The main objective of skull stripping is to efficiently remove the areas of non-brain tissues from MRI images. Some of the areas of the noncerebral tissue are the meninges, scalp, and skull. Recently, skull-stripping has gained the major attention of active research scholars. The reason behind this sudden growth of skull-stripping is the continuous demands of accurate techniques for MRI image processing. Consequently, it helps to find brain-related diseases for suggesting the immediate medical diagnosis.

This page is about to give more reliable project information on skull stripping using python for active research scholars and final year students!!!

Generally, MRI imaging/neuroimaging systems face challenges in skull-stripping. Since it acquires brain images along with non-brain tissues. So, it results in unresolved issues by subsequent analysis. This makes the increasing demand for advanced skull-stripping methods. As well, these methods are expected to guarantee efficient performance in all sorts of scanners. Here, we have given you some important operations for skull stripping. 

Major Tasks for Skull Stripping

• Mesh Fitting
• Histogram Modification
• Intensity Computations
• 3D Edge Detection
• Bias and Non-uniformity Correction
• Mathematical Operation
• Image Registration
• Geometric Processing
• Image Extraction / Filter
• Personalized Image Filter Construction
• 3D Surface Deformation
• Surface Point Identification
• Non-brain and Brain Tissues Classification
• Image Contrast Enhancement

Next, we have given you some important input images that are extensively used for skull stripping using python projects. Based on your project requirements, we suggest appropriate datasets for your project. Since all these images are different from each other. Further, we are ready to share another form of images that are highly used for skull-stripping.  

Different Skull Modalities / Images

• T2‐Weighted
• T1‐Weighted
• T2W with Fluid‐Attenuated Inversion Recovery
• Proton Density‐Weighted
• T1W with Contrast
• T1 Inversion Recovery

Although there are various techniques for skull-stripping, the T1-Weighted technique is widely used in many MRI brain-related applications. Since it is the best in analysing brain regions and removing skull portions. Further, it is also more useful in anatomy investigation and other cranial structural analyses. Overall, it enables you to successfully classify the healthy and non-healthy tissues. In the following, we have given some conventional but important skull-striping algorithms. 

Traditional skull stripping algorithms

• Hybrid Approaches
• Morphology-assisted Approaches
• Region Growing Approaches
• Meta-Heuristic Approaches
• Histogram Thresholding Approaches
• Atlas-based Approaches
• Deformable Surface Model-assisted Approaches

Current Skull Stripping Techniques

Further, our developers have given you the three most important skull-stripping techniques. As well, they are clustering techniques, region-based removal, and edge removal. In this, we have also included the advantages of each technique. Our developers are intelligent to handle both conventional and modern techniques. Intending to achieve the best result, we work on every project. Even if complexity occurs, we think smartly and develop new techniques/algorithms to solve issues. 

• Clustering Techniques (like K-Means)
  • It divides an image into “k” number of various regions
  • Advantage
    • Produce accurate clusters even in small datasets
• Region-based Removal
  • It compares the neighboring pixels for similarities and groups them accordingly
  • Advantages
    • Fast in mathematical computation     
    • Simple to learn and code
    • If the object and contextual information have high contrast, then it performance well
• Edge Detection and Removal
  • It identifies the object borders and so sharp modification using convolutions and filtering techniques
  • Advantages
    • Developer-friendly to apply in real-time application
    • Simple to detect smooth borders

So far, we have discussed traditional techniques and three primary techniques of skull stripping using python algorithms. Now, we can see about the latest skull-stripping techniques that are more apt for the advanced skull removing process. We assure you that all these algorithms are flexible to provide you with expected results in implementation. Also, it is simple to modify based on custom requirements. If you are new to these algorithms, then we guide you on right path of development. And, we help you to identify the best one for your project. 

Latest Skull Stripping Algorithms

• E-Net
• 3D-UNet
• V-Net
• U-Net
• Exfuse
• E-Net
• CNN with CRF
• ParseNet
• Dense-ASSP
• FC-DenseNet
• RefineNet
• FCN (VGG-16)
• DeepMask
• Pyramid Scene Parsing Network
• Path Aggregation Network
• Context Encoding Net
• Dilated Convolutional Net
• Global Convolutional Net
• Deconvolution Network
• Reature Pyramid Network
• Discriminative Feature Network

We hope that now you are clear with major tasks, input images, and techniques that are basic requirements of the skull-stripping project. Now, we can see different stages of skull stripping using python. For illustration, here we have taken the BRATS database as the input dataset. It comprises MRI images for network training to perform brain tumor segmentation. Likewise, we also support you in your project development by providing an implementation plan with basic system requirements. 

Processing Steps for Skull Stripping using Python

• Network Training
  • Mainly intended for Keras library
  • Enable to categorize center pixel by training 4 layer sequential model over MRI image (33×33 patches)
  • Each input is comprised with 4 channels where one channel is used for imaging sequence
  • In overall, the network can acquire the relative pixel intensities of every class
• Dataset Processing
  • Mainly intended for assessing techniques of brain tumor segmentation
  • Enable to process the input data from BRATS database
  • BRATS database is comprised with following images
    • T2 weighted fluid attenuated inversion recovery (FLAIR)
    • T1 weighted MRI with gadolinium contrast (T1c)
  • In overall, it is used to do comparative study among automated delineation techniques
• Sample Skull-Stripping Procedure
  • Collect the raw MRI input images as FLAIR / T1c / T1 / T2 in NIFTI or DICOM format
  • Preprocess the data of BRATS database
  • Pass over the Skull-stripped image from BRATS space to BRATS segmentor as input
  • Perform candidate dissections by tumor delineations from algorithmic database
  • Transfer the dissected candidates into BRATS for fused segmentation
  • Utilize consensus voting for fused segmentation

Next, we can see the significant python libraries used for removing the skull from MRI images. Every library supported in python has separate functionalities to perform. So, it is required to choose all necessary and suitable libraries for your skull stripping using python. Our developers have long-lasting practice in handling python libraries. So, we are adept to find the suitable one which minimizes your code work for your project. 

Python Libraries for Skull Stripping

• OpenCV
  • It is a python-based open-source library
  • It enables developers to implement different mechanisms over video / image for object detection
  • It is largely used in image processing, computer vision and machine learning fields
  • It supports languages such as Java, Python, C++, etc.
• Pixellib
  • It is a python-based scalable library
  • It enables developers to solve real-time issues by different stripping methods
• TensorFlow
  • It is a python-based open-source library introduced
  • It enables developers to implement various techniques like deep neural network
  • It is majorly preferred in medical image processing similar to disease detection, prediction and classification
  • For instance: medical organ stripping from medical images
• Sci_Kit Image
  • It is a python-based open-source library
  • It enables developers to implement advance methods for feature extraction, specific regions stripping, color space adjustments, filtering, geometric conversion, morphology, etc.
  • It is specifically used in image processing and computer vision
• Pillow
  • It is a python-based imaging library
  • It enables developers to implement diverse algorithms for image manipulation
  • It allows processing various image formats (PPM, TIFG, PNG, BMP, JPEG, GIF)

Further, there are more important python libraries are available for processing and storing images. For instances: 

• numpy is intended for image storing
• matplotlib library is intended for image visualization
• scipy is intended for image improvement and scikit-learn library is intended for learning techniques.

Similarly, mahots, OpenCV, and scikit-image are used for other image processing purposes. Below, we have given installation commands for some important python libraries. 

Installation Commands for Python Libraries

• To install scikit-image
  • pip3 install scikit-image
• To install pillow
  • pip3 install pillow
• To install tensorflow
  • pip3 install tensorflow
• To install pixellib
  • pip3 install pixellib
• To install opencv-python
  • pip3 install opencv-python

In addition, we have also given you some important python IDE for developing skull stripping projects. From our experience, we have found that the following IDEs provide a friendly environment for developers. These IDEs are enriched with Graphical User Interface (GUI) and Toolboxes. 

Our developers have developed several simple and complex applications in all these IDEs. If you need any add-on information on python IDE, then communicate with us. We suggest appropriate IDEs for your project based on your project goal. 

Which python IDE is best for Skull Stripping?

• Sublime
• Wing
• Python
• PyCharm
• Pydev
• Spyder
• Visual Studio Code
• AmazonCloud9

Last but not least, now we can see about the various research topics that gain more attention in skull stripping using python. All these topics are collected from innovative research areas of skull-stripping. More than these topics, we have wide recent topics repositories that comprise only futuristic research ideas. If you want to know novel research ideas from your desired research areas, then communicate with us. 

Latest Research Topics in Skull Stripping

• ANTs and FSL based Preprocessing pipeline using MRI Images
• Single and Multi-MRI Modalities for Brain Region Filtration  
• Employment of Deep Learning for Skull Stripping using Python
• Thresholding Method for Tumor Segmentation and Skull-stripping
• Skull-stripping and Brain Extraction over CT and MRI Brain Images
• Bias Field Correction, Enhancement, Registration Operations in MR images
• U-Net-based FLAIR Anomaly Stripping in MR Brain Images
• Skull Removal and Brain Volume Analysis over CT Brain image using MATLAB

We suggest you talk to our experts for a complete report on the analysis of the performance of all our successful skull removal projects. By providing benchmark resources for reference we will make your research work interesting and easier. Get in touch with us for any kind of assistance regarding your project that aimed to remove skull stripping using python. We ensure you that we provide you project running video, procedure, and screenshots at the time of project delivery.