"Image Processing in Optical Coherence Tomography using Matlab" by Robert Koprowski, Zygmunt Wróbel

The monograph comprises proposals of new and also of known algorithms, modified by authors, for image analysis and processing, presented on the basis of example of Matlab environment with Image Processing tools. The results are not only obtained fully automatically, but also repeatable, providing doctors with quantitative information on the degree of pathology occurring in the patient.


The book is addressed both to ophthalmologists willing to expand their knowledge in the field of automated eye measurements and also primarily to IT specialists, Ph.D. students and students involved in the development of applications designed for automation of measurements for the needs of medicine.

CONTENTS
1 INTRODUCTION
2 ACQUISITION OF IMAGE DATA
3 ANALYSIS OF ANTERIOR EYE SEGMENT
3.1 Introduction to Anterior Eye Segment Analysis
3.2 Review of Hitherto Filtration Angle Analysis Methods
3.3 Verification of the Sensitivity of the Proposed Methods
3.3.1 Methodology for Measuring Methods Sensitivity to Parameters Change
3.3.2 Methods Sensitivity to Parameters Change
3.3.3 Conclusions From the Sensitivity Analysis Methods
3.4 The Results of Automatic Analysis Chamber Angle Obtained Using Well-Known Algorithms
3.5 Proposed Modifications to the Well-Known Method of Measuring
3.6 Algorithm for Automated Analysis of the Filtration Angle
3.6.1 Advantages of the Algorithm Proposed
3.7 Determination of Anterior Chamber Volume Based on a Series of Images
4 ANALYSIS OF POSTERIOR EYE SEGMENT
4.1 Introduction to the fundus of the eye analysis
4.2 Algorithm for Automated Analysis of Eye Layers in the Classical Method
4.2.1 Preprocessing
4.2.2 Detection of RPE Boundary
4.3 Detection of IS, ONL Boundaries
4.4 Detection of NFL Boundary
4.5 Correction of Layers Range
4.6 Final Form of Algorithm
4.7 Determination of „Holes‟ on the Image
4.8 Assessment of Results Obtained Using the Algorithm Proposed
4.9 Layers Recognition on a Tomographic Eye Image Based on Random Contour Analysis
4.9.1 Determination of Direction Field Image
4.9.2 Starting Points Random Selection and Correction
4.9.3 Iterative Determination of Contour Components
4.9.4 Determination of Contours from Their Components
4.9.5 Setting the Threshold of Contour Components Sum Image
4.9.6 Properties of the Algorithm Proposed
4.9.7 Assessment of Results Obtained from the Random Method
4.10 Layers Recognition on Tomographic Eye Image Based on Canny Edge Detection
4.10.1 Canny Filtration
4.10.2 Features of Line Edge
4.10.3 Contour Line Correction
4.10.4 Final Analysis of Contour Line
4.11 Hierarchical Approach in the Analysis of Tomographic Eye Image
4.11.1 Image Decomposition
4.11.2 Correction of Erroneous Recognitions
4.11.3 Reducing the Decomposition Area
4.11.4 Analysis of ONL Layer
4.11.5 Determination of the Area of Interest and Preprocessing
4.11.6 Layers Points Analysis and Connecting
4.11.7 Line Correction
4.11.8 Layers Thickness Map and 3D Reconstruc
4.11.9 Evaluation of Hierarchical Approach
4.12 Evaluation and Comparison of Suggested Approaches Results
5 SUMMARY
6 SUPPLEMENT
BIBLIOGRAPHY