The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. This new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years.
In Chapter 1 we present in detail a framework for fully automated brain tissue
classification. The framework consists of a sequence of fully automated state
of the art image registration (both rigid and nonrigid) and image segmentation
algorithms. Models of the spatial distribution of brain tissues are combined with
models of expected tissue intensities, including correction of MR bias fields and
estimation of partial voluming. We also demonstrate how this framework can
be applied in the presence of lesions....
Chapter 1 presents IVUS. Intravascular ultrasound images represent a unique
tool to guide interventional coronary procedures; this technique allows to
supervise the cross-sectional locations of the vessel morphology and to provide
quantitative and qualitative information about the causes and severity of
coronary diseases. At the moment, the automatic extraction of this kind of information
is performed without taking into account the basic signal principles
that guide the process of image generation....
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Conditional Random Field Approach to Unsupervised Texture Image Segmentation
Changes or discontinuities in an image amplitude attribute such as luminance or tristimulus value are fundamentally important primitive characteristics of an image because they often provide an indication of the physical extent of objects within the image. Local discontinuities in image luminance from one level to another are called luminance edges. Global luminance discontinuities, called luminance boundary segments, are considered in Section 17.4.
Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation is image luminance amplitude for a monochrome image and color components for a color image. Image edges and texture are also useful attributes for segmentation. The definition of segmentation adopted in this chapter is deliberately restrictive; no contextual information is utilized in the segmentation. Furthermore, segmentation does not involve classifying each segment....