Advances in Computational Techniques for Biomedical Image Analysis

Methods and Applications

Deepika Koundal, Savita Gupta

• Publisher: Academic Press
• Year: 2020
• Print ISBN: 9780128200247
• eBook ISBN: 9780128204115
• Topic: Computer Science
• Subtopic: Digital Media
• Index: Computer Science

Advances in Computational Techniques for Biomedical Image Analysis: Methods and Applications focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis. It discusses the archiving and transfer of images, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing. It examines various feature detection and segmentation techniques, together with methods for computing a registration or normalization transformation.Advances in Computational Techniques for Biomedical Image Analysis: Method and Applications is ideal for researchers and post graduate students developing systems and tools for health-care systems.- Covers various challenges and common research issues related to biomedical image analysis- Describes advanced computational approaches for biomedical image analysis- Shows how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.- Explores a range of computational algorithms and techniques, such as neural networks, fuzzy sets, and evolutionary optimization- Explores cloud based medical imaging together with medical imaging security and forensics

Section IV

Biomedical image compression and transmission

Outline

• 9 Discrete cosine transform–based compressive sensing recovery strategies in medical imaging
• 10 Segmentation-based compression techniques for medical images
• 11 Systematic survey of compression algorithms in medical imaging

9

Discrete cosine transform–based compressive sensing recovery strategies in medical imaging

Amira S. Ashour¹, Yanhui Guo², Eman Elsaid Alaa¹ and Hossam M. Kasem¹, ¹Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt, ²Department of Computer Science, University of Illinois, Springfield, IL, United States

Abstract

In medical imaging, it is crucial to reduce the exposure time of the patient to the medical modality. Medical image compression has a significant role, including telemedicine, medical imaging, and video conferencing for consultation. One of the effective compression techniques, compressive sensing (CS), defined as a hypothesis to represent the information at a rate below Nyquist-Shannon sampling rate for sparse and incoherence images/signals in a particular domain. CS effectively reduces the sampling rate without important information loses and breaks the canonical rules. Thus it has broad applications in the medical imaging for medical image compression with high image reconstructions quality and less data loss. Basically, the image is converted to the discrete cosine transform, for example, with the relief of sensing matrix to extract the essential coefficients having less dimensionality compared to the image dimensions. CS recovery strategies can be divided into greedy recovery and L1 minimization techniques, where the first depends on iteration calculations of the image’s coefficients till realizing the convergence criterion, while the second technique depends on solving a linear optimization problem. In the present chapter, several recovery strategies are applied for image reconstruction, namely

1-magic, orthogonal matching pursuit, compressive sampling matching pursuit, and CVX. Then, the performance of these techniques is studied by measuring the peak signal-to-noise ratio and the structural similarity index measure as a reconstruction quality metric. Furthermore, the impact of CS on medical imaging applicators is also deliberated. The results established the superiority of the weighted-based CVX compared to the traditional as well as the weighted-based other recovery methods.

Keywords

Compressive sensing; discrete cosine transform; orthogonal matching pursuit; CVX; L1-magic; reconstruction strategies

Chapter outline

Outline

• 9.1 Introduction 167
• 9.2 Literature review 169
• 9.3 Methodology 170
  • 9.3.1 Compressive sensing 170
  • 9.3.2 CS recovery algorithms 171
  • 9.3.3 Proposed weighted compressive sensing 173
  • 9.3.4 Performance metrics 174
• 9.4 Results and discussion 175
  • 9.4.1 Comparative study using traditional recovery schemes 175
  • 9.4.2 Comparative study using proposed weighting-based recovery methods 179
• 9.5 Conclusions 180
• References 183

9.1 Introduction

Medical images from different modalities are characterized by their outsized dimensions. From the other side, telemedicine systems for consultation with a dramatic increase in the medical information raise the need for efficient use of the transmission channel bandwidth, which depends on the size of the transmitted information. Subsequently, there is an urgent need for developing efficient compression approaches, which keep the significant details in the medical images. The main concept of the compression methods is to minimize the required number of bits by manipulating the perceptual and spatial redundancy in the image. This procedure represents the image information while preserving adequate visual image quality.

Commonly, lossy- and lossless-compression methods are the main categories for image compassion. Lossy methods, including fractal compression, and transform coding, provide high compression ratios (CRs). Nevertheless, it is difficult to restore the original image from the compressed image due to the loss of information from the original image during the compression procedure. Conversely, the lossless methods, including predictive coding, arithmeti...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Section I: Overview
• Section II: Image preprocessing and segmentation techniques
• Section III: Medical image classification and analysis
• Section IV: Biomedical image compression and transmission
• Section V: Biomedical image security
• Index