Diffuse Parenchymal Lung Diseases 3D Automated Detection in MDCT 1st Edition by Catalin Fetita, Kuang Che Chang Chien, Pierre Yves Brillet, Francoise Preteux, Philippe Grenier ISBN 9783540757573

Diffuse Parenchymal Lung Diseases: 3D Automated Detection in MDCT 1st Edition by Catalin Fetita, Kuang Che Chang Chien, Pierre Yves Brillet, Francoise Preteux, Philippe Grenier – Ebook PDF Instant Download/Delivery. 9783540757573
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ISBN 10: 
ISBN 13: 9783540757573
Author: Catalin Fetita, Kuang Che Chang Chien, Pierre Yves Brillet, Francoise Preteux, Philippe Grenier

Characterization and quantification of diffuse parenchymal lung disease (DPLD) severity using MDCT, mainly in interstitial lung diseases and emphysema, is an important issue in clinical research for the evaluation of new therapies. This paper develops a 3D automated approach for detection and diagnosis of DPLDs (emphysema, fibrosis, honeycombing, ground glass).The proposed methodology combines multi-resolution image decomposition based on 3D morphological filtering, and graph-based classification for a full characterization of the parenchymal tissue. The very promising results obtained on a small patient database are good premises for a near implementation and validation of the proposed approach in clinical routine.

Diffuse Parenchymal Lung Diseases: 3D Automated Detection in MDCT 1st Table of contents:

• Introduction
  1.1 Motivation and Background
  1.2 Overview of Diffuse Parenchymal Lung Diseases (DPLD)
  1.3 Importance of MDCT in Lung Disease Diagnosis
  1.4 Key Contributions of the Paper
  1.5 Structure of the Paper

• Preliminaries
  2.1 Diffuse Parenchymal Lung Diseases: Classification and Pathology
  2.2 Multidetector Computed Tomography (MDCT) Overview
  2.3 3D Imaging Techniques in MDCT
  2.4 Current Methods for Lung Disease Detection
  2.5 Challenges in Automatic Detection of DPLD

• Automated Detection of DPLD in MDCT
  3.1 Key Challenges in 3D Lung Disease Detection
  3.2 Image Segmentation for MDCT Data
  3.3 Feature Extraction for Lung Parenchyma Analysis
  3.4 Classification Algorithms for Disease Detection
  3.5 Integration of Machine Learning in Automated Systems

• Methodology
  4.1 Data Acquisition and MDCT Imaging Protocol
  4.2 Preprocessing and Image Enhancement Techniques
  4.3 3D Segmentation of Lung Parenchyma
  4.4 Disease Localization and Detection Approach
  4.5 Automated Decision-Making Model (Algorithm Design)

• Evaluation and Results
  5.1 Evaluation Metrics for Detection Accuracy
  5.2 Experimental Setup and Dataset Description
  5.3 Performance of the Proposed Method
  5.4 Comparative Study with Traditional Methods
  5.5 Case Studies: Detection of Specific DPLDs (e.g., Idiopathic Pulmonary Fibrosis, Sarcoidosis)

• Applications in Clinical Practice
  6.1 Clinical Relevance of Automated Detection Systems
  6.2 Integrating MDCT with Other Diagnostic Tools
  6.3 Impact on Early Diagnosis and Prognosis
  6.4 Automated Systems in Radiology Workflow

• Discussion
  7.1 Insights from the 3D Automated Detection Method
  7.2 Limitations and Challenges in the Current Approach
  7.3 Potential Improvements and Future Directions
  7.4 Impact of Automated Detection on Healthcare and Radiology

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