Abstract
With the advent of image analysis and computation in different domains, image segmentation has emerged as the most crucial step to achieve a compact segment-based description of image scene by decomposing it into meaningful segments of similar attributes. The pre-and-post filtering operation reduces the effect of noise from the segmented image. The Cameraman image is pre-filtered using Laplacian, Median and Min filter. The Split and Merge method for Region based image segmentation which guarantees to connected regions are now applied on the filtered image. The Median, Laplacian and Sobel filter is then used to post-filter the segmented image. The PSNR and MSE values are calculated to quantitative evaluation of segmented images. The quantitative evaluation of post-filtered segmented image shows that median filter produces most effective result with lowest MSE of 84.89 dB and highest PSNR of 5.72 dB.
Highlights
To analyze or interpret an image automatically, preprocessing is done which involves segmenting the image into different objects of interest e.g. separation of foreground from the background [6]
The Peak Signal-to-Noise Ratio (PSNR) and mean square error (MSE) values are calculated to quantitative evaluation of segmented images
The quantitative evaluation of post-filtered segmented image shows that median filter produces most effective result with lowest MSE of 84.89 dB and highest PSNR of 5.72 dB
Summary
To analyze or interpret an image automatically, preprocessing is done which involves segmenting the image into different objects of interest e.g. separation of foreground from the background [6]. Image segmentation has firmed its ground in many practical applications that involve a visual interpretation, namely in medical imaging, object detection Segmentation is the process of subdividing an image into its constituent regions or objects. A number of image segmentation algorithms with increased complexity have been developed over the years. All these algorithms work on the use of any of the three main criteria: the homogeneity within a segment, separation from adjacent segments and shape homogeneity. The segmentation algorithms can be grouped into three major categories on the basis of their segment formation properties, namely Threshold Based Segmentation [11, 22], Boundary based Segmentation [9, 16] and Region Based Segmentation [10]
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