By Joo-Hwee Lim, Sim-Heng Ong, Wei Xiong

A accomplished consultant to realizing and analyzing electronic pictures in clinical and useful applications

Biomedical picture Understanding specializes in snapshot realizing and semantic interpretation, with transparent introductions to comparable thoughts, in-depth theoretical research, and exact descriptions of significant biomedical functions. It covers photograph processing, snapshot filtering, enhancement, de-noising, recovery, and reconstruction; photograph segmentation and have extraction; registration; clustering, trend type, and knowledge fusion.

With contributions from specialists in China, France, Italy, Japan, Singapore, the uk, and the USA, Biomedical picture Understanding: 

  • Addresses movement monitoring and knowledge-based platforms, parts which aren't lined largely somewhere else in a biomedical context
  • Describes vital medical functions, resembling digital colonoscopy, ocular illness prognosis, and liver tumor detection
  • Contains twelve self-contained chapters, each one with an creation to simple innovations, rules, and strategies, and a case examine or application

With over a hundred and fifty diagrams and illustrations, this bookis a vital source for the reader attracted to quickly advancing examine and purposes in biomedical photo understanding.

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Extra resources for Biomedical Image Understanding: Methods and Applications

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3. Optical Flow. Optical flow is commonly used as a feature in motion-based segmentation and tracking applications. Popular techniques for computing optical flow include those given in References [127–131]. 4. Texture. Texture is to measure the change of intensity of a surface with smoothness and regularity. Compared to color, texture requires a processing step to generate the descriptors, such as gray-level cooccurrence matrices (GLCMs) [132], Law’s texture measures [133], wavelets [134] , and steerable pyramids [135].

In generative models, the estimates of priors and pdfs are used in place of the true densities. Some density estimates are parametric, such as linear discriminant classifier (LDC) and quadratic discriminant classifier (QDC) [180]. The others are nonparmateric, for example, k-nearest neighbor (KNN) rule and the Parzen classifier [15]. 2. 4 Taxonomy of Classifiers Intuitive approach (based on concept of similarity) Probabilistic approach (based on Bayes decision rule) [179] Geometric approach (to construct decision boundaries) Template matching [176] Nearest mean classifier [168] 1-nearest neighbor rule [177, 178] k-nearest neighbor classifier [15] … Parametric methods Nonparametric methods Linear methods Nonlinear methods Linear discriminant classifier [180] Quadratic discriminant classifier [180] … Parzen windows classifier [15] … Linear support vector machine (SVM) [185] Single-layer perceptron neural network [181] … Kernel-SVM [186] Multilayer perceptron neural network [172] Radial basis network [172] Decision tree [182, 184] … Source: From Reference [164] The third approach is to construct decision boundaries by minimizing certain error criterion.

The authors propose a method to estimate the Parzen windows, which are used to analytically represent parametrized marginal and joint histograms and hence the NMI and its derivative. They also provide theoretical analysis and experimental comparisons of the performance of the designed kernel and the B-spline. The proposed registration method is applied to magnetic resonance image-guided efficient interventional therapy of liver tumors using microwave thermocoagulation. As closed-formed derivatives can be derived, the histograms and hence the NMI can be readily computed, gradient-based optimization methods can be used and this results in 50% less computation costs and hence much faster registration.

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