A version of watershed algorithm for color image segmentation

Md. Habibur Rahman (11-94853-2)

Master’s Thesis Presentation and Defense

Thesis Committee :

American International University-Bangladesh

June, 20131

Prof. Dr. Md. Rafiqul Islam (Advisor)Dr. Md. Saiful Azad (External)Dr. Dip Nandi (Head of Graduate Program)

Problem Definition

Thesis Contributions

Introduction

Proposed Watershed Algorithm

Image Quality Assessment (IQA) Metrics

Results Analysis

Conclusions

List of publication

References

Outline

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Over-segmentation problem in the existing watershed algorithm

Sensitive to noise

High computational complexity

Performance varies in different classes of images

Problem Definition

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An adaptive masking and a thresholding mechanism over each color channel before combining the segmentation from each channel into the final one

Overcome over-segmentation problem

Computationally inexpensive

Perform well in case of noisy image

Perform better with respect to five IQA metrics in 20 different classes of images

Thesis Contributions

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What is digital image?

Digital image processing

How image is stored?

Image Segmentation

Why Image Segmentation?

Color Image Segmentation Algorithms

Introduction

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What is a digital image?

• A numeric representation of a two-dimensional image as a finite set of digital values

• Pixel values usually represent intensity levels or gray levels, colors, heights, and opacities [11].

611. R C Gonzalez and R E Woods, Digital Image Processing, 3rd Edition, Pearson, pp. 51

An image can be defined as a two-dimensional function, p (x, y)

Where x and y are spatial (plane) coordinated

The amplitude of p at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point

Digital Image Processing

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How image is stored?

• In image, P (0, 0) represents the top left corner pixel

• P (X−1, 0) represents the bottom left corner pixel of the image

• In digital image, pixels contain color value and each pixel uses 8 bits or 1 Byte or 256 values [13]

813. H. Vankayalapati, "Evaluation of wavelet based linear subspace techniques for face recognition," Klagenfurt, 2008

It is a process to divide the digital image into homogeneous and different meaningful regions

The main goal of image segmentation is to cluster of pixels in the relevant regions

It is used to recognize similar regions and grouping the similar visual objects

Property like grey level, color, intensity, texture, shape, depth or motion from the digital image

Image Segmentation

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We do image segmentation to separate homogeneous area

It requires everywhere for precise segmentation if we want to analyze what inside the image.

It is separate objects and analyze each object individually to check what it is.

Why Image Segmentation?

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Fuzzy C-Means (FCM)

• Partition a finite collection of pixels into a collection of "C" fuzzy clusters [22]

Region Growing (RG)

• Group of pixels with similar properties to form a region

• For similarity measure, difference between a pixel's intensity value and the region's mean [23]

Image Segmentation Algorithms

1122. M. Singha and K. Hemachandran, "Color Image Segmentation for Satellite Images", IJCSE, vol. 3(12), 2011.23. M. Edman, "Segmentation Using a Region Growing Algorithm," Rensselaer Polytechnic Institute, 2007.

Hill Climbing with K-Means (HKM)

• detects local maxima of clusters in global three-dimensional color histogram of an image [28]

Watershed (WS)

• It comes from geography

• It is that of a topographic relief which is flooded by water

• Watershed lines being the divide lines of the domains of attraction of rain falling over the region [6]

Image Segmentation Algorithms

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28. R. Vijayanandh and G. Balakrishnan, "Hill climbing Segmentation with Fuzzy C-Means Based Human Skin Region Detection using Bayes Rule," EJSR, Vol. 76(1), pp. 95-107, 2012. 6. X. Han, Y. Fu and H. Zhang, "A Fast Two-Step Marker-Controlled Watershed Image Segmentation Method," Proceedings of ICMA, pp. 1375-1380, 2012

Proposed Watershed Algorithm

• It can quickly calculate the every region of the watershed segmentation

• Image normalization operation by Eq. 1

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Adaptive threshold determined by Eq. 2 and Eq. 3 based on Gray-threshold function

N-dimensional convolution for smoothing image

Adaptive masking operations by Eq. 4 and Eq. 5

Proposed Watershed Algorithm

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Impose Minima to create morphological process image using Nucleus-masking (M2) on three color channels

Apply Watershed algorithm (Wn) on three color channels

Pixel labeling calculated by Ln = BWLABEL (Wn)

Proposed Watershed Algorithm

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Convert three channels into a RGB image for visualizing labeled regions by Pn = label2rgb (Ln)

R, G and B color channels (Pn) are added to generate segmented image

Proposed Watershed Algorithm

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Applied canny edge detection method to detect enclosed region boundary and remove all small object from the combined three color channels

The enclosed region boundary is superimposed on original image in the final segmentation

Proposed Watershed Algorithm

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Peak Signal to Noise Ratio (PSNR) is calculated between two images by Eq. 6 [40].

Mean Square Error (MSE) is calculated pixel-by-pixel by adding up the squared difference of all the pixels and dividing by the total pixel count using the Eq. 7 [40].

Image Quality Measure (CQM) is based on color transformation from RGB to YUV.

Quality Evaluation Metrics

1840. C. Mythili and V. Kavitha, "Color Image Segmentation using ERKFCM," IJCA, Vol. 41(20), pp. 21-28, 2012

Reversible YUV Color Transformation (RCT) that is created from the JPEG2000 standard in Eq. 8

PSNR of each YUV color channel (Y, U and V) is calculated separately

CQM value is calculated using the Eq. 9 [43].

Riesz-transform based Feature Similarity Metric (RFSIM) is based on the human vision system (HVS) perceives an image mainly according to its low-level features

Quality Evaluation Metrics

1943. Y. YALMAN and Đ. ERTÜRK, "A new color image quality measure based on yuv transformation and psnr for human vision system,“ Turkish Journal of Electrical Engineering & Computer Sciences, 2013, in press.

Compute the similarity between two images f and g

M1 and M2 is the result of edge detection performed on f and g

Then, the feature mask is defined as Eq. 10.

Similarity between two feature maps fi (i = 1~5) and gi at the corresponding location (x, y) is defined as the Hilbert transform of a 1-D function in Eq. 11.

Quality Evaluation Metrics

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To define similarity between feature maps fi and gi

by considering only key locations marked by mask M and Hilbert transform of a 2-D function by Eq. 12

RFSIM index computes between f and g image as Eq. 13 [42]

RFSIM range between [0, 1), the higher RFSIM value indicates better image quality

Quality Evaluation Metrics

2142. L. Zhang, L. Zhang and X. Mou, "RFSIM: A Feature based image quality assessment metric using Riesz-Transforms," Image Processing (ICIP), 17th IEEE International Conference, pp. 321-324, 2010.

Visual Verification

• Comparative performance of the proposed MWS method with four modified watershed methods

• Compared the results of the proposed algorithm with three image segmentation algorithms

Quantitative Verification

• Color image segmentation results with 20 different classes of images

• Performance of proposed method with three different algorithms with respect to 5 IQA metrics

Results Analysis

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33. C. Zhang, S. Zhang, J. Wu, S. Han, "An improved watershed algorithm for color image segmentation,“ I CCSEE, pp. 69-72, 2012. 35. S. Li, J. Xu, J. Ren and T. Xu, "A Color Image Segmentation Algorithm by Integrating Watershed with Region Merging," RSKT, LNAI 7414, pp. 167–173, 2012.

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7. H. Tan, Z. Hou, X. Li, R. Liu and W. Guo, "Improved watershed algorithm for color image segmentation," Proc. of SPIE Vol. 7495 74952Z-(1-8). 9. L. Gao, S. Yang, J. Xia, S. Wang, J. Liang and Y. Qin, "New Marker-Based Watershed Algorithm," TENCON 2006.

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A novel image segmentation method based on adaptive threshold and masking operation with watershed algorithm

Compared the proposed MWS algorithm with four modified watershed algorithms

The results achieved using my technique ensure accuracy and quality of the image in 20 different classes of images in four segmentation algorithms

Proposed method is less computational complexity, which makes it appropriate for real-time application

In future I am going to develop a robust algorithm for the segmentation of color and video images

Conclusions

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1) "Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm," Presented at 2nd International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka University, Bangladesh, ISBN: 978-1-4799-0399-3, May 2013 (To appear in IEEE Xplore).

2) "A version of watershed algorithm for color image segmentation," AIUB Journal of Science and Engineering (AJSE), Bangladesh, Vol. 12(1), 2013 (accepted).

List of Publication related to this thesis

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[6] X. Han, Y. Fu and H. Zhang, "A Fast Two-Step Marker-Controlled Watershed Image Segmentation Method," Proceedings of ICMA, pp. 1375-1380, 2012.

[7] H. Tan, Z. Hou, X. Li, R. Liu and W. Guo, "Improved watershed algorithm for color image segmentation," Proc. of SPIE Vol. 7495 74952Z-(1-8).

[9] L. Gao, S. Yang, J. Xia, S. Wang, J. Liang, and Y. Qin, "New Marker-Based Watershed Algorithm," TENCON 2006.

[11] R. Gonzalez and R. Woods, “Digital Image Processing,” 3rd edition, Pearson Prentice Hall, 2007.

[13] H. Vankayalapati, "Evaluation of wavelet based linear subspace techniques for face recognition," Alpen-Adria University and Institute for Smart System-Technologies, Klagenfurt, 2008.

[22] M. Singha and K. Hemachandran, "Color Image Segmentation for Satellite Images", IJCSE, vol. 3(12), 2011.

[23] M. Edman, "Segmentation Using a Region Growing Algorithm," Rensselaer Polytechnic Institute, 2007.

[28] R. Vijayanandh and G. Balakrishnan, "Hill climbing Segmentation with Fuzzy C-Means Based Human Skin Region Detection using Bayes Rule," EJSR, Vol. 76(1), pp. 95-107, 2012.

[33] C. Zhang, S. Zhang, J. Wu, S. Han, "An improved watershed algorithm for color image segmentation," International Conference on Computer Science and Electronics Engineering (ICCSEE), pp. 69-72, 2012.

[35] S. Li, J. Xu, J. Ren, and T. Xu, "A Color Image Segmentation Algorithm by Integrating Watershed with Region Merging," RSKT, LNAI 7414, pp. 167–173, 2012.

[40] C. Mythili and V. Kavitha, "Color Image Segmentation using ERKFCM," International Journal of Computer Applications (IJCA), Vol. 41(20), pp. 21-28, 2012.

[42] L. Zhang, L. Zhang, and X. Mou, "RFSIM: A Feature based image quality assessment metric using Riesz-Transforms," Image Processing (ICIP), 17th IEEE International Conference, pp. 321-324, 2010.

[43] Y. YALMAN and Đ. ERTÜRK, "A new color image quality measure based on yuv transformation and psnr for human vision system," Turkish Journal of Electrical Engineering & Computer Sciences, 2013, in press.

Some Important References

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Thank you

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