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This paper proposes an improved Fuzzy C-Means (FCM) image segmentation algorithm that combines superpixel segmentation and a Convolutional Autoencoder (CAE) for medical image segmentation. The method first uses superpixel segmentation to preprocess the input image to optimize the membership filtering matrix, followed by iterative optimization using a convolutional autoencoder. We validated the performance of the algorithm on the BrainWeb dataset.

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Enhanced FCM for Medical Image Segmentation Using Superpixel and Convolutional Autoencoder

  • 会议论文

  • 发表日期：

    2025年

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  • Weimin Zhou,
  • Yi Gu

摘要

This paper proposes an improved Fuzzy C-Means (FCM) image segmentation algorithm that combines superpixel segmentation and a Convolutional Autoencoder (CAE) for medical image segmentation. The method first uses superpixel segmentation to preprocess the input image to optimize the membership filtering matrix, followed by iterative optimization using a convolutional autoencoder. We validated the performance of the algorithm on the BrainWeb dataset.