Post-Processing Medical Images: N4 Bias Correction and Skull Stripping
during my internship in Biomedical image processing , i explored many of different techniques Per-Processing data , because data collection was the first part was done at the Lab university , but data has many issues before using any type of model deep learning. in this article we will explain two main method widely used in neuroscience community to diagnosis Images.
Medical machine diagnosis such MRI and CT scan are famous types of machine used to scan patients to obtain the produce image of anatomy human , we will not dive to how these machine works at this article more then issues comes with produced images come from them , and are
- intensity inhomogeneities
- ROI ( region of interest )
Introduction :
Post-processing is an important step in medical image analysis that involves refining and enhancing the images to improve their quality and reliability for further analysis. There are various techniques that can be used for post-processing, including image enhancement, image registration, image segmentation, and image visualization. In this article, we will focus on two specific post-processing techniques: N4 bias correction and skull stripping.
N4 Bias Correction
N4 bias correction is a method for removing intensity inhomogeneities, or “bias fields,” from medical images. These bias fields can be caused by various factors, such as differences in the scanner parameters or patient anatomy, and they can significantly affect the accuracy and reliability of image analysis results. By correcting for bias fields, we can improve the image quality and reduce the influence of confounding factors on the analysis.
N4 bias correction works by estimating the bias field using a non-parametric, non-uniform intensity correction algorithm. The algorithm estimates the intensity distribution of the image and applies a correction factor to reduce the intensity inhomogeneities. The correction factor is based on the intensity histogram of the image, and it is applied using a convolution operation.
N4 bias correction is widely used in medical image analysis and is particularly useful for correcting images with low contrast or low signal-to-noise ratio. It is a fast and effective method for improving the image quality, and it can be implemented using software tools such as ANTs (Advanced Normalization Tools) or ITK-SNAP (Insight Segmentation and Registration Toolkit).
This code example demonstrates how to use the N4BiasFieldCorrection interface from the FSL package to apply N4 bias correction to a medical image in Python.
Skull Stripping
Skull stripping is a process for removing the non-brain tissue from brain images, such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. The purpose of skull stripping is to isolate the brain tissue and eliminate the confounding effects of non-brain structures,as the skull, scalp, or dura mater. This can be useful for tasks such as brain segmentation, brain volume measurement, or brain activation analysis.
There are various techniques that can be used for skull stripping, including thresholding, machine learning, and atlas-based methods. Thresholding involves setting a intensity threshold to differentiate brain tissue from non-brain tissue, based on the intensity distribution of the image. Machine learning methods use algorithms such as neural networks or decision trees to learn the pattern of brain tissue from a training dataset, and then apply the learned pattern to the test image. Atlas-based methods use a predefined brain atlas to guide the skull stripping process, by warping the atlas to the test image and using the atlas as a reference.
Skull stripping is a crucial step in many medical image analysis pipelines, and it is important to choose an appropriate method that can accurately and reliably remove the non-brain tissue. Some methods may be more suitable for certain types of images or tasks, and it is important to evaluate the performance of different methods to determine the best approach.
Here is an example of how to apply skull stripping to a brain MRI using the FSL package in Python:
Conclusion
in this article i showed two different methods help processing data , i combined this techniques to create an automated tool to improve the Data BRAST2020 tumor tissue here link
at end , N4 bias correction and skull stripping are important post-processing techniques that can improve the quality and reliability of medical images for further analysis. N4 bias correction is a method for correcting intensity inhomogeneities, or bias fields, in images, and it is widely used in medical image analysis. Skull stripping is a process for removing the non-brain tissue from brain images, and it is useful for tasks such as brain segmentation or brain volume measurement. Both techniques are implemented in various software tools and can be easily applied to medical images using Python.
Post-processing is a crucial step in medical image analysis, and it is important to choose appropriate techniques and tools that can effectively improve the image quality and reliability. By understanding the different post-processing techniques and their applications, we can better analyze and interpret medical images and derive more accurate and meaningful results.
References
- N4BiasFieldCorrection: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/N4BiasFieldCorrection
- ANTs: https://github.com/ANTsX/ANTs
- ITK-SNAP: http://www.itksnap.org/
- Thresholding: https://en.wikipedia.org/wiki/Thresholding_(image_processing)
- Machine learning: https://en.wikipedia.org/wiki/Machine_learning
- Atlas-based methods: https://en.wikipedia.org/wiki/Atlas_(anatomy)
