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Updated: Apr 19, 2026

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
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Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy.

Ilias Maglogiannis1, Konstantinos K Delibasis2

  • 1Department of Digital Systems, University of Piraeus, Greece.

Computer Methods and Programs in Biomedicine
|December 27, 2014
PubMed
Summary

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This study introduces a new method to detect and count dark dots and globules in dermoscopy images, improving skin lesion analysis. The findings suggest this technique enhances the accuracy of classifying skin lesions as melanoma or non-malignant nevus.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Dermoscopy is crucial for skin lesion assessment.
  • Identifying dark dots and globules is vital but challenging with current tools.

Purpose of the Study:

  • Develop a novel method for detecting, segmenting, and counting dark dots and globules in dermoscopy images.
  • Evaluate the diagnostic value of features extracted from these structures for skin lesion classification.

Main Methods:

  • A multi-resolution approach using inverse non-linear diffusion for segmentation.
  • Feature extraction from segmented dots and globules.
  • Evaluation of features in classifying skin lesions (melanoma vs. nevus).

Main Results:

Keywords:
Dark dot segmentationDermoscopy imagesGlobule segmentationImage classificationMelanoma detectionSkin lesions

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  • The proposed algorithm effectively segments dark dots and globules.
  • Extracted features improve classification performance when combined with other descriptors.
  • The method shows high effectiveness in automatic segmentation.

Conclusions:

  • The novel methodology accurately segments critical features in dermoscopy images.
  • Incorporating features from segmented dots and globules enhances the discrimination between malignant and benign skin lesions.