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This work introduces a sophisticated strategy for improving imaging of stroke lesions utilizing the Pixel Correlation Histogram Analysis (PCHA) CT method, which was created especially for CT scans. The PCHA CT method uses intensity-based pixel correlation histograms to enhance the visibility of defective atrophic brain cells. The basis for a comprehensive Pixel Correlation Histogram Matrix is the original Region of Interest (ROI) matrix, which offers a grayscale histogram for every pixel value. The distribution level of individual pixels across different modules is shown in this matrix, which also displays pixel distribution patterns inside discrete blocks. The technique uses a contact histogram matrix that records changes in pixel intensity for particular objects in order to overcome the drawbacks of universal pixel correlation analysis. By efficiently managing variances and adjusting pixel intensity distributions, this optimization method enhances the precision and lucidity of stroke lesion visualization. The suggested approach offers a dependable instrument for improving medical imaging, which helps with stroke care by improving diagnostic and therapy planning.

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Predicting Stroke Outcomes Using Machine Learning: Analysis of Clinical and Imaging Data

  • 会议论文

  • 发表日期：

    2026年

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  • Sachin Bere,
  • Manali Shah,
  • Mayuri M. Gawade,
  • Gopal Deshmukh,
  • Pawar Sonali,
  • Dattatray G. Takale,
  • Parishit N. Mahalle,
  • Bipin Sule

摘要

This work introduces a sophisticated strategy for improving imaging of stroke lesions utilizing the Pixel Correlation Histogram Analysis (PCHA) CT method, which was created especially for CT scans. The PCHA CT method uses intensity-based pixel correlation histograms to enhance the visibility of defective atrophic brain cells. The basis for a comprehensive Pixel Correlation Histogram Matrix is the original Region of Interest (ROI) matrix, which offers a grayscale histogram for every pixel value. The distribution level of individual pixels across different modules is shown in this matrix, which also displays pixel distribution patterns inside discrete blocks. The technique uses a contact histogram matrix that records changes in pixel intensity for particular objects in order to overcome the drawbacks of universal pixel correlation analysis. By efficiently managing variances and adjusting pixel intensity distributions, this optimization method enhances the precision and lucidity of stroke lesion visualization. The suggested approach offers a dependable instrument for improving medical imaging, which helps with stroke care by improving diagnostic and therapy planning.