Catálogo de publicaciones - libros

The automated detection of lesions in the breast is important. The area of computer-aided detection (CAD) in digital mammography is devoted to developing sophisticated image analysis tools that can automatically detect breast lesions. The whole process can be viewed as a pipeline of subprocesses that are aimed at finding regions of interest (ROI) and classifying them in breast images. These processes (layers) are common to most medical imaging applications and involve image preprocessing, enhancement, segmentation, feature extraction, classification, and postprocessing for reducing false positives. There is a variety of algorithms for these processes available in medical imaging literature but little to guide their selection. There are only a few comparative studies that exhaustively compare different algorithms on large datasets and correlate the success of the algorithm with the type of data used. Most clinical studies use a preselected set of image analysis algorithms that are uniformly applied to all images. In our opinion, this practice is not good. In this chapter we demonstrate the use of a knowledge-based framework that integrates the various layers of analysis under an adaptive scheme. The main emphasis is to have at our disposal more than one algorithm per layer to produce the same type of output, and then based on the properties of the image under consideration, predict the single best algorithm to be applied at each layer from this set. We demonstrate that this scheme of work has significant advantages over a nonadaptive structure (where only one algorithm is available per layer and it is fixed for all images in the dataset).

Digital image segmentation is the process of assigning distinct labels to different objects in an image. The level of detail indicated by the labeling is related to the application at hand. To perform object identification in digital or continuous, moving or still images, humans make use of high-level reasoning and knowledge, as well as of different visual cues, such as shadowing, occlusion, parallax motion, and the relative size of objects. Aside from the difficulty of inserting this type of reasoning into a computer program, the task of segmenting out an object from its background in an image becomes particularly hard for a computer when, instead of the brightness values, what distinguishes the object from the background is some textural property, or when the image is corrupted by noise and/or inhomogeneos illumination.

Medical image analysis is an area that has always attracted the interest of engineers and basic scientists. Research in the field has been intensified in the last 15–20 years. Significant work has been done and reported for breast cancer imaging with particular emphasis on mammography. The reasons for the impressive volume of work in this field include (a) increased awareness and education of womenon the issues of early breast cancer detection and mammography, (b) the potential for significant improvements both in the fields of imaging and management, and (c) the multidisciplinary aspects of the problems and the challenge presented to both engineers and basic scientists.

Segmentation is in many cases the bottleneck when trying to use radiological image data in many clinically important applications as radiological diagnosis, monitoring, radiotherapy, and surgical planning. The availability of efficient segmentation methods is a critical issue especially in the case of large 3-D medical datasets as obtained today by the routine use of 3-D imaging methods like magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US).