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FIGURE 10 Image segmentation using correlation mapping. (A) First image in a sequence of 60 temporal images with 3x3 pixel ROIs drawn in tumor and normal area; (B) plot of the average intensity of the reference ROI (tumor) and the normal ROI for 60 images in a sequence; (C) correlation map of the tumor See also Plate 5.

segmentation and visualization tools for temporal sequences of images. They are particularly useful for evaluation of disease processes, drug treatments, or radiotheraphy results.

7 Other Techniques

Combined (hybrid) strategies have also been used in many applications. Here are some examples: Kapur etal. [58] present a method for segmentation of brain tissue from magnetic resonance images that combines the strengths of three techniques: single-channel expectation/maximization segmentation, binary mathematical morphology, and active contours models. Masutani et al. [73] segment cerebral blood vessels on MRA images using a model-based region growing, controlled by morphological information of local shape. A hybrid strategy [3] that employs image processing techniques based on anisotropic filters, thresholding, active contours, and a priori knowledge of the segmentation of the brain is discussed in Chapter 11.

Many segmentation techniques developed originally for two-

dimensional images can be extended to three dimensions — for example, region growing, edge detection, or multispectral segmentation [12,19,21,90,125]. 3D segmentation combined with 3D rendering allows for more comprehensive and detailed analysis of image structures than is possible in a spatially limited single-image study. A number of 3D segmentation techniques can be found in the literature, such as 3D connectivity algorithm with morphological interpolation [57], 3D matching of deformable models [70], 3D edge detection [78], coupled surfaces propagation using level set methods [131], and a hybrid algorithm based on thresholding, morphological operators, and connected component labeling [46,100]. Several volumetric segmentation algorithms are discussed in Chapter 12, where their accuracy is compared using digital MR phantoms. Partial volume segmentation with voxel histograms is presented in Chapter 13.

There has been great interest in building digital volumetric models (3D atlases) that can be used as templates, mostly for the MR segmentation of the human brain [23,47,61]. A modelbased segmentation is achieved by using atlas information to guide segmentation algorithms. In the first step, a linear registration is determined for global alignment of the atlas with the image data. The linear registration establishes corresponding regions and accounts for translation, rotation and scale differences. Next, a nonlinear transform (such as elastic warping, [5]) is applied to maximize the similarity of these regions.

Warfield et al. [122,123] developed a new, adaptive, template-moderated, spatially varying, statistical classification algorithm. The algorithm iterates between a classification step to identify tissues and an elastic matching step to align a template of normal anatomy with the classified tissues. Statistical classification based upon image intensities has often been used to segment major tissue types. Elastic registration can generate a segmentation by matching an anatomical atlas to a patient scan. These two segmentation approaches are often complementary. Adaptive, template moderated, spatially varying, statistical classification integrates these approaches, avoiding many of the disadvantages of each technique alone, while exploiting the combination. The algorithm was applied to several segmentation problems, such as quantification of normal anatomy (MR images of brain and knee cartilage) and pathology of various types (multiple sclerosis, brain tumors, and damaged knee cartilage). In each case, the new algorithm provided a better segmentation than statistical classification or elastic matching alone.

Figure 11 shows an example of 3D segmentation of normal and pathological brain tissues. The tumor segmentation was carried out with the algorithm of Kaus et al. [60]. This visualization was used to support preoperative surgical planning for tumor resection.

In some medical images, regions that have similar average intensities are visually distinguishable because they have different textures. In such cases, the local texture can be

FIGURE 11 Rendering of 3D anatomical models and 2D MRI cross-sections of a patient with a meningioma. The models of the skin surface, the brain, and the tumor (green) are based on automatically segmented 3D MRI data. The precentral gyrus (yellow) and the corticospinal tract (blue) are based on a previously aligned digital brain atlas [61]. See also Plate 6. (Courtesy of Drs. Ron Kikinis, Michael Kaus, and Simon Warfield, Surgical Planning Lab, Department of Radiology, Brigham and Women's Hospital, Boston.)

FIGURE 11 Rendering of 3D anatomical models and 2D MRI cross-sections of a patient with a meningioma. The models of the skin surface, the brain, and the tumor (green) are based on automatically segmented 3D MRI data. The precentral gyrus (yellow) and the corticospinal tract (blue) are based on a previously aligned digital brain atlas [61]. See also Plate 6. (Courtesy of Drs. Ron Kikinis, Michael Kaus, and Simon Warfield, Surgical Planning Lab, Department of Radiology, Brigham and Women's Hospital, Boston.)

quantified using techniques described in Chapters 14 and 15. Each pixel can be assigned a texture value and the image can be segmented using texture instead of intensity [6,79].

Fuzzy clustering, which provides another approach for segmentation of two-dimensional or multispectral images, is discussed in Chapter 6. Segmentation has also been addressed with neural networks in several applications [2,28,37,40,69,82,101,132]. The use of neural networks for segmentation is illustrated in Chapter 7. The family of active contour (snakes, deformable templates) algorithms that have been widely used for medical image segmentation [74, 95, 128, 129] is presented in Chapter 8, shape constraints for deformable models are discussed in Chapter 9 and gradient vector flow deformable models are explained in Chapter 10.

8 Concluding Remarks

Segmentation is an important step in many medical applications involving measurements, 3D visualization, registration, and computer-aided diagnosis. This chapter was a brief introduction to the fundamental concepts of segmentation and methods that are commonly used.

Selection of the "correct" technique for a given application is a difficult task. Careful definition of the goals of segmentation is a must. In many cases, a combination of several techniques may be necessary to obtain the segmentation goal. Very often integration of information from many images (acquired from different modalities or over time) helps to segment structures that otherwise could not be detected on single images.

As new and more sophisticated techniques are being developed, there is a need for objective evaluation and quantitative testing procedures [17,20,26]. Evaluation of segmentation algorithms using standardized protocols will be useful for selection of methods for a particular clinical application.

Clinical acceptance of segmentation techniques depends also on ease of computation and limited user supervision. With the continued increases in computer power, the automated realtime segmentation of multispectral and multidimensional images will become a common tool in clinical applications.


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Essentials of Human Physiology

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This ebook provides an introductory explanation of the workings of the human body, with an effort to draw connections between the body systems and explain their interdependencies. A framework for the book is homeostasis and how the body maintains balance within each system. This is intended as a first introduction to physiology for a college-level course.

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