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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
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A titration curve is a plot of some solution property versus the amount of added titrant. For acid-base titrations, solution pH is a useful property to monitor because it varies predictably with the solution composition and, therefore, may be used to monitor the titration’s progress and detect its endpoint. Acid-base titration can be performed with a strong acid and a strong base, a strong acid and a weak base, or a strong base and a weak acid.
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In 1923, G. N. Lewis proposed a generalized definition of acid-base behavior in which acids and bases are identified by their ability to accept or to donate a pair of electrons and form a coordinate covalent bond.
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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
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Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based

Shrikant A Mehre1, Ashis Kumar Dhara1,2, Mandeep Garg3

  • 1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.

Journal of Digital Imaging
|October 27, 2018
PubMed
Summary

This study introduces a novel class membership-based retrieval (CMR) system for lung nodules, outperforming existing methods. Optimal feature selection significantly enhances retrieval precision for lung cancer diagnosis support.

Keywords:
CT imagesContent-based image retrievalDiagnosis of lung cancerFeature selectionLung cancerLung nodulesSelf-learning tool of radiology

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Area of Science:

  • Medical Imaging and Diagnostics
  • Computer-Aided Diagnosis
  • Artificial Intelligence in Medicine

Background:

  • Lung cancer diagnosis relies on identifying lung nodules, crucial for treatment planning.
  • Automated retrieval systems aid radiologists in learning and differential diagnosis.
  • High-dimensional features can negatively impact retrieval performance (curse of dimensionality).

Purpose of the Study:

  • To develop and evaluate an enhanced content-based image retrieval (CBIR) system for lung nodules.
  • To investigate the impact of optimal feature selection on retrieval performance.
  • To compare the proposed system against conventional retrieval methods.

Main Methods:

  • Utilized the minimal-redundancy-maximal-relevance (mRMR) technique for optimal feature selection.
  • Proposed a class membership-based retrieval (CMR) system incorporating shape, margin, and texture features.
  • Evaluated the system on LIDC/IDRI (public) and PGIMER-IITKGP (private) databases using Canberra, City block, and Euclidean distances.

Main Results:

  • Optimal feature sets improved precision for simple distance-based retrieval (SDR).
  • Class membership-based retrieval (CMR) effectively addressed zero retrieval precision issues seen in conventional classifier-based retrieval (CCBR).
  • The proposed CMR system with optimal features demonstrated superior average precision compared to CCBR and SDR.

Conclusions:

  • Optimal feature selection is critical for both classification and retrieval components in nodule retrieval systems.
  • The proposed CMR-based retrieval system offers improved performance for lung nodule retrieval.
  • This CBIR system shows promise for assisting radiologists in lung nodule diagnosis and self-learning.