Image Processing Techniques for Tumor Detection 1st edition by Robin Strickland – Ebook PDF Instant Download/Delivery: 1135551405, 9781135551407
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ISBN 10: 1135551405
ISBN 13: 9781135551407
Author: Robin Strickland
“Provides a current review of computer processing algorithms for the identification of lesions, abnormal masses, cancer, and disease in medical images. Presents useful examples from numerous imaging modalities for increased recognition of anomolies in MRI, CT, SPECT and digital/film X-Ray.”
Image Processing Techniques for Tumor Detection 1st Table of contents:
1: Tumor Imaging
I. Definitions: Neoplasms and Tumors
II. Reasons For Imaging Tumors Or Potential Tumors
III. Direct and Indirect Visualization of Tumors
IV. Factors that Influence the Appearance of Tumors in Images
V. Features of Malignant Tumors Seen on Images
VI. Human Factors that Influence Our Understanding of the Nature of Image Feature
VII. Screening for Breast and Lung Cancer
VIII. Conclusions: Some Lessons for the Computer Scientist
References
2: Evaluating Detection Algorithms
I. Introduction
II. Detection Criteria
III. Algorithm Evaluation
IV. Clinical Evaluation
V. Recommendations
VI. A Case Study
Acknowledgments
References
3: Clinical Applications Present and Future
I. Introduction
II. Prompting/Cueing to Improve Performance
III. Medical Imaging and Lesion Detection
IV. Critical Issues for Clinical Use Of CAD
IV. Conclusions
References
4: Statistical Decision Theory and Tumor Detection
I. Introduction
II. Data and Observers
III. Ideal Observers
IV. Hotelling Observers
V. Observer Performance
VI. SKE/BKE
VII. SKE
VIII. BKE
IX. NKE
X. Summary
References
5: Display, Including Enhancement, of Two- Dimensional Images
I. Display Scales
II. Assignment of Display Scale Intensities to Recorded Intensities
References
6: Detection of Microcalcifications
I. Introduction
II. Preprocessing
III. Segmentation
IV. Feature Analysis
V. Alternatives to Feature Extraction
VI. Computation Times
VII. Digital Mammograms
VIII. Conclusion
IX. Appendix
References
7: Evaluation of a Multiscale Enhancement Protocol for Digital Mammography
I. Introduction
II. Enhancement Protocol
III. Development of A Graphical User Interface
IV. Description of the Receiver Operating Characteristics (ROC) Study
V. Conclusions and Future Work
Acknowledgement
References
8: Detection of Masses in Mammograms
I. Introduction
II. Common Aspects of Detection Algorithms
III. Preprocessing
IV. Detection in Single Views
V. Detection Using Multiple Views
VI. Experimental Results and Discussion
References
9: Region-Based Adaptive Contrast Enhancement
I. Enhancement of Mammograms
II. Region Growing Methods
III. Adaptive Contrast Enhancement
IV. Case Selection, Digitization, and Presentation
V. ROC and Statistical Analysis
VI. Results
VII. Discussion
VIII. Summary
Acknowledgment
References
10: Computerized Detection of Lung Nodules
I. Introduction
II. Image Processing Techniques for Lung Nodule Enhancement
III. Computerized Detection of Lung Nodules
IV. Summary
Acknowledgment
References
11: Lung: X-Ray and CT
I. Introduction
II. Clinical Setting
III. Image Processing Methods Used in Pulmonary Nodule Detection on CT
IV. Future Work
References
12: Optimal Processing of Brain MRI
I. Introduction
II. Preprocessing
III. Contrast Enhancement
IV. Feature Extraction
V. Image Segmentation
VI. Discussion
Acknowledgment
References
13: Brain Tumor Imaging: Fusion of Scintigraphy with Magnetic Resonance and Computed Tomography
I. Introduction
II. Technical Considerations
III. Our Method
IV. Clinical Applications
Acknowledgment
References
14: Image Registration in the Thorax, Abdomen, and Pelvis
I. Introduction
II. Technical Considerations
III. Clinical Applications of Image Registration
IV. Conclusion
References
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