Dynamic Contrast Enhancement

The aim of this chapter is to provide an overview about models and methodologies used for the Dynamic Contrast Enhancement (DCE) analysis. DCE is a non-invasive methodology aimed to diagnostic the nature of a lesion on the base of the perfusion’s dynamic of specific contrast agents. The idea at the base of DCE is that, in several pathological tissues, including tumors and inflammatory diseases, the angiogenic process is abnormal, hence the characterization of vascularisation structure may be used to support the diagnosis. In this chapter, we will describe the basic DCE procedures and introduce some of its most innovative evolution based on the pharmacokinetic analysis technique (PK), and the empirical model (EM). Even if DCE is still a medical research topic, there is large interest for this type of approach in biomedical applications as witnessed by the availability of specific tools in the last generation top-class US, CT and MR machines.

Diffusion Tensor Imaging and Fiber Tractography

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain structures and neural connectivity. DTI measures are thought to be representative of brain tissue microstructure and are particularly useful for examining organized brain regions, such as white matter tract areas. DTI measures the water diffusion tensor using diffusion weighted pulse sequences which are sensitive to microscopic random water motion. The resulting diffusion weighted images (DWI) display and allow quantification of how water diffuses along axes or diffusion encoding directions. This can help to measure and quantify the tissue’s orientation and structure, making it an ideal tool for examining cerebral white matter and neural fiber tracts. In this chapter the authors discuss the theoretical aspects of DTI, the information that can be extracted from DTI data, and the use of the extracted information for the reconstruction of fiber tracts and the diagnosis of a disease. In addition, a review of known fiber tracking algorithms is presented.

Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI

Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water molecules in a biological tissue. Although originally proposed for the characterization of cerebral white matter connectivity and pathologies, its implementation has extended to many other areas of the human body. In a parallel development, a number of diffusion models have been proposed in order to extract the underlying tissue microstructural properties from the diffusion MRI signal. The present study reviews the basic considerations that have to be taken into account in the selection of the diffusion encoding parameters in diffusion MRI acquisition. Both diffusion tensor imaging (DTI) and high-order schemes are reviewed. The selection of these parameters relies strongly on requirements of the adopted diffusion model and the diffusion characteristics of the tissue under study. The authors review several successful parameter selection strategies for the imaging of the human brain, and conclude with the basics of parameter optimization on promising applications of the technique on other tissues, such as the spinal cord, the myocardium, and the skeletal muscles.

Computational Methods and Tools for Decision Support in Biomedicine

The automation of diagnostic tools and the increasing availability of extensive medical datasets in the last decade have triggered the development of new analytical methodologies in the context of biomedical informatics. The aim is always to explore a problem’s feature space, extract useful information and support clinicians in their time, volume, and accuracy demanding decision making tasks. From simple summarizing statistics to state-of-the-art pattern analysis algorithms, the underlying principles that drive most medical problems show trends that can be identified and taken into account to improve the usefulness of computerized medicine to the field-clinicians and ultimately to the patient. This chapter presents a thorough review of this field and highlights the achievements and shortcomings of each family of methods. The authors’ effort has been focused on methodological issues as to generalize useful conclusions based on the large number of notable, yet case-specific developments presented in the field.

Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer

The objective of this chapter is to propose a quantitative hysteroscopy imaging analysis system in gynaecological cancer and to provide the current situation about endoscopy imaging. Recently works, involves endoscopy, gastroendoscopy, and colonoscopy imaging with encouraging results. All the methods are using image processing using texture and classification algorithms supporting the physician diagnosis. But none of the studies were involved with the pre-processing module. Also, the above studies are trying to identify tumours in the organs and no of the are investigates the tissue texture. The system supports a standardized image acquisition protocol that eliminates significant statistical feature differences due to viewing variations. In particular, the authors provide a standardized protocol that provides texture features that are statistically invariant to variations to sensor differences (color correction), angle and distance to the tissue. Also, a Computer Aided Diagnostic (CAD) module that supports the classification of normal vs abnormal tissue of early diagnosis in gynaecological cancer of the endometrium is discussed. The authors investigate texture feature variability for the aforementioned targets encountered in clinical endoscopy before and after color correction. For texture feature analysis, three different features sets were considered: (i) Statistical Features, (ii) Spatial Gray Level Dependence Matrices, and (iii) Gray Level Difference Statistics. Two classification algorithms, the Probabilistic Neural Network and the Support Vector Machine, were applied for the early diagnosis of gynaecological cancer of the endometrium based on the above texture features. Results indicate that there is no significant difference in texture features between the panoramic and close up views and between different camera angles. The gamma correction provided an acquired image that was a significantly better approximation to the original tissue image color. Based on the texture features, the classification algorithms results show that the correct classification score, %CC=79 was achieved using the SVM algorithm in the YCrCb color system with the combination of the SF and GLDS texture feature sets. This study provides a standardized quantitative image analysis protocol for endoscopy imaging. Also the proposed CAD system gave very satisfactory and promising results. Concluding, the proposed system can assist the physician in the diagnosis of difficult cases of gynaecological cancer, before the histopathological examination.

Automatic Correspondence Methods towards Point-Based Medical Image Registration

The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image registration. Numerous point correspondence methods have been proposed, each exhibiting its own characteristics, strengths and weaknesses. This chapter presents a comparative study of four automatic point correspondence methods. The four featured methods are the Automatic Extraction of Corresponding Points approach, the Trimmed Iterated Closest Points scheme, the Correspondence by Sensitivity to Movement technique and the Self-Organizing Maps network. All methods are presented, mainly focusing on their distinct characteristics. An extensive set of dental images, subject to unknown transformations, was employed for the qualitative and quantitative evaluation of the four methods, which was performed in terms of registration accuracy. After assessing all methods, it was deduced that the Self-Organizing Maps approach outperformed in most cases the other three methods in comparison.

Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images

In this chapter the state of the art is presented in the domain of automatic identification and classification of bodies on the basis of their deformed images obtained via microscope. The approach is illustrated by means of the case of automatic recognition of third-stage larvae from microscopic images of them in high deformation instances. The introduced methodology incorporates elements of elasticity theory, image processing, curve fitting and clustering methods; a concise presentation of the state of the art in these fields is given. Combining proper elements of these disciplines, we first evaluate the undeformed shape of a parasite given a digital image of a random parasite deformation instance. It is demonstrated that different orientations and deformations of the same parasite give rise to practically the same undeformed shape when the methodology is applied to the corresponding images, thus confirming the consistency of the approach. Next, a pattern recognition method is introduced to classify the unwrapped parasites into four families, with a high success rate. In addition, the methodology presented here is a powerful tool for the exact evaluation of the mechano-elastic properties of bodies from images of their deformation instances.

Developments and Advances in Biomedical Functional Infrared Imaging

This chapter presents an overview on recent developments in the field of clinical applications of the functional infrared imaging. The functional infrared imaging is a relatively recent imaging methodology introduced for the study of the functional properties and alterations of the human thermoregulatory system for biomedical purposes. The methodology is based on the modeling of the bio-heat exchange processes and the recording of thermal infrared data by means of advanced technology. Some innovative applications of functional infrared imaging to diagnostics, psychometrics, stress measurements and psycho-neurophysiology will be presented, with special emphasis to the potentialities and the capabilities that such technique may bring to biomedical investigations.

Analysis and Quantification of Motion within the Cardiovascular System

The estimation of motion of the myocardial and arterial wall is important for the quantification of tissue elasticity and contractility and has gained attention as a determinant of cardiovascular disease. In this chapter, a review is attempted regarding the analysis and quantification of motion within the cardiovascular system from sequences of images. The main sources of cardiovascular wall motion include blood pressure, blood flow and tethering to surrounding tissue. The most commonly applied techniques for cardiovascular motion analysis include feature-based and pixel-based methodologies; the latter further include block matching, optical flow and registration techniques. Two distinct paradigms based on these methodologies are highlighted, namely myocardium and carotid artery wall motion. The current status of research in these areas is reviewed and future research directions are indicated.

Segmentation Methods in Ultrasound Images

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.