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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Early Detection of Skin Cancer Using Melanoma Segmentation technique.

Tammineni Sreelatha1, M V Subramanyam2, M N Giri Prasad3

  • 1Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Ananthapuramu, Ananthapuramu, Andhra Pradesh, India. tamminenisreelatha.jntu@gmail.com.

Journal of Medical Systems
|May 22, 2019
PubMed
Summary
This summary is machine-generated.

A new Gradient and Feature Adaptive Contour (GFAC) model precisely segments melanoma in dermoscopic images. This advanced image processing technique aids early skin cancer detection and diagnosis, improving patient outcomes.

Keywords:
Feature extractionGradientMelanomaSegmentation

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Pattern recognition

Background:

  • Melanoma is a deadly skin cancer with high mortality rates.
  • Early detection through precise lesion segmentation is crucial for effective treatment.
  • Dermoscopic images are vital for diagnosing skin cancer.

Purpose of the Study:

  • To introduce a precise melanoma segmentation technique for early skin cancer detection.
  • To enhance the diagnosis of melanoma using dermoscopic images.
  • To develop a noise-free and efficient segmentation model.

Main Methods:

  • A Gradient and Feature Adaptive Contour (GFAC) model was developed for image segmentation.
  • Pre-processing and noise elimination techniques were employed to improve image quality and processing speed.
  • Multiple Gaussian distributed patterns were utilized for efficient feature extraction and precise segmentation.

Main Results:

  • The proposed GFAC model achieved noise-free segmentation with smoother borders.
  • The technique effectively separates melanoma lesions from the background in dermoscopic images.
  • The model demonstrated superiority in segmented image quality, error reduction, and feature extraction compared to state-of-the-art methods on the PH2 dataset.

Conclusions:

  • The developed GFAC model offers a precise and efficient method for melanoma segmentation.
  • This technique significantly aids in the early detection and diagnosis of skin cancer.
  • The GFAC model shows promise for improving melanoma detection accuracy in clinical settings.