- Histogram Equalization: Use the "histeq" function to enhance contrast by spreading out the intensity values.
- Adaptive Histogram Equalization: Use the "adapthisteq" for localized contrast enhancemen.
preprocessing steps for ultrasound image for segmentation
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taking fetal ultrasound images for segmention, before segmentaion how to prapare the image for segmention?. what are the steps and techneques to normalize the image( deniose, resizing etc)
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Answers (1)
Harsh
on 6 May 2025
Hi @abid wazir
To prepare fetal ultrasound images for segmentation in MATLAB, it's essential to preprocess the images to enhance quality and consistency. You can use several techniques such as Denoising, Background removal and Intensity normalisation from the Medical Imaging Toolbox.
1. Denoising - Ultrasound images often contain speckle noise, which can hinder accurate segmentation. You can use the "specklefilt" function from the Medical Imaging Toolbox to reduce speckle noise.
2. Background removal - Isolating the region of interest (ROI) by removing irrelevant background can improve segmentation accuracy. Create a binary mask of the ROI and apply it to the image.
3. Intensity normalisation - Normalizing the intensity values of images helps in reducing variability due to different acquisition settings:
Refer to the following documentation for more more information regarding the above mentioned mentohds - https://www.mathworks.com/help/medical-imaging/ug/overview-medical-image-preprocessing.html#mw_4e5b2c6f-7c2e-4a3f-8a6d-9c1b2f3e5a6d
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