The discoveries of seminal physical phenomena such as X-rays, ultrasound, radioactivity, and magnetic resonance, and the development of imaging instruments that harness them have provided some of the most effective diagnostic tools in medicine. The medical imaging community is now able to probe into the structure, function, and pathology of the human body with a diversity of imaging systems. These systems are also used for planning treatment and surgery, as well as for imaging in biology. Data sets in two, three, or more dimensions convey increasingly vast and detailed information for clinical or research applications. This information has to be interpreted in a timely and accurate manner to benefit health care. The examination is qualitative in some cases, quantitative in others; some images need to be registered with each other or with templates, many must be compressed and archived. To assist visual interpretation of medical images, the international imaging community has developed numerous automated techniques which have their merits, limitations, and realm of application. This Handbook presents concepts and digital techniques for processing and analyzing medical images after they have been generated or digitized. It is organized into six sections that correspond to the fundamental classes of algorithms: enhancement, segmentation, quantification, registration, visualization, and a section that covers compression, storage, and communication. The last chapter describes some software packages for medical image processing and analysis.

I Enhancement

Enhancement algorithms are used to reduce image noise and increase the contrast of structures of interest. In images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. In many cases, enhancement improves the quality of the image and facilitates diagnosis. Enhancement techniques are generally used to provide a clearer image for a human observer, but they can also form a preprocessing step for subsequent automated analysis. The chapters in this section present diverse techniques for image enhancement including linear, nonlinear, fixed, adaptive, pixel-based, or multi-scale methods.

II Segmentation

Segmentation is the stage where a significant commitment is made during automated analysis by delineating structures of interest and discriminating them from background tissue. This separation, which is generally effortless and swift for the human visual system, can become a considerable challenge in algorithm development. In many cases the segmentation approach dictates the outcome of the entire analysis, since measurements and other processing steps are based on segmented regions. Segmentation algorithms operate on the intensity or texture variations of the image using techniques that include thresholding, region growing, deformable templates, and pattern recognition techniques such as neural networks and fuzzy clustering. Hybrid segmentation and volumetric segmentation are also addressed in this section.

III Quantification

Quantification algorithms are applied to segmented structures to extract the essential diagnostic information such as shape, size, texture, angle, and motion. Because the types of measurement and tissue vary considerably, numerous techniques that address specific applications have been developed. Chapters in this section cover shape and texture quantification in two- and three-dimensional data, the use of shape transformations to characterize structures, arterial tree mor-phometry, image-based techniques for musculoskeletal biomechanics, image analysis in mammography, and quantification of cardiac function. In applications where different kinds of tissue must be classified, the effectiveness of quantification depends significantly on the selection of database and image features, as discussed in this section. A comprehensive chapter covers the choices and pitfalls of image interpolation, a technique included in many automated systems and used particularly in registration.

IV Registration

Registration of two images of the same part of the body is essential for many applications where the correspondence between the two images conveys the desired information. These two images can be produced by different modalities, for example CT and MRI, can be taken from the same patient with the same instrument at different times, or can belong to two different subjects. Comparison of acquired images with digital anatomic atlas templates also requires registration algorithms. These algorithms must account for the distortions between the two images, which may be caused by differences between the imaging methods, their artifacts, soft tissue elasticity, and variability among subjects. This section explains the physical and biological factors that introduce distortions, presents various linear and nonlinear registration algorithms, describes the Talairach space for brain registration, and addresses interpolation issues inherent in registration. Chapters that describe clinical applications and brain atlases illustrate the current and potential contributions of registration techniques in medicine.

V Visualization

Visualization is a relatively new area that is contributing significantly to medicine and biology. While automated systems are good at making precise quantitative measurements, the complete examination of medical images is accomplished by the visual system and experience of the human observer. The field of visualization includes graphics hardware and software specifically designed to facilitate visual inspection of medical and biological data. In some cases such as volumetric data, visualization techniques are essential to enable effective visual inspection. This section starts with the evolution of visualization techniques and presents the fundamental concepts and algorithms used for rendering, display, manipulation, and modeling of multidimensional data, as well as related quantitative evaluation tools. Fast surface extraction techniques, volume visualization, and virtual endo-scopy are discussed in detail, and applications are illustrated in two and three dimensions.

VI Compression, Storage, and Communication

Compression, storage, and communication of medical images are related functions for which demand has recently increased significantly. Medical images need to be stored in an efficient and convenient manner for subsequent retrieval. In many cases images have to be shared among multiple sites, and communication of images requires compression, specialized formats, and standards. Lossless image compression techniques ensure that all the original information will remain in the image after compression but they do not reduce the amount of data considerably. Lossy compression techniques can produce significant savings in storage but eliminate some information from the image. This section covers fundamental concepts in medical image compression, storage and communication, and introduces related standards such as JPEG, DICOM, and HL-7. Picture archiving and communication systems (PACS) are described and techniques for preprocessing images before storage are discussed. Three chapters address lossy compression issues and one introduces an efficient three-dimensional image compression technique based on the wavelet transform.


This Handbook is the product of a relatively large international team which reflects the diversity of the medical imaging community. It has been a great privilege and pleasure for me to interact with the authors. I would like to express my most sincere thanks to the section editors, Bernie Huang, Rangaraj Rangayyan, Richard Robb, and Roger Woods, for their initiative, insight, coordination, and perseverance. The journey of the Handbook was set on its course with the guidance of two distinguished leaders who served on the advisory board of the Handbook: William Brody, president of Johns Hopkins University, and Elias Zerhouni, director of the Radiology and Radiological Science Department at Hopkins. I appreciate the vision and encouragement of Joel Claypool who initiated this Handbook at Academic Press and allowed the journey to progress smoothly in all its phases and for all involved. I also thank Julie Bolduc from Academic Press and Marty Tenney from Textbook Writers Associates for coordinating the compilation of the Handbook so effectively. My deepest gratitude goes to my wife, Lisa, and my children, Judy and Danny, for enduring and encouraging the journey graciously.

Isaac N. Bankman Johns Hopkins University

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