Abstract

Breast cancer, per WHO, ranks top in diagnoses and cancer fatalities. Early detection via mammography reduces mortality significantly, yet mammogram images often have indistinct features. Hence, precise tumor edge identification requires both image enhancement and segmentation. In response, we introduce the Max Planck Algorithm, a novel segmentation method rooted in Planck's quantum theory, specifically his thermal radiation principles. We innovatively converted this theory to create a unique segmentation tool applicable to digital image processing and medical imaging. The algorithm works by relating mammogram pixel values to X-ray wavelengths, adapting Planck's Law to use 'temperature' as an arbitrary variable (originally tied to actual temperature in Planck's work). Gradually adjusting 'temperature' optimizes the mammogram image's meaningfulness. The Max Planck Algorithm boasts advantageous properties, delivering higher efficiency and superior segmentation results. This innovative model introduces new methods for enhancing and segmenting mammograms, establishing itself as a unique technique without comparison to existing methods.

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Cite this article

@article{cai-7129,
  author  = {Yun Zhang and Refat Mohammed Abdullah Eshaq and Weian Dong and Chunxu Li},
  title   = {Max Planck Theory for Digital Image Processing: A New Algorithm for Mammogram Image Segmentation to Identify Masses in Regions of the Breast},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {2},
  pages   = {366--384},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_2_366}
}
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