Biomedical Image Processing with Morphology and Segmentation Methods for Medical Image Analysis

摘要

Modern three-dimensional (3-D) medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced.It has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerized medical image visualization and advances in analysis methods and computer-aided diagnosis[1],and is now,for example,a vital part of the early detection,diagnosis, and treatment of cancer.The challenge is to effectively process and analyze the images in order to effectively extract, quantify,and interpret this information to gain understanding and insight into the structure and function of the organs being imaged.The general goal is to understand the information and put it to practical use.A multitude of diagnostic medical imaging systems are used to probe the human body.They comprise both microscopic (viz. cellular level) and macroscopic (viz.organ and systems level) modalities.Interpretation of the resulting images requires sophisticated image processing methods that enhance visual interpretation and image analysis methods that provide automated or semi-automated tissue detection,measurement, and characterization [2–4].In general,multiple transformations will be needed in order to extract the data of interest from an image,and a hierarchy in the processing steps will be evident, e.g., enhancement will precede restoration,which will precede analysis,feature extraction,and classification[5].Often,these are performed sequentially, but more sophisticated tasks will require feedback of parameters to preceding steps so that the processing includes a number of iterative loops.Segmentation is one of the key tools in medical image analysis.The objective of segmentation is to provide reliable, fast, and effective organ delineation.While traditionally, particularly in computer vision, segmentation is seen as an early vision tool used for subsequent recognition, in medical imaging the opposite is often true. Recognition can be performed interactively by clinicians or automatically using robust techniques, while the objective of segmentation is to precisely delineate contours and surfaces. This can lead to effective techniques known as “intelligent scissors” in 2D and their equivalent in 3D. This paper divided as follows. starts off with a more “philosophical” section setting the background for this study. We argue for a segmentation context where high-level knowledge, object information, and segmentation method are all separate. we survey in some detail a number of segmentation methods that are well-suited to image analysis, in particular of medical images.We illustrate this, make some comparisons and some recommendations. we introduce very recent methods that unify many popular discrete segmentation methods and we introduce a new technique. we give some remarks about recent advances in seeded, globally optimal active contour methods that are of interest for this study. we compare all presented methods qualitatively. We then conclude and give some indications for future work. Nonlinear filtering techniques are becoming increasingly important in image processing applications, and are often better than linear filters at removing noise without distorting image features. However, design and analysis of nonlinear filters are much more difficult than for linear filters. One structure for designing nonlinear filters is mathematical morphology, which creates filters based on shape and size characteristics. Morphological filters are limited to minimum and maximum operations that introduce bias into images. This precludes the use of morphological filters in applications where accurate estimation of the true gray level is necessary.This work develops two new filtering structures based on mathematical morphology that overcome the limitations of morphological filters while retaining their emphasis on shape. The linear combinations of morphological filters eliminate the bias of the standard filters, while the value-and-criterion filters allow a variety of linear and nonlinear operations to be used in the geometric structure of morphology. One important value-and-criterion filter is the Mean of Least Variance (MLV) filter, which sharpens edges and provides noise smoothing equivalent to linear filtering. To help understand the behavior of the new filters, the deterministic and statistical properties of the filters are derived and compared to the properties of the standard morphological filters. In addition, new analysis techniques for nonlinear filters are introduced that describe the behavior of filters in the presence of rapidly fluctuating signals, impulsive noise, and corners. The corner response analysis is especially informative because it quantifies the degree to which a filter preserves corners of all angles.Examples of the new nonlinear filtering techniques are given for a variety of medical images, including thermographic, magne