Handbook of Medical Image Processing and Analysis

Isaac Bankman

• Éditeur: Academic Press
• Année: 2008
• ISBN:
  9780080559148
• Édition: 2
• Sujet: Computer Science
• Sous-sujet: Digital Media

The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. The Handbook is organized into six sections that relate to the main functions: enhancement, segmentation, quantification, registration, visualization, and compression, storage and communication.The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation; tumor growth modeling in oncological image analysis; analysis of cell nuclear features in fluorescence microscopy images; imaging and communication in medical and public health informatics; and dynamic mammogram retrieval from web-based image libraries.For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing. Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE.

• Includes contributions from internationally renowned authors from leading institutions
• NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis.
• Provides a complete collection of algorithms in computer processing of medical images
• Contains over 60 pages of stunning, four-color images

Part I

Enhancement

Chapter 1 Fundamental Enhancement Techniques

Raman B. Paranjape

University of Regina

1.1 Introduction

1.2 Preliminaries and Definitions

1.3 Pixel Operations

1.3.1 Compensation for Nonlinear Characteristics of Display or Print Media

1.3.2 Intensity Scaling

1.3.3 Histogram Equalization

1.4 Local Operators

1.4.1 Noise Suppression by Mean Filtering

1.4.2 Noise Suppression by Median Filtering

1.4.3 Edge Enhancement

1.4.4 Local-Area Histogram Equalization

1.5 Operations with Multiple Images

1.5.1 Noise Suppression by Image Averaging

1.5.2 Change Enhancement by Image Subtraction

1.6 Frequency Domain Techniques

1.7 Concluding Remarks

1.8 References

1.1 Introduction

Image enhancement techniques are used to refine a given image so that desired image features become easier to perceive for the human visual system or more likely to be detected by automated image analysis systems [1, 13]. Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. This may be the case, for example, when the dynamic range of the data and that of the display are not commensurate, when the image has a high level of noise, or when contrast is insufficient [4, 5, 8, 9].

Fundamentally, image enhancement is the transformation or mapping of one image to another [10, 14]. This transformation is not necessarily one-to-one, so two different input images may transform into the same or similar output images after enhancement. More commonly, one may want to generate multiple enhanced versions of a given image. This aspect also means that enhancement techniques may be irreversible.

Often the enhancement of certain features in images is accompanied by undesirable effects. Valuable image information may be lost, or the enhanced image may be a poor representation of the original. Furthermore, enhancement algorithms cannot be expected to provide information that is not present in the original image. If the image does not contain the feature to be enhanced, noise or other unwanted image components may be inadvertently enhanced without any benefit to the user.

In this chapter we present established image enhancement algorithms commonly used for medical images. Initial concepts and definitions are presented in Section 1.2. Pixel-based enhancement techniques described in Section 1.3 are transformations applied to each pixel without utilizing specifically the information in the neighborhood of the pixel. Section 1.4 presents enhancement with local operators that modify the value of each pixel using the pixels in a local neighborhood. Enhancement that can be achieved with multiple images of the same scene is outlined in Section 1.5. Spectral domain filters that can be used for enhancement are presented in Section 1.6. The techniques described in this chapter are applicable to dental and medical images.

1.2 Preliminaries and Definitions

We define a digital image as a two-dimensional array of numbers that represent the real, continuous intensity distribution of a spatial signal. The continuous spatial signal is sampled at regular intervals, and the intensity is quantized to a finite number of levels. Each element of the array is referred to as a picture element or pixel. The digital image is defined as a spatially distributed intensity signal f (m, n), where f is the intensity of the pixel, and m and n define the position of the pixel along a pair of orthogonal axes usually defined as horizontal and vertical. We shall assume that the image has M rows and N columns and that the digital image has P quantized levels of intensity (gray levels) with values ranging from 0 to P − 1.

The histogram of an image, commonly used in image enhancement and image characterization, is defined as a vector that contains the count of the number of pixels in the image at each gray level. The histogram, h(i), can be defined as

where

A useful image enhancement operation is convolution using local operators, also known as kernels. Considering a kernel w(k, l) to be an array of (2K + 1 × 2L + 1) coefficients where the point (k, l) = (0, 0) is t...

Table des matières

• Cover Image
• Title page
• Copyright
• Foreword
• Contributors
• Preface
• Dedication
• Acknowledgments
• Table of Contents
• Part I: Enhancement
• Part II: Segmentation
• Part III: Quantification
• Part IV: Registration
• Part V: Visualization
• Part VI: Compression, Storage, and Communication
• Index