A Comprehensive Strategy for the Identification of Arachnoid Cysts in the Brain Utilizing Image Processing Segmentation Methods
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Abstract
This study focuses on the segmentation and characterization of arachnoid cysts in brain MRI images, aiming to enhance diagnostic accuracy through advanced image processing techniques. Arachnoid cysts are cerebrospinal fluid-filled sacs located between the brain or spinal cord and the arachnoid membrane. These cysts can be asymptomatic but may also cause neurological symptoms such as headaches, seizures, or cognitive impairments when they increase in size or pressure. Accurate detection and characterization are essential for timely intervention and treatment. In this research, 269 brain MRI images were analyzed using connected component analysis (CCA) and contrast-limited adaptive histogram equalization (CLAHE). CLAHE was employed to enhance image contrast, particularly in regions with subtle intensity differences, while CCA facilitated the segmentation of connected regions corresponding to cysts. The smallest connected components were identified and analyzed to isolate arachnoid cysts with high precision. Post-segmentation, quantitative analysis was performed to extract features such as size, shape, and density, enabling comprehensive cyst characterization. Additionally, calculations for area and approximate volume were conducted, providing critical information for clinical assessment. Visual validation of segmentation outcomes confirmed the effectiveness of the applied methods in accurately delineating cyst boundaries. This research addresses a significant gap in the existing literature. While most studies focus on brain tumor segmentation, there is limited work on arachnoid cyst detection and volume estimation. By integrating image processing techniques tailored for arachnoid cysts, this study offers a novel approach to their diagnosis and monitoring. The findings demonstrate the potential for automated diagnostic tools, reducing subjectivity and improving efficiency in clinical workflows. The proposed methodology aligns with advancements in medical imaging and contributes to the development of improved tools for neuroimaging diagnostics, paving the way for more precise and reliable assessments in the detection of brain pathologies.
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