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Preprocessing with image denoising and histogram equalization for endoscopy image analysis using texture analysis.

Tomoyuki Hiroyasu, Katsutoshi Hayashinuma, Hiroshi Ichikawa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
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    Summary

    This study introduces an improved preprocessing method for analyzing early gastric cancer using narrow-band imaging endoscopy. The new technique enhances image quality, leading to more accurate identification of lesion zones in endoscopic images.

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    Area of Science:

    • Medical imaging
    • Gastroenterology
    • Computer vision

    Background:

    • Texture analysis is crucial for early gastric cancer detection in narrow-band imaging (NBI) endoscopy.
    • Previous methods combining co-occurrence and run-length matrices were affected by noise and halation, hindering accurate lesion zone identification.

    Purpose of the Study:

    • To develop an advanced preprocessing method for NBI endoscopy images to improve early gastric cancer detection.
    • To overcome limitations of previous texture analysis methods caused by image noise and halation.

    Main Methods:

    • Implementation of a non-local means filter for effective image de-noising.
    • Application of contrast limited adaptive histogram equalization (CLAHE) to enhance image contrast.
    • Integration of these preprocessing steps before texture analysis using combined co-occurrence and run-length matrices.

    Main Results:

    • The proposed preprocessing method significantly improved the clarity of gastric mucosa patterns in endoscopic images.
    • De-noising and contrast enhancement resulted in more accurate delineation of lesion zones.
    • The color map generated after preprocessing demonstrated improved visualization of cancerous areas.

    Conclusions:

    • The novel preprocessing technique enhances the reliability of texture analysis for early gastric cancer detection.
    • This method offers a more accurate approach to identifying lesion zones in NBI endoscopic images.
    • The improved image quality facilitates better diagnosis and potentially earlier intervention for gastric cancer.