Generally, the skull stripping is a preprocessing step for brain tissue analysis or disease detection through the removal of the skull and it handles and identifies the abnormalities of the human brain. The acquisition of brain image is a challenging one actually because it is subject to numerous nerves and tissues.

Skull stripping needs automated algorithms execution in their process to attain the reliability and exact results on the massive MRI scans. Background noise in the MRI scans leads to an ineffective system. Thus skull stripping algorithm Matlab helps the system to obtain the predicted results.

We have initiated the article with the basic comparison between the MRI and CT scans to make you understand. MRI and CT scans are used to picture the human brain. They are otherwise known as image techniques. MRI stands for the Magnetic Resonance Image. CT stands for Computed Tomography. Now we can have the comparison of the CT and MRI in the immediate passage.

Are you looking for an article regarding the skull stripping algorithm Matlab? Then this article is exclusively presented to you guys!!!!

MRI vs. CT for Skull Stripping

• MRI Pros
  • Good Visualizations
  • Soft Tissue Contrast
  • Radiance-free
• CT Pros
  • Budget Friendly System
  • Fast Data Provision
  • Extended Accessibility

Stroke diseases are diagnosed by CT and MRI scans. It is very easy to detect the stroke with the help of image processing instead of radiologists’ opinions. These techniques help to attain the accuracy levels when diagnosing and eliminate the possibility of human wrong perceptions. MRI scans are highly proficient with the exactness equated to the CT scans. MRI scans don’t compatible with emergency cases whereas CT scans are highly demanded in emergency cases.

Our technical team has listed the fundamental aspects of the skull tripping for the ease of your understanding. We hope this article can help you to do your projects in Matlab based skull stripping. Let us have the next section regarding the fundamental key aspects of skull stripping.

Research Ideas of Skull Stripping

• Skull stripping segments the tissues from the non-tissues
• MRI and CT scan analysis & registering

The above listed are the essential fundamental process of skull stripping in general. Apart from this brain aging disorders, strokes, Alzheimer’s disease, and mental issues are segregated by the skull stripping.

In every technology, some merits and demerits are pampered. As well in the skull stripping, some challenges come into existence when it is subject to research. Yes, you guessed right! The next phase is all about the challenges consisting of the skull stripping.

What are the research challenges of Skull Stripping?

• Variance in the MRI scans leads to the inaccurate results (noise, contrast & quality)
• Voxel intensity imitations on non –brain based tissues
• Lack of solitary segmentation of the brain tissue extractions
• High time consumption on the manual mask formations
• Variations in the morphology and its directions (geriatric & pediatric)

The aforementioned are some of the challenges that occurred in brain extraction. However, we can overcome these constraints by implementing the skull stripping algorithm Matlab. Doing projects in this area would yield you the best results. If you are a beginner in this area, you can probably avail yourself of our assistance in the relevant project and research approaches. Next, we have given you the top 3 skull stripping methods so far used by the top engineers and experts of the industry.

Top 3 Skull Stripping Methods

• Deformable Model Based Methods
  • Application of active contour deformation
  • Localization of the brain by image processing
• Intensity Based Methods
  • Thresholding oriented classification
  • Intensity vacillations
• Morphology Based Methods
  • Combination of edge detections, morphological functions & thresholding
  • Segregation of tissues from non-tissues

The foregoing passage conveyed to you the top 3 methods used in skull stripping in general. These methods are vitally performing their roles in the human brain analysis. For your better understanding, we have demonstrated to you one of the above-mentioned methods with bolt and nut points. That is nothing but morphology-based methods. It is lies under the 5 steps.

Skull Stripping Algorithm Steps

• Input = Input MRI image
  • Thresholding of image
  • Objects gaps identification by operators
  • Edge detecting & edge improvements
  • Binary mask creation
• Output = Binary Mask

These are the 5 important steps involved in the morphological-based methods. On the other hand, methods of skull stripping are to be applied according to the skull stripping algorithm matlab based on the nature of the analysis. You might get thoughts on what are algorithms can be fitted in the skull stripping /removal. Don’t squeeze your head, my dear students! We are going to point out to you the algorithms used in the skull removal for your better understanding.

List of Algorithms for Skull Removal

• Multi-seeded 2D Region Growing Method
• Graph-theoretic Image Segmentation Techniques
• Foreground & Background Thresholding
• Local Free-form Deformations & Similarity Transformation
• Connectivity-based Threshold Algorithm
• Anatomical Information-based Method
• Adaptive Fuzzy Segmentation Algorithm
• Neighborhood Analysis
• Connected Component Analysis
• Probability Density Function
• Down-hill Simplex Method
• Watershed Principle (Segmented Tissues & Registration)

The foregoing passage is all about the algorithms so far used in skull removal. As this article is concerned with Matlab, here we are going to list out you the Matlab toolboxes. Skull stripping algorithm matlab are precise and automated, on the other hand, aid in improving the performance and quality of prognostic and diagnostic operations in medical uses. Each algorithm of skull removal has advantages and disadvantages. Visit our website for updated information on all the latest algorithms used for skull removal from MRI images.

As of now, we had seen the basic concepts following the skull removals. In addition to this coverage, our technical team wanted to let you know about the toolboxes that are widely used in the skull stripping approaches. Are you ready to know about that? Let us try to understand them.

Best Matlab Toolboxes for Skull Stripping

• Statistics & Machine Learning
• Deep Learning
• 3D Volumetric Processing
• Display & Exploration
• Filtering & Enhancement

The listed above are some of the toolboxes that are widely used in skull removal. They are not limited to these tools but also consist of the following tools. Shall we get into that? Here we go.

Major Matlab Functions for Skull Stripping

• Volume Segmentation: RGB/3D volume images
• Color Thresholding: MRI images
• Image Segmentation: Region refinement

Usually, effective projects in this area need experts’ guidance. For this, you can have our researchers’ suggestions in your determined upbringing projects. Our technical team is always concerned to transfer their knowledge with the students and scholars presented in the world.

Skull stripping techniques are being facilitated by the Matlab functions. Various Matlab functions are pillared with skull removal. We know that you might need a brief explanation in these areas. So we can have the skull stripping techniques with Matlab functions.

Matlab Functions for Skull Stripping Techniques

• superpixels3 (): Superpixels 3D (segmentation)
• superpixels (): Superpixels 2D (segmentation)
• imsegkmeans3 (): K-means volume (segmentation)
• imsegkmeans (): K-means image (segmentation)
• graydiffweight (): Grayscale weight difference (color conversion)
• gradientweight (): Image gradient for weight (gradient compute)
• imsegfmm (): Fast marching binary (segmentation)
• imseggeodesic (): Geodesic distance region (segmentation)
• lazysnapping (): Graph foreground to background (segmentation)
• activecontour (): Active contour foreground to background (segmentation)
• grayconnected (): Flood fill contiguous gray region (color conversion)
• adaptthresh (): Local first-order stat adaptive image (thresholding)
• otsuthresh (): Otsu’s global histogram (thresholding)
• multithresh (): Otsu’s multi-tier image (thresholding)
• graythresh (): Otsu’s global image (thresholding)
• grabcut (): Iterative graph foreground to background (segmentation)

The above listed are the various Matlab functions that are widely used in skull removal. On the other hand, these functions are interconnected with the interfaces. Matlab interfaces are the key components despite permitting the users to navigate the aspects according to their requirements to conclude the best results. Yes, we have highlighted the Matlab interfacing for the skull stripping or removal for ease of your understanding.

Matlab Interfacing for Skull Stripping

• OpenCV
• Scilab
• Python IDEs
• JAVA

Apart from this, there are multiple interfacing tools and applications are utilized in skull stripping algorithm matlab. If you do want more information on this area you can approach our researchers at any time.

The common dataset being used for skull stripping or removal is called the skull dataset. The dataset consists of data obtained from the various subjects of MRI scans for adults and kids who have different kinds of symptoms concerning subclinical and clinical observations. The resolution of the image is obtained in about one cubic millimeter (mm3). In this regard, let us get into the dataset aspects for your better understanding.

Skull Stripping Datasets

• CC-12- Calgary Campinas-12
  • Consists of 12 weighted MRI scans from Siemens, Philips & GE
  • Magnetic field strengths-1.50 T and 3.00 T
  • Compared with spoiled gradient recalled echo sequences (3D)
  • Structured voxel acquisition (mm3)
  • Subject consists of 12 (6:male & 6:female) with magnetic field intensity mixtures
  • Nonlocal segmentation based brain extraction masks
  • ITK-snap-manual corrections
  • Authorization of the experienced neurologists
• LPBA40- LONI Probabilistic Brain Atlas
  • Consists of 40 3D coronals MRI scans from GE (1.50 T)
  • Acquisition fromspoiled gradient recalled echo sequences
  • Size of the voxel is mm3 (1.50)
  • Manual corrections by brain extraction tools
• OASIS- Open Access Series of Imaging Studies
  • Consists of classifications & subjects (20 & 77)
  • Number of registered MP rage scans 3-4
  • Acquisition fromSiemens (1.50T)
  • Size of the voxel is mm3 (1.250)
  • In-house based manual corrections (masks)

The brain mask, also known as the actual truth, seems to be the visual cover of the brain. It’s made with the Best approach (brain extraction based on nonlocal segmentation) and supervised editing by subject specialists to get rid of non-brain tissues. Overlaying masks on realistic images is compared with the spoiled gradient recalled echo sequences.

Contemporary research ideas getting boom in the field of skull removal from MRI images, advanced and more reliable datasets are being developed to enhance the accuracy of the system. You can get a clear-cut explanation with technical handy notes and all the necessary info regarding the latest development in the field from our experts. We will now look into the parameters used in analyzing the performance of skull removal mechanisms.

Performance Evaluation Metrics for Skull Stripping

• FN- False Negative
  • False detection of background pixels
• TN- True Negative
  • Exact detection of background pixels
• FP- False Positive
  • False foreground pixel segmentation
• TP- True Positive
  • Exact foreground pixel segmentation