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Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...

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Related Experiment Video

Updated: Jun 12, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

A soft kinetic data structure for lesion border detection.

Sinan Kockara1, Mutlu Mete, Vincent Yip

  • 1Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA. skockara@uca.edu

Bioinformatics (Oxford, England)
|June 10, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph spanner method for automatic skin lesion border detection in dermoscopic images. The approach achieves high accuracy, demonstrating its potential to improve diagnostic procedures for skin cancer.

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Last Updated: Jun 12, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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13:44

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Published on: August 30, 2013

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Dermatology

Background:

  • Medical imaging and image processing are crucial for dermatological diagnostics.
  • Automated assessment of dermoscopic images is vital due to human interpretation variability.
  • Accurate segmentation and border detection in dermoscopic images are key for skin cancer diagnosis and treatment.

Purpose of the Study:

  • To propose a novel graph spanner approach for automatic border detection in dermoscopic images.
  • To evaluate the effectiveness of the graph spanner method in identifying skin lesion boundaries.

Main Methods:

  • A proximity graph representation of dermoscopic images was utilized.
  • The graph spanner algorithm was applied for automatic region and border detection.
  • The method was tested on 100 dermoscopic images with dermatologist-defined ground truth.

Main Results:

  • The graph spanner approach achieved 100% precision and recall rates for lesion boundary detection.
  • The overall accuracy of the assessment averaged 97.72%.
  • The mean border error rate was found to be 2.28% across the dataset.

Conclusions:

  • The proposed graph spanner method demonstrates high efficacy for automatic border detection in dermoscopic images.
  • This technique shows potential for enhancing diagnostic accuracy and reducing inter-observer variability in skin cancer assessment.
  • The results support the integration of automated tools in dermatological decision-making processes.