Skull stripping is nothing but remove skull in MRI Images (Magnetic Resonance). Generally, the high-resolution MRI brain images comprise brain image along with the tissues of non-brain namely, fat, muscle, eyeballs, neck, and skin. In other related images like fMRI (Functional Magnetic Resonance Imaging), PET (Positron Emission Tomography), and SPECT (Single Photon Emission Computed Tomography), the presence of non-brain issues are at a minimum level
This article is subjected to a study of challenges, steps, techniques to Remove Skull from MRI Images along with recent research areas and performance metrics!!!
In comparison with other imaging techniques, MRI is the commonly used technique in the medical domain. Since, it can work even without ionizing radiation (x-rays) and also poses capabilities of non-destructive, flexible, and non-invasive. It mainly focuses on externally invisible soft tissues for the complete anatomy of human soft tissues. Due to its high spatial resolution, it is extremely used for pathological investigation over anatomical structure.
Although the MRI imaging technique has more advantages than general techniques likes fMRI, PET, and SPECT, it has some real-world implication/challenges while processing MRI brain images. Here, we have given you only a few challenges to make you familiar with the recent research interest of active research scholars who particularly focus on MRI images for skull removal. Moreover, we also have appropriate solutions for all these challenges. Further, we also support you to solve other challenges through best-fitting problem-solving methods/ algorithms.
Important challenges in MRI Brain Images
- Need Domain-intensiveness for preprocessing
- As mentioned earlier, MRI images have more non-brain tissue information. So, its need the best preprocessing techniques
- Also, more technical skills are required to preprocess the input image prior to feeding an image into model
- Real-time data Issues
- In jupyter notebook, it is easy to construct model in best accuracy
- When the same model is implemented in real-world, it may get poor performance because of data drift
- Since, the data trained in model are varied with data used in the real-world
- In the case of MRI images, techniques / metrics used in MRI image generation may vary
- Insufficient huge datasets (For instance: Imagenet)
- Require expert’s guidance to generate precise datasets. If tried without guidance then it will take more time
- To construct deep model for skull stripping, only small-scale datasets are accessible
Overview of MRI Images for Skull Removal
Now, we can see the summary of remove skull from MRI Images. Generally, the MRI system generates 3D volumetric brain images which are expressed as 2D slices stack. As well, it uses a different computer-assisted tool to study the anatomy of brain structure, slices, and tissues. Further, it is also used in other different MRI brain image applications. Here, we have mentioned to you the main tasks involved in the skull-stripping process of MRI images.
- Surgical Preparation
- 3D Brain Visualization
- Computer-assisted Surgery
- Analysis of Volumetric Data
- Pathology Localization
- Anatomical Models Development
- Diagnosis Plan for Medical Treatment
- Anatomical Structure Investigation
Skull stripping and preprocessing of brain images is necessary to enhance the accuracy of brain regions segmentation. Ultimately, it gives precise results in brain disease identification, disease severity identification, disease diagnosis, etc. Most importantly, skull-stripping is the first step to removing the non-brain tissues from MRI images by extracting skull parts. It helps to speed up the image analysis and segmentation processes with high accuracy. For your information, here we have given you a list of MRI brain images for skull-stripping.
Input Brain MRI Images for Skull Removal
- T1-weighted images
- 3D T1-weighted images
- 3D sagittal brain images
- T1-weighted coronal oriented images
- PD and 3D T2-weighted images
- PD, T1, and T2-weighted images
- T1 and T2-weighted images
- Coronal T1-weighted images
- T1-weighted sagittal oriented images
- T1, T2, T2-flair, T1-contrast and CT images
- T1-weighted sagittal and coronal brain images
- T2 / PD-weighted axially obtained multi-spectral images
To share with you the procedure of skull-stripping over MRI images, here we have given you the simple workflow. We hope that this workflow will help you to understand the general development steps of non-brain tissues elimination. Further, this workflow will vary from project to project based on research objectives.
Our developers are best to assist you in every phase of your code development ranges from project topic selection to performance analysis. Moreover, we also provide an implementation plan for your confirmed project topic before beginning the development. If you require acceptable technical modification, then we consider doing changes to plan accordingly.
Simple Flow for Skull Removal from MRI images
- Step 1 – Get the MRI brain image as input
- Step 2 – Implement the median filtering technique
- Step 3 – Estimate the threshold T value
- Step 4 – Perform binarization based on T value
- Step 5 – Opening morphology
- Step 6 – Identify the largest linked component
- Step 7 – Closing morphology
- Step 8 – Remove the skull from MR brain image
In the earlier section, we have already seen the significant challenges of MRI brain images. Now, we can see the research challenges related to remove skull from MRI images. This explicitly points out the issues over techniques of skull-stripping. We are smart not only in recognizing the latest research challenges but also best in providing suitable techniques and algorithms. If you are excited to know the research solutions for these challenges, then connect with our team. Further, we also design our algorithms/techniques for complicated research challenges.
Remove Skull from MRI Images Research Topics
- Noise due to motion artifacts. For instance: muscles, blood vessels, etc.
- Border are not sharply accurately marked in intensity-based borders
- Overlap of signal intensities between varied brain structures. For instance: brain and non-brain tissues have same intensities
- Heterogeneity of brain structures which differs with individuals
- Presence of echos over edges of brain image (tissue / air borders)
- Different medical machine uses various imaging parameters. So, the generated brain images have different scan quality and contrast for same tissue type
- Existence of imaging noise and artifacts because of poor sensors which may minimize the brain image quality and maximize challenges over skull-stripping
- Intensity variation is caused due to the partial volume impact among tissue classes
One more notable research challenge in MRI-based skull removal is MRI images with gross deformities (glioblastoma). In this, the conventional techniques are not effective due to poor lesion separation in the skull border. Further, it also needs feature detection for shaping deformities. To solve all these issues reliable and accurate techniques are required to develop. Full-automated skull removal techniques enable you to filter the necessary diagnostic details.
Overall, these techniques are essential to assuring the accuracy, fault tolerance, speed, and reliability for the prediction and diagnosis of medical disorders related to the brain. Although several improvements are ongoing, every skull stripping technique has its own merits and demerits. For your knowledge, here we have given you the top 5 skull stripping techniques that are majorly used in many current MRI brain-related applications.
Top 4 Skull Stripping Methods
- Atlas-assisted Techniques
- Hybrid Techniques
- Intensity-assisted Techniques
- Deformable Surface-assisted Techniques
- Mathematical Morphology-assisted Techniques
Other Important Approaches for Skull Removal
- 2D / 3D Convolution Approaches
- Histogram Thresholding Approach
- Region Growing Approach
- Meta-Algorithms
- Watershed Approach
Before processing the image, it is necessary to remove skull from MRI images. Specifically, there are so many research areas are available in this field/process. Some of the main research perspectives of skull-stripping are given in the below list. All these areas are collected from the latest research demands of the current research scholars. We support you not only on these techniques but also on other newly evolving research ranges of skull-stripping. If you are already interested in some research areas/ideas, then share them with us. We are here to help you to enhance your thought towards the peak of advancement.
Research Ideas in Skull Removal
- Cortical Structure Investigation
- Face Emotion Recognition
- Cortical Wideness Approximation
- 3D Measurement of Brain Volume
- Inhomogeneity Analysis and Correction
- Image Registration and Analysis
- Brain Cortical Surface Restoration
- MR-based Multiple Sclerosis Investigation
- Image Warping and Restoration
- Computation of Brain Aging
- Brain Region Detection and Segmentation
- Brain and Non-Brain Tissue Classification
- Schizophrenia and Alzheimer’s Disease Recognition
Next, we can see the different datasets that are extensively used to remove skull from MRI images. The dataset plays a major role in developing data-intensive projects like skull-stripping. So, it is required to take more attention to choosing the suitable dataset for your project. Our developers have developed several MRI brain-related medical applications in different datasets. So, we are capable to identify the optimal one for your project by examining your project goal. Here, we have one sample dataset which is more important for skull-stripping projects.
Datasets for Remove Skull
Skull dataset
This dataset is composed of clinical and sub-clinical psychiatric symptoms. These data are collected from 125 participants in the age group of 21-45 years. As well, these images have a resolution of about 1 mm3 where the files are in NiFTI format. Further, all the participants have the following information in their respective repositories.
- Brain mask
- It is the brain image mask which is also termed as ground-truth
- It uses BEAST approach to acquire brain mask
- BEAST – Brain Extraction based on Nonlocal Segmentation
- It also requires experts guidance to eliminate non-brain tissues
- Skull-stripped image
- It is a subset of T1 weighted image
- It creates covering masks over real images
- Structural T1-weighted anonymized (de-faced) image
- It is the collection of raw T1 weighted images (MRI) along with one channel
In addition, our developers have also given you about performance metrics. On using the performance metrics only, one can measure the efficiency of used methodologies in a developed system. Moreover, it also enables to enhance the performance of the system is lacking aspects like accuracy, image quality, etc. by doing small adjustments over the handpicked metrics.
If you are interested to know the performance metrics of your selected project, then reach us. Our developers will guide you appropriately in quantitative analysis based on project requirements. Here, we have given you vital metrics for quantitative analysis for your reference.
Performance Metrics for Skull Removal
- Mean & Standard Deviation
- Specificity
- Dice Coefficient (DC)
- Jaccard Coefficient
- Sensitivity
- Absolute Volume Difference (AVD)
- Hausdorff Distance (HD)
To sum up, our resource team will support you in developing MRI-based skull removal projects in best-fitting development tools and technologies. More precisely, we also give individual care on
- Research problem selection
- Apt solutions selection
- Development framework selection
- Programming language selection
- Dataset selection
- Performance metrics selection
Overall, our guided projects surely yield accurate expected results in all aspects. So, create a bond with us to create a masterpiece of your research remove skull from MRI Images Projects. Further, we provide our assistance for medical image processing thesis plus final year students too.
