Segmentation of color image using adaptive thresholding and masking with watershed algorithm

The individualization of an object from a digital image is a common problem in the field of image processing. We propose a modified version of the watershed algorithm for image segmentation. We have presented an adaptive masking and a threshoding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of the 10 different kinds of color images. The experimental results are obtained using image quality assessment (IQA) metrics such as PSNR, MSE, PSNRRGB and Color Image Quality Measure (CQM) based on reversible YUV color transformation. Consequently, the proposed modified watershed approach can enhance the image segmentation performance. Similarly, it is worth noticing that our proposed MWS approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than three other algorithms.

[1]  Gueesang Lee,et al.  Morphological gradient applied to new active contour model for color image segmentation , 2012, ICUIMC.

[2]  Boren Li,et al.  An improved segmentation of high spatial resolution remote sensing image using Marker-based Watershed Algorithm , 2012, 2012 20th International Conference on Geoinformatics.

[3]  Roberto Marcondes Cesar Junior,et al.  Interactive image segmentation by matching attributed relational graphs , 2012, Pattern Recognit..

[4]  R. Krishna,et al.  Image Segmentation and Region Growing Algorithm , 2012 .

[5]  P. Yugander,et al.  Colour Based Image Segmentation Using Fuzzy C-Means Clustering , 2011 .

[6]  Safaai Deris,et al.  A Novel Image Segmentation Enhancement Technique based on Active Contour and Topological Alignments , 2011, ArXiv.

[7]  Pradeep Kumar,et al.  A New Framework for Color Image Segmentation Using Watershed Algorithm , 2011 .

[8]  Lei Zhang,et al.  RFSIM: A feature based image quality assessment metric using Riesz transforms , 2010, 2010 IEEE International Conference on Image Processing.

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  V. Kavitha,et al.  Color Image Segmentation using ERKFCM , 2012 .

[11]  John Wright,et al.  Segmentation of multivariate mixed data via lossy coding and compression , 2007, Electronic Imaging.

[12]  Tianhe Xu,et al.  A Color Image Segmentation Algorithm by Integrating Watershed with Region Merging , 2012, RSKT.

[13]  Yildiray Yalman,et al.  A new color image quality measure based on YUV transformation and PSNR for human vision system , 2013 .

[14]  Allen Y. Yang,et al.  Unsupervised segmentation of natural images via lossy data compression , 2008, Comput. Vis. Image Underst..

[15]  Ju Zhang,et al.  Watershed segmentation algorithm based on gradient modification and region merging , 2011 .

[16]  Jobin Christ,et al.  MEDICAL IMAGE SEGMENTATION USING FUZZY C-MEANS CLUSTERING AND MARKER CONTROLLED WATERSHED ALGORITHM , 2012 .

[17]  Guang Deng,et al.  The Study of Improved Marker-Controlled Watershed Crown Segmentation Algorithm , 2011, 2011 Seventh International Conference on Computational Intelligence and Security.

[18]  Michael J. Gormish,et al.  Lossless and nearly lossless compression for high-quality images , 1997, Electronic Imaging.

[19]  Yili Fu,et al.  A fast two-step marker-controlled watershed image segmentation method , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[20]  John Wright,et al.  Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Krishnavir Singh,et al.  A Study Of Image Segmentation Algorithms For Different Types Of Images , 2012 .

[22]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[23]  V. Vaithiyanathan,et al.  Image Segmentation Based on , 2014 .

[24]  Rong Liu,et al.  Improved watershed algorithm for color image segmentation , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[25]  Song Jian-jun.  Color Image Segmentation Based on Marked-Watershed and Region-Merger , 2011 .