox T, Rousson M, Deriche R, Weickert J (2010). Colour, texture, and motion in level set based segmentation and tracking. Image Vision Comput 28:376–90.

BurgerW, Burge MJ (2009). Digital Image Processing. Springer-Verlag London. Chan F, Lam F, Zhu H (1998). Adaptive thresholding by variational method. Ieee T Image Process 7:468–73.

Chan TF, Sandberg B, Vese LA (2000). Active contours without edges for vector-valued images. J Vis Commun Image R 11:130–41.

Dev S, Lee YH,Winkler S (2014). Systematic study of color spaces and components for the segmentation of sky/cloud images. In: IEEE Image Proc.

Garcˆıa-Lamont F, Cervantes J, Lopez-Chau A, Rodrˆıguez L (2018). Segmentation of images by color features: A survey. Neurocomputing 292:1– 27.

Garc´ıa-Ugarriza L, Saber E, Amuso V, Shaw M, Bhaskar R (2008). Automatic color image segmentation by dynamic region growth and multimodal merging of color and texture information. In: ICASSP conference. Gore JC, Ding Z, Li C, Kao C (2007). Implicit active contours driven by local binary fitting energy. In: Proc. Cvpr. Ieee.

Guo W, Zheng B, Duan T, Fukatsu T, Chapman S, Ninomiya S (2017). Easypcc: Benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions. Sensors 17.

Haug S, Ostermann J (2015). A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. In: Computer Vision - ECCV 2014 Workshops.

He L, Osher S (2007). A fast multiphase level set algorithm for solving the chan-vese model. Proc Appl Math 7:1041911–912.

Hern´andez-Hern´andez J, Garc´ıa-Mateos G, Gonz´alez- Esquiva J, Escarabajal-Henarejos D, Ruiz-Canales A, Molina-Mart´ınez J (2016). Optimal color space selection method for plant/soil segmentation in agriculture. Comput Electron Agr 122:124–132.

Hoogi A, Subramaniam A, Veerapaneni R, Rubin DL (2017). Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis. Ieee T Med Imaging 36:781– 91.

Hu Q, Tian J, He DJ (2017). Wheat leaf lesion color image segmentation with improved multichannel selection based on the chan–vese model. Comput Electron Agr 135:260–8.

Kass M, Witkin AP, Terzopoulos D (1988). Snakes: Active contour models. Int J Comput Vis 1:321– 31.

Lei Y, Weng G (2021). A robust hybrid active contour model based on pre-fitting bias field correction for fast image segmentation. Signal Process image Volume 97:116351.

Li C, Xu C, Gui C, Fox MD (2005). Level set evolution without re-initialization: A new variational formulation. In: Proc. Cvpr. Ieee, vol. 1. Washington, DC, USA: IEEE Computer Society.

Li C, Xu C, Gui C, Fox MD (2010). Distance regularized level set evolution and its application to image segmentation. Ieee T Image Process 19:3243–54.

Li X, Wang X, Dai Y (2018). Adaptive energy weight based active contour model for robust medical image segmentation. J Signal Process Sys 90:449– 65.

Moallem P, Mousavi BS, Monadjemi SA (2011). A novel fuzzy rule base system for pose independent faces detection. Appl Soft Comput 11:1801–10.

Mousavi BS, Fazlollah S, Razmjooy N (2013). Color image segmentation using neuro-fuzzy system in a novel optimized color space. Neural Comput Appl 23:1513–20.

Mumford D, Shah J (1989). Optimal approximation by piecewise smooth function and associated variational problems. Lect Notes Pure Appl 42:577–685.

Nakib A, Oulhadj H, Siarry P (2007). Image histogram thresholding based on multiobjective optimization. Signal Process 87:2516–34.

Ng HF (2006). Automatic thresholding for defect detection. Pattern Recogn Lett 27:1644–49. Nilsback ME, Zisserman A (2008). Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics Image Processing.

Song B, Chan T (2002). A fast algorithm for level set based optimization. CAM UCLA 68:02–68. Subudhi P, Mukhopadhyay S (2021). A statistical active contour model for interactive clutter image segmentation using graph cut optimization. Signal Process 184:108056.

Vandenbroucke N, Macaire L, Postaire JG (2003a). Color image segmentation by pixel classification in an adapted hybrid color space. application to soccer image analysis. Comput Vis Image Und 90:190–216.

Vandenbroucke N, Macaire L, Postaireb J (2003b). Color image segmentation by pixel classification in an adapted hybrid color space. application to soccer image analysis. Comput Vis Image Und 90:190–216.

Vese LA, Chan TF (2002). A multiphase level set framework for image segmentation using the mumford and shah model. Int J Comput Vision 50:271–93.

Wang J, Markert K, Everingham M (2009). Learning models for object recognition from natural language descriptions.

Wang P, Hu X, Li Y, Liu Q, Zhu X (2016). Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process 122:1–13.

Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P (2010). Caltech-UCSD Birds 200. Tech. Rep. CNS-TR-2010-001, California Institute of Technology.

Zhang X, Xiao P, Feng X, He G (2019). Another look on region merging procedure from seed region shift for high-resolution remote sensing image segmentation. Isprs J Photogramm 148:197–207.

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Published

2024-06-13

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Section

Original Research Paper

How to Cite

Boutiche, Y., Abdesselam, A., Ramou, N., Chetih, N., & Khorchef, M. (2024). A Histogram-Based Heuristic for an Adaptive Active Contours Color Image Segmentation. Image Analysis and Stereology, 43(2), 167-183. https://doi.org/10.5566/ias.3039