Wavelet Based Image Segmentation
Updated Fri, 30 Dec 2016 05:42:27 +0000
This code is a part of our work "Nonseparable Wavelet Based Segmentation ..." . It contains the methods to extract out the darker or lighter blobs (spots) of various intensities and shapes (including faint/ low intensity spots) from noisy or inhomogeneous background. The method is designed for segmenting the protein blobs from 2D gel images. The other suitable images are quantum dot images, images of cirucular objects in noisy inhomogneous background, malaria parasite images, oil blobs on sea/river, fluroscence cell images similar to http://www.robots.ox.ac.uk/~vgg/research/counting/, dermoscopy images etc. The kernel-bandwidth and contrast threshold are two parameter that may need to change according to the image. For 2D gel images, you may vary only contrast threshold for your dataset although no change is required in any parameter in case of 2D gel images. The little modification in region refinement part according to an application may segment many other type of images such as some type of microarray images.
Ashutosh Kumar Upadhyay (2023). Wavelet Based Image Segmentation (https://www.mathworks.com/matlabcentral/fileexchange/48610-wavelet-based-image-segmentation), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
Inspired by: Toolbox Wavelets, Kmeans Clustering
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Link to (lite) kmeans has been modified, so link is updated in program and description.
only description on main page updated.
comments & supplementary file updated
The line 349 is slightly modified to work on newer version of MATLAB. Credit goes to Justin Du.
comments and discription corrected.
The extra results are added.
The code for separation of overlapping spots in case of 2d gel images is added.
Default parameter's values and comments updated for testing on blob images as well as natural images of public database
Default parameter 's values with comments updated to test with blob images as well as natural images in public database.
comments in the code are provided for using MATLAB inbuilt k-means implementation instead of 'litekmeans'.
comments in code are corrected.