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Several neuropathologies of the central nervous system such as multiple sclerosis (MS), schizophrenia, epilepsy, Alzheimer, and Creutzfeldt-Jakob disease (CJD) are related to morphological and/or functional changes in the brain. Studying such diseases by objectively measuring these changes instead of assessing the clinical symptoms is of great social and economical importance. These changes can be measured in three dimensions in a noninvasive way using current medical imaging modalities. Magnetic resonance imaging (MRI), in particular, is well suited for studying diseases of the nervous system due to its high spatial resolution and the inherent high soft tissue contrast.

Vascular disease, stroke, and arterial dissection or rupture of coronary arteries are considered some of the main causes of mortality in present days. The behavior of the atherosclerotic lesions depends not only on the degree of lumen narrowing but also on the histological composition that causes that narrowing. Therefore, tissue characterization is a fundamental tool for studying and diagnosing the pathologies and lesions associated to the vascular tree.

Detection, localization, diagnosis, staging, and monitoring treatment responses are the most important aspects and crucial procedures in diagnostic medicine and clinical oncology. Early detection and localization of the diseases and accurate disease staging can improve the survival and change management in patients prior to planned surgery or therapy. Therefore, current medical practice has been directed toward early but efficient localization and staging of diseases, while ensuring that patients would receive the most effective treatment.

Pancreatic cancer is the fourth leading cause of cancer deaths in the United States but only the tenth site for new cancer cases (estimated at 30,300 in 2002) [ 1 , 2 ]. The reason for this major difference is that there are no clear early symptoms of pancreatic cancer and no screening procedures or screening policy for this disease. It is usually diagnosed at a late stage and has a poor prognosis with a 1-year survival rate of 20% and a 5-year survival rate of less than 5% [ 3 ]. Complete surgical resection is the only way to significantly improve prognosis and possibly lead to a cure. Unfortunately, only 15–20% of the patients can undergo resection with median survival rates from 12 to 19 months and a 5-year survival rate of 15–20%. A large majority of patients with pancreatic cancer receives palliative care or may follow a therapeutic approach the impact of which is currently quite limited and difficult to assess quantitatively [ 3 ].

Assessment and characterization of endothelial function in the diagnosis of cardiovascular diseases is a current clinical research topic [ 1 , 2 ]. The endothelium shows measurable responses to flow changes [ 3 , 4 ], and flow-mediated dilation (FMD) may therefore be used for assessing endothelial health; B-mode ultrasonography (US) is a cheap and noninvasive way to estimate this dilation response [ 5 ]. However, complementary computerized image analysis techniques are still very desirable to give accuracy and objectivity to the measurements [ 1 ].

Image analysis techniques have been broadly used in computer-aided medical analysis and diagnosis in recent years. Computer-aided image analysis is an increasingly popular tool in medical research and practice, especially with the increase of medical images in modality, amount, size, and dimension. Image segmentation, a process that aims at identifying and separating regions of interests from an image, is crucial in many medical applications such as localizing pathological regions, providing objective quantative assessment and monitoring of the onset and progression of the diseases,as well as analysis of anatomical structures.

Medical image processing is the meeting of two sciences that behave in completely different ways. While medicine is a science where experience plays a majors role and where the practical use is evident, image processing—as a derivative of applied mathematics—is a more theoretical discipline. Hence, the conditions of this meeting need to be analyzed sophisticatedly; not everything possible to implement is useful, and not everything useful is possible to implement.

Advanced atherosclerotic plaque can lead to complications such as vessel lumen stenosis, thrombosis, and embolization, which are the leading causes of death and major disability among adults in the United States. To reduce the healthcare costs, improved methods of diagnosis, treatment, and prevention of these kinds of diseases are very important [ 1 ].

The importance of plaque component classi .cation and vessel wall quantification has been well established by several research groups (see Refs. [ 1 – 30 ]).

With high-resolution three-dimensional (3-D) imaging modalities becoming commonly available in medical imaging, a strong need has arisen for a means of accurate extraction and 3D quantification of the anatomical structures of interest from acquired volume data. Three-dimensional local structures have been shown to be useful for 3-D modeling of anatomical structures to improve their extraction and quantification [ 1 – 16 ]. In this chapter, we describe an approach to enhancement, description, and quantification of the anatomical structures characterized by second-order 3D local structures, that is, line, sheet, and blob structures.