False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns 1st edition by Arnau Oliver, Xavier Llado, Jordi Freixenet, Joan Marti ISBN 9783540757573

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ISBN 10: 
ISBN 13: 9783540757573
Author: Arnau Oliver, Xavier Llado, Jordi Freixenet, Joan Marti

In this paper we propose a new approach for false positive reduction in the field of mammographic mass detection. The goal is to distinguish between the true recognized masses and the ones which actually are normal parenchyma. Our proposal is based on Local Binary Patterns (LBP) for representing salient micro-patterns and preserving at the same time the spatial structure of the masses. Once the descriptors are extracted, Support Vector Machines (SVM) are used for classifying the detected masses. We test our proposal using a set of 1792 suspicious regions of interest extracted from the DDSM database. Exhaustive experiments illustrate that LBP features are effective and efficient for false positive reduction even at different mass sizes, a critical aspect in mass detection systems. Moreover, we compare our proposal with current methods showing that LBP obtains better performance.

False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns 1st Table of contents:

• Preliminaries
  2.1 Mammographic Mass Detection: Challenges and Importance
  2.2 False Positives in Mammography: Causes and Impact
  2.3 Local Binary Patterns: Definition and Properties
  2.4 Current Techniques for Reducing False Positives in Mammography
  2.5 Related Work on LBP in Medical Image Analysis

• Methodology
  3.1 Overview of the Detection System
  3.2 Image Preprocessing and Enhancement for Mammograms
  3.3 Feature Extraction Using Local Binary Patterns
  3.4 Classifier Selection and Training for Mass Detection
  3.5 False Positive Reduction Strategies Using LBP Features

• Local Binary Patterns for Texture Analysis
  4.1 LBP Variants and Their Application to Mammograms
  4.2 Texture Features for Mass Detection in Mammography
  4.3 Extracting and Encoding Texture Information with LBP
  4.4 Enhancements to LBP for Improving Detection Accuracy

• Evaluation and Experimental Results
  5.1 Dataset and Experimental Setup
  5.2 Evaluation Metrics for Mass Detection Performance
  5.3 Results on False Positive Reduction Using LBP
  5.4 Comparison with Other Methods for Mass Detection
  5.5 Case Studies: Detecting Benign vs. Malignant Masses

• Impact of False Positive Reduction on Clinical Practice
  6.1 Significance of Reducing False Positives in Mammography
  6.2 Effect on Radiologist Workload and Diagnosis Accuracy
  6.3 Potential for Improved Screening Efficiency
  6.4 Integration with Existing Mammography Systems

• Discussion
  7.1 Insights on the Effectiveness of LBP for False Positive Reduction
  7.2 Limitations of the Approach and Areas for Improvement
  7.3 Future Directions in LBP and Mass Detection
  7.4 Challenges in Real-World Clinical Application

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