Improving the Contrast of Breast Cancer Masses in Ultrasound Using an Autoregressive Model Based Filter 1st Edition by Etienne von Lavante, J Alison Noble ISBN 9783540757573

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ISBN 10: 
ISBN 13: 9783540757573
Author: Etienne von Lavante, J Alison Noble

The assessment and diagnosis of breast cancer with ultrasound is a challenging problem due to the low contrast between cancer masses and benign tissue. Due to this low contrast it has proven to be difficult to achieve reliable segmentation results on breast cancer masses. An autoregressive model has been employed to filter out of the backscattered RF-signal from a tissue harmonic image which is not degraded by harmonic leakage. Measurements on the filtered image have shown a significant (up to 45 %) increase in contrast between cancer masses and benign tissue.

Improving the Contrast of Breast Cancer Masses in Ultrasound Using an Autoregressive Model Based Filter 1st Table of contents:

• Introduction
  1.1 Motivation and Background
  1.2 Importance of Breast Cancer Detection and Imaging
  1.3 Challenges in Ultrasound Imaging for Breast Cancer Detection
  1.4 Autoregressive Models in Image Processing
  1.5 Key Contributions and Objectives of the Paper
  1.6 Structure of the Paper

• Preliminaries
  2.1 Overview of Breast Ultrasound Imaging
  2.2 Ultrasound Image Artifacts and Noise Challenges
  2.3 Image Enhancement Techniques in Medical Imaging
  2.4 The Concept of Autoregressive Models and Their Application in Filtering
  2.5 Related Work on Ultrasound Image Enhancement and Contrast Improvement

• Autoregressive Model-Based Filtering
  3.1 Mathematical Foundation of Autoregressive (AR) Models
  3.2 AR Model-Based Filters for Image Enhancement
  3.3 Estimation and Parameterization of AR Models in Image Data
  3.4 Application of AR Filters for Contrast Enhancement in Ultrasound Images
  3.5 Advantages of AR Model-Based Filters Over Traditional Methods

• Improving Contrast of Breast Cancer Masses
  4.1 Identification and Detection of Breast Masses in Ultrasound Images
  4.2 Challenges in Improving Contrast for Tumor Detection
  4.3 Incorporating AR Model-Based Filters for Mass Contrast Enhancement
  4.4 Algorithm for Mass Contrast Enhancement in Breast Ultrasound
  4.5 Evaluation Metrics for Contrast Improvement

• Methodology
  5.1 Data Acquisition and Ultrasound Imaging Protocol
  5.2 Preprocessing and Noise Removal in Ultrasound Images
  5.3 Design of AR Model-Based Filtering Algorithm
  5.4 Parameter Estimation and Tuning of the AR Filter
  5.5 Computational Framework and Algorithm Implementation

• Experimental Results
  6.1 Dataset Description and Experimental Setup
  6.2 Evaluation of Contrast Improvement Metrics (e.g., Signal-to-Noise Ratio, Contrast-to-Noise Ratio)
  6.3 Comparison with Other Image Enhancement Techniques (e.g., Filtering, Edge Detection)
  6.4 Case Studies and Visual Comparisons of Enhanced Ultrasound Images
  6.5 Sensitivity to Image Quality and Tumor Characteristics

• Clinical Relevance and Applications
  7.1 Enhancing Tumor Visualization for Radiologists
  7.2 Potential Benefits for Early Breast Cancer Detection
  7.3 Applications in Tumor Characterization and Biopsy Guidance
  7.4 Impact on Screening Programs and Diagnostic Workflow

• Discussion
  8.1 Insights on the Effectiveness of AR Model-Based Filters in Ultrasound Imaging
  8.2 Limitations and Challenges in Current Approach
  8.3 Future Directions for AR Model-Based Filtering in Ultrasound Imaging
  8.4 Integration with Other Imaging Modalities (e.g., Mammography, MRI)

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