Electrical Impedance Tomography (EIT)

Looking into the human body is very essential not only for studying the anatomy and physiology, but also for diagnosing a disease or illness. Doctors always try to visualize an organ or body part in order to study its physiological and anatomical status for understanding and/or treating its illness. This necessity introduced the diagnostic tool called medical imaging. The era of medical imaging started in 1895, when Roentgen discovered the magical powerful invisible rays called X-rays. Gradually the medical imaging introduced X-Ray CT, Gamma Camera, PET, SPECT, MRI, USG. Recently medical imaging field is enriched with comparatively newer tomographic imaging modalities like Electrical Impedance Tomography (EIT), Diffuse Optical Tomography (DOT), Optical Coherence Tomography (OCT), and Photoacaustic Tomography (PAT). The EIT has been extensively researched in different fields of science and engineering due to its several advantages. This chapter will present a brief review on the available medical imaging modalities and focus on the need of an alternating method. EIT will be discussed with its physical and mathematical aspects, potentials, and challenges.

Digital Image Watermarking

With the advent of telemedicine, Digital Rights Management of medical images has become a critical issue pertaining to security and privacy preservation in the medical industry. The technology of telemedicine makes patient diagnosis possible for physicians located at a remote site. This technology involves electronic transmission of medical images over the internet, thus raising the need for ensuring security and privacy of such information. Digital watermarking is a widely used technique for the authentication and protection of multimedia data such as images and video against various security and privacy threats. But such digital rights management practices as watermarking often lead to considerable distortion or information loss of the medical images. The medical images being highly sensitive and legally valuable assets of the medical industry, such information loss are often not tolerable. Most importantly, such information loss may lead to incorrect patient diagnosis or reduced accuracy of disease detection. In this chapter we investigate the impact of digital watermarking, and its effect on the accuracy of disease diagnosis, specifically diagnosis of malarial infection caused by Plasmodium vivax parasite. We have used a computer–aided, automatic diagnostic model for our work in this chapter. Our experimental results show that although general (lossy) digital watermarking reduces the diagnostic accuracy, it can be improved with the use of reversible (lossless) watermarking. In fact, the adverse effect(s) of watermarking on the diagnostic accuracy can be completely mitigated through the use of reversible watermarking.

Computer Vision Based Technique for Surface Defect Detection of Apples

The automatic inspection of quality in fruits is becoming of paramount importance in order to decrease production costs and increase quality standards. Computer vision techniques are used in fruit industry for fruit grading, sorting, and defect detection. In this chapter, we review recent approaches for automatic inspection of quality in fruits using computer vision techniques. Particularly, we focus on the review of advances in computer vision techniques for automatic inspection of quality of apples based on surface defects. Finally, we present our approach to estimate the defects on the surface of an apple using grow-cut and multi-threshold based segmentation technique. The experimental results show that our method effectively estimates the defects on the surface of apples significantly more effectively than color based segmentation technique.

3D Reconstruction Using Multiple View Stereo and a Brief Introduction to Kinect

This chapter deals with the methodology of 3D reconstruction, both sparse and dense. The basic properties of the projective geometry and the camera models are introduced to understand the preliminaries about the subject. A more detail can be found in the book (Hartley & Zisserman, 2000). The sparse reconstruction deals with reconstructing 3D points for few image points. There are gaps in the reconstructed 3D points. Dense reconstruction tries to fill up gaps and make the density of the reconstruction higher. Estimation of correspondences is an integral part of multiview reconstruction and the author will discuss the point correspondences among images here. Finally the author will introduce the Microsoft Kinect, a divice which directly capture 3D information in realtime, and will show how to enhance the Kinect point cloud using vision framework.

A Brief Review on Recent Trends in Image Restoration

This chapter gives the opportunity to get an idea of recent trends in image denoising and restoration. It relates to the present research scenario in the field of image restoration. As much as possible the newest break-through regarding the methods of denoising as well as the performance metrics of evaluation has been dealt. The assessments done by the researchers have been included first so as to know how much analysis they propose to be done with respect to the application point of view of the denoising methods. The concept behind the metric selection for the assessment and evaluation has been introduced along with the need for shifting the dependence of the research community towards the newly proposed metrics than the old ones. The new trends in image denoising have been referred duly so that the readers can directly refer to the main algorithms and techniques from the papers proposed by their authors.

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

Computational Models of Visual Attention

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

Image Representation Using a Sparsely Sampled Codebook for Super-Resolution

In this chapter, the authors propose a Super-Resolution (SR) method using a vector quantization codebook and filter dictionary. In the process of SR, we use the idea of compressive sensing to represent a sparsely sampled signal under the assumption that a combination of a small number of codewords can represent an image patch. A low-resolution image is obtained from an original high-resolution image, degraded by blurring and down-sampling. The authors propose a resolution enhancement using an alternative l1 norm minimization to overcome the convexity of l0 norm and the sparsity of l1 norm at the same time, where an iterative reweighted l1 norm minimization is used for optimization. After the reconstruction stage, because the optimization is implemented on image patch basis, an additional deblurring or denoising step is used to globally enhance the image quality. Experiment results show that the proposed SR method provides highly efficient results.

Survey of Medical Image Compression Techniques and Comparative Analysis

Today digital imaging is widely used in every application around us like Internet, High Definition TeleVision (HDTV), satellite communications, fax transmission, and digital storage of movies and more, because it provide superior resolution and quality. Recently, medical imaging has begun to take advantage of digital technology, opening the way for advanced medical imaging and teleradiology. However, medical imaging requires storing, communicating and manipulating large amounts of digital data. Applying image compression reduces the storage requirements, network traffic, and therefore improves efficiency. This chapter provides the need for medical image compression; different approaches to image compression, emerging wavelet based lossy-lossless compression techniques, how the existing recent compression techniques work and also comparison of results. After completing this chapter, the reader should have an idea of how to increase the compression ratio and at the same time maintain the PSNR level compared to the existing techniques, desirable features of standard compression techniques such as embededness and progressive transmission, how these are very useful and much needed in the interactive teleradiology, telemedicine and telebrowsing applications.

Research and Developments in Medical Image Reconstruction Methods and its Applications

Image reconstruction from projection is the field that lays the foundation for Medical Imaging or Medical Image Processing. The rapid and proceeding progress in medical image reconstruction, and the related developments in analysis methods and computer-aided diagnosis, has promoted medical imaging into one of the most important sub-fields in scientific imaging. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features and physiological functioning, free from overlying structures.In this chapter, the authors share their opinions on the research and development in the field of Medical Image Reconstruction Techniques, Computed Tomography (CT), challenges and the impact of future technology developments in CT, Computed Tomography Metrology in industrial research & development, technology, and clinical performance of different CT-scanner generations used for cardiac imaging, such as Electron Beam CT (EBCT), single-slice CT, and Multi-Detector row CT (MDCT) with 4, 16, and 64 simultaneously acquired slices. The authors identify the limitations of current CT-scanners, indicate potential of improvement and discuss alternative system concepts such as CT with area detectors and Dual Source CT (DSCT), recent technology with a focus on generation and detection of X-rays, as well as image reconstruction are discussed. Furthermore, the chapter includes aspects of applications, dose exposure in computed tomography, and a brief overview on special CT developments. Since this chapter gives a review of the major accomplishments and future directions in this field, with emphasis on developments over the past 50 years, the interested reader is referred to recent literature on computed tomography including a detailed discussion of CT technology in the references section.