%{search_type} search results

• Purpose and Scope of the Monograph.- Introduction.- Image Acquisition.- Image Pre-processing.- Image Processing.- Examples of Tailoring the Algorithm.
• (source: Nielsen Book Data)

The book presents automatic and reproducible methods for the analysis of medical infrared images. All methods highlighted here have been practically implemented in Matlab, and the source code is presented and discussed in detail. Further, all methods have been verified with medical specialists, making the book an ideal resource for all IT specialists, bioengineers and physicians who wish to broaden their knowledge of tailored methods for medical infrared image analysis and processing.
(source: Nielsen Book Data)

• Processing of Hyperspectral Medical Images: Applications in Dermatology Using Matlab (R).- Foreword.- Preface.- Acknowledgments.- List of selected symbols.- Chapte
• r1: Introduction.- Chapte
• r2: Image acquisition.- Chapte
• r3: Image pre-processing.- Chapte
• r4: Image processing.- Chapte
• r5: Classification.- Chapte
• r6: Sensitivity to parameter changes.- Chapte
• r7: Conclusions.- Appendix.- References.- Summary.
• (source: Nielsen Book Data)

This book presents new methods of analyzing and processing hyperspectral medical images, which can be used in diagnostics, for example for dermatological images. The algorithms proposed are fully automatic and the results obtained are fully reproducible. Their operation was tested on a set of several thousands of hyperspectral images and they were implemented in Matlab. The presented source code can be used without licensing restrictions. This is a valuable resource for computer scientists, bioengineers, doctoral students, and dermatologists interested in contemporary analysis methods.
(source: Nielsen Book Data)

• Introduction
• Image pre-processing
• Main Image processing
• Additional Image Processing and Measurement
• Impact of Image Acquisition and Selection of Algorithm Parameters on the Results
• Summary of Measured Features
• Conclusions.

This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

• 1. Corvis ST tonometer and the possibility of analysing corneal deformation dynamics during intraocular pressure measurement
• 1.1. Introduction
• 1.2. Basics of measurements with the Corvis ST tonometer
• 1.3. Available parameters
• 1.4. Characteristics of dynamic corneal response parameters
• 1.5. Biomechanical-compensated IOP (bIOP) and dynamic corneal response parameters in the available literature
• 1.6. Image processing for obtaining new biomechanical parameters of the cornea
• 1.7. Modelling of dynamic corneal deformation
• 1.8. Summary

• 2. Air-puff devices, not just tonometry
• 2.1. Introduction
• 2.2. How the instruments work
• 2.3. Accuracy and repeatability
• 2.4. Corneal deformation in healthy corneas
• 2.5. Corneal deformation in diseased corneas
• 2.6. Corneal deformation after surgery
• 2.7. Studies regarding software improvements
• 2.8. Conclusions

• 3. Clinical applications of the Corvis ST for glaucoma
• 3.1. Introduction
• 3.2. Basic description of the Corvis ST
• 3.3. bIOP : concept and first clinical results
• 3.4. Conclusion

• 4. Evaluation of the algorithms utilised to diagnose keratoconus for the Corvis ST
• 4.1. Introduction
• 4.2. Methods
• 4.3. Results
• 4.4. Discussion

• 5. Intraocular pressure and three-dimensional corneal biomechanics
• 5.1. Behind the need for change in glaucoma diagnosis : IOP and corneal biomechanics
• 5.2. Patient-specific geometry : three-dimensional corneal shape
• 5.3. Corneal biomechanics : traditional ex vivo mechanical testing
• 5.4. Corneal biomechanics and IOP : linking non-invasive imaging and simulations
• 5.5. Novel concepts to decipher corneal biomechanics in 3D and IOP
• 5.6. Looking for new alternatives in IOP measurements

• 6. Ultra-high-speed Scheimpflug imaging for intraocular pressure measurements
• 6.1. IOP measurements with the Corvis ST
• 6.2. Assessment of corneal biomechanical properties with the Corvis ST
• 6.3. Biomechanical corrected IOP (bIOP)
• 6.4. Simulation-based versus empirically-derived correction formulas
• 6.5. Accuracy of bIOP in patients after refractive surgery
• 6.6. bIOP measurements in patients with keratoconus
• 6.7. Ongoing Corvis ST studies in glaucoma
• 6.8. Summary

This book presents the latest achievements and research works of intraocular pressure measurement by the air-puff method presented by experts in the field. It is about tonometers, in particular using the air-puff method. These methods are used, for example, by the Corvis ST. Owing to the ultra-high-speed Scheimpflug camera, which records corneal deformation being the response to an air-puff. In this connection, this book is interdisciplinary in: new methods of image analysis and processing; new engineering methods used in devices; method reducing measurement (intraocular pressure) errors; the change in the blood pulsation phase during measurement affects the results in different ways; other IOP measurement methods (using the air-puff method); medical and engineering problems in air-puff method. This book is recommended reading for those involved in the analysis and processing of images and wanting to expand their knowledge of contemporary diagnostic methods and modern methods of image analysis. Part of IOP Series in Medical and Biological Image Analysis