Brain MRI Images Processed with Histogram Equalization and Skull Stripping

Zahid Ullah from Changwon National University presents a method for processing brain MRI images. The methodology uses histogram equalization for contrast enhancement and mathematical morphology for skull stripping, implemented in MATLAB R2015a. Results were evaluated using Mean Square Error and Peak Signal to Noise Ratio metrics.

Use Cases

• Evaluating skull stripping algorithms based on the described morphological processing technique.
• Benchmarking contrast enhancement methods using histogram equalization on brain MRI.
• Developing pre-processing steps for brain image diagnosis applications as mentioned in the description.

Strengths

• The methodology is explicitly described, including the use of histogram equalization and mathematical morphology.
• Results are evaluated with specific quantitative metrics: Mean Square Error and Peak Signal to Noise Ratio.
• The implementation platform is specified as MATLAB R2015a.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Zahid Ullah, Changwon National University

Collection Method

Proposed image processing methodology implemented in MATLAB.