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@article{183535,
        author = {Syeda Saniya Ithrath and Dr. Mohammed Abdul Waheed},
        title = {Methods for segmenting and analyzing images to predict hydrocephalus using magnetic resource images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {2086-2091},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183535},
        abstract = {Hydrocephalus, a neurological condition characterized by abnormal accumulation of cerebrospinal fluid (CSF) in brain's ventricles, can be effectively diagnosed and monitored through Magnetic Resonance Imaging (MRI). Advanced image processing and segmentation techniques play a critical role in the early prediction of hydrocephalus by enabling precise analysis of MRI scans. This study presents a methodological approach for segmenting and analyzing brain MR images to detect signs of hydrocephalus. The process begins with preprocessing steps such as skull stripping, noise reduction, and intensity normalization to enhance image quality and isolate relevant anatomical structures. Region of interest (ROI) extraction focuses on ventricular areas, followed by the application of segmentation methods including thresholding, region growing, clustering, and deep learning-based U-Net models to delineate CSF-filled spaces. Quantitative features such as ventricle size, brain-CSF ratio, and volumetric changes are extracted to facilitate classification using machine learning algorithms like Support Vector Machine (SVM) or Random Forest. The proposed approach aims to support clinicians by providing a non-invasive, automated system for early hydrocephalus detection, improving diagnostic accuracy and patient outcomes.},
        keywords = {Hydrocephalus, Brain, Region of interest (ROI), Support Vector Machine (SVM) or Random Forest, brain-CSF},
        month = {August},
        }