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Related Experiment Video

Updated: Jun 11, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Anisotropic morphological filters with spatially-variant structuring elements based on image-dependent gradient

Rafael Verdú-Monedero1, Jesús Angulo, Jean Serra

  • 1Department of Tecnologías de Información y Comunicaciones, Universidad Politécnica de Cartagena, 32202 Cartagena, Spain. rafael.verdu@upct.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces spatially-variant mathematical morphology for image analysis. It enables adaptive structuring elements to enhance anisotropic features in images, proving effective for flow-like structures.

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Last Updated: Jun 11, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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Published on: August 30, 2013

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

  • Image processing
  • Computer vision
  • Mathematical morphology

Background:

  • Traditional mathematical morphology uses fixed structuring elements.
  • This limits its ability to analyze images with spatially varying structures.

Purpose of the Study:

  • To formalize and apply spatially-variant discrete mathematical morphology.
  • To develop adaptive morphological operators for analyzing anisotropic image features.

Main Methods:

  • Defined spatially variant dilation/erosion and opening/closing using structuring functions.
  • Extracted local orientation via diffusion of gradient fields.
  • Adapted structuring element shape based on image features.

Main Results:

  • Developed novel morphological operators with locally adaptive structuring elements.
  • Successfully enhanced anisotropic features like flow-like structures in images.
  • Demonstrated effectiveness on both binary and gray-level images.

Conclusions:

  • The proposed spatially-variant mathematical morphology provides a theoretically sound and novel approach.
  • This method effectively enhances anisotropic features by adapting to local image characteristics.