Md. Habibur Rahman

American International University Bangladesh

Presented By

Md. Habibur Rahman* and Md. Rafiqul Islam

Authors

ICIEV 13, 17-18 May, 2013, Dhaka, Bangladesh

Main idea of our paper

• The main idea is to propose a modified version of the watershed algorithm for image segmentation

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

• We have compared it with FCM, RG and HKM with respect to PSNR , MSE, PSNRRGB and CQM in 10 different kinds of images.

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Overview of Image Segmentation

Image Segmentation is the method of assigning a label to each pixel in an image

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

Fuzzy C-Means (FCM)

Partition a finite collection of pixels into a collection of "C" fuzzy clusters

Region Growing (RG)

Group of pixels with similar properties to form a region

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Overview of Image Segmentation (cont.)

Hill Climbing with K-Means (HKM)

This method detects local maxima of clusters in the global three-dimensional color histogram of an image

It associates the pixels of an image with the detected local maxima

Watershed (WS)

This method 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

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Modified Watershed Algorithm

It can quickly calculate the every region of the watershed segmentation

Image normalization has been done by Eq. 1

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Modified Watershed Algorithm (cont.) To determine the adaptive threshold 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

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Modified Watershed Algorithm (cont.)

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)

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Modified Watershed Algorithm (cont.) Convert three channels into a RGB image for

visualizing the labeled regions by Pn = label2rgb (Ln)

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

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Overview of evaluation metrics

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

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.

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

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

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Overview of evaluation metrics (cont.)

The image quality metrics like PSNR of each YUV color channel (Y, U and V) is calculated separately

Finally, CQM value is calculated using the Eq. 9

Where, weighted luminance quality measure and weighted color quality measure components

Cw and Rw means the weights on the human perception of these cone and rod sensors

Cw and Rw are 0.0551 and 0.9449 respectively

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Result Analysis

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Conclusion Our proposed MWS method ensures accuracy and

quality of the 10 different kinds of color images

Proposed modified watershed approach can enhance the image segmentation performance

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.

In future, we will focus on a more standard performance measure which could well reflect the difference between segmentation results 14

Questions ?

Thank You

for

Kind Attention

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