Soft computing techniques for medical image analysis
TL;DR: Different soft computing based approaches for image processing and segmentation, which can improve the diagnostic value of the images are reviewed and an architectural framework forsoft computing based application development is suggested.
Abstract: The use of soft computing techniques for medical image analysis is reviewed in this paper. These techniques involve application of fuzzy logic, neural network and evolutionary algorithms. We introduce the different imaging modalities and motivation for using soft computing techniques for medical image analysis. We review different soft computing based approaches for image processing and segmentation, which can improve the diagnostic value of the images. We also present some results with MRI images. Based on these approaches we suggest an architectural framework for soft computing based application development.
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01 Jun 2011
TL;DR: A scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue is proposed based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach.
Abstract: Magnetic resonance system generates image data, where the contrast is dependent on various parameters like proton density (PD), spin lattice relaxation time (T1), spin-spin relaxation time (T2), chemical shift, flow effect, diffusion, and perfusion. There is a lot of variability in the intensity pattern in the magnetic resonance (MR) image data due to various reasons. For example a T2 weighted image of same patient can be generated by different pulse sequence (Spin Echo, Fast Spin Echo, Inversion recovery, etc.) or on different MR system (1T, 1.5T, 3T, system, etc.) or using different RF coil system. Hence, there is a need for an adaptive scheme for segmentation, which can be modified depending on the imaging scheme and nature of the MR images. This paper proposes a scheme to automatically generate fuzzy rules for MR image segmentation to classify tissue. The scheme is based on hybrid approach of two popular genetic algorithm based machine learning (GBML) techniques, Michigan and Pittsburg approach. The proposed method uses a training data set generated from manual segmented images with the help of an expert in magnetic resonance imaging (MRI). Features from image histogram and spatial neighbourhood of pixels have been used in fuzzy rules. The method is tested for classifying brain T2 weighted 2-D axial images acquired by different pulse sequences into three primary tissue types: white matter (WM), gray matter (GM), and cerebro spinal fluid (CSF). Results were matched with manual segmentation by experts. The performance of our scheme was comparable.
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TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
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TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
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01 Mar 1996
TL;DR: The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model, and outlines network architectures and learning processes, and presents some of the most commonly used ANN models.
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TL;DR: Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches, which addresses the issue of quantitative evaluation of segmentation results.
3,527 citations