Knowledge Representation by Image Grouping on Various Criteria

This approach to implementing a knowledge-based component attempts to adap-tively determine optimum parameter settings for groups of images on the basis of image feature vectors. The feature vector is used to group images accordingly, that in turn serve as a form of a priori knowledge for use in subsequent components. In this way components may be trained to operate on particular image groupings with different parameter settings. In mammography, Zheng et al. 9 propose an adaptive computer-aided...

Mammographic Cluster Classification Single View Application

A set of 260 single-view mammograms with calcification clusters was first used for the validation of the CADiagnosis algorithm described previously. The set included 138 calcification clusters associated with benign disease that are commonly referred to as benign calcifications or clusters and 122 calcification clusters associated with cancer that are commonly referred to as malignant calcifications or clusters. All mammograms were selected from the patient files of the H. Lee Moffitt Cancer...

Novel Algorithm for Pancreatic Tumor Detection and Classification

Fuzzy-based segmentation and classification techniques have been used in various medical imaging applications although not pancreatic cancer 46-48 . An application closest to CT pancreatic imaging with analogous problems is the magnetic resonance imaging (MRI) of the brain and brain tumors. Unsupervised, supervised, and semisupervised fuzzy c-means (FCM) algorithms and knowledge-guided FCM segmentation have been successfully applied to brain tumor MRI applications 49-55 . Similar approaches...

System Weakness

The ROI is determined by looking at the overlay of the ground truth contour and the grayscale image. The center is estimated by eye and the radius of the contour Figure 9.48 Results of estimated boundary using circular- vs. elliptical-based methods. The system used was FCM based. Top rows are circular-based ROI, while the corresponding bottom rows are elliptical-based ROIs. Figure 9.48 Results of estimated boundary using circular- vs. elliptical-based methods. The system used was FCM based. Top...

Polynomial Contrast Enhancement

Let f E T be a gray-level image with T n, , imax) C R aset of rational numbers. Let U Wmin, , umax C R be a second set of rational numbers. An application is called gray-level transformation. For convenience, the gray-level transformation is constructed in such a way that it assigns 1 (u + umax) to the mean value xt of the original image f. Instead of t and u, we consider in the following the variables t and v defined by A polynomial gray-level transformation can then be defined as follows 1 (t...

Classification Algorithm

Classification was done with a three-layer, feed-forward artificial neural network ANN consisting of an input layer, one hidden layer, and an output layer. The NevProp4 backpropagation software was used in this study. NevProp4 is a general backpropagation algorithm developed by Philip H. Goodman at the University of Nevada, Reno 60 . Figure 13.15 shows a diagram of the network structure. The feature vector of the input layer consisted of 14 elements features defined in the previous stage Table...

Ra0d min Ra0c fmc d and R Ra0d1212

We now show that, for all c, d e V, c, d is consistent. To do this, we assume that there is a c, d and an m such that R am R ac and yet none of Eqs. 12.10 - 12.12 holds and show that this leads to a contradiction. A consequence of our assumption is that c d and at least one of the 1 RIad lt min , fm c, d 12.13 1 Biad min RV0c, fm c, d and a IRIad 12.14 We may assume that IRIaC gt 0 and that m c, d gt 0, for otherwise one of Eqs. 12.11 or 12.12 clearly holds. Hence 1 RIa 1 RIa ir, for some 1 lt...

Live Wire Segmentation

The first class of algorithms reviewed, which are usually referred to as Live-Wire algorithms 38 , belongs to the field of dynamic programming. The foundations of these algorithms lie in the F algorithm 39 , and will briefly be sketched here. In a Live-Wire algorithm, the image I is considered as a discrete neighborhood graph, where each pixel corresponds to a node in the graph. Generally an 8-Neighborhood N8, Moore neighborhood is defined, so that diagonal connections are allowed. A cost...

Helical CT Imaging Characteristics

CT was developed in the early 1960s with the first clinical system installed in 1971 26 . Today, there are five generations of CT scanners characterized by different scanning conditions and properties. Helical or spiral CT is the latest generation of scanners that combines a continuous rotation of the X-ray source and ring of detectors with a continuous movement of the examination table. Hence, data are acquired continuously while the patient is moved through the gantry 27, 28 . Figure 4.3...

Retinal Image Segmentation from Stereo Fundus Images

Objects such as blood vessels, optic disk, and optic cup in retinal images are crucial in monitoring and detecting the progression of retinal diseases such as vascular diseases, glaucoma hypertension, and diabetic retinopathy. b First pass segmentation background b First pass segmentation background c First pass segmentation CSF and other d First pass segmentation gray matter e First pass segmentation white matter f White matter mask Figure 6.12 DA segmentation of MS lesions from Fig. 6.11 a ....

Bibliography

1 Jemal, A., Thomas, A., and Murray, T., Cancer statistics, 2002, CA Cancer J. Clin., Vol. 52, pp. 23-47, 2002. 2 Kern, S., Tempero, M., and Conley, B., Co-Chairs , Pancreatic cancer An agenda for action, Report of the Pancreatic Cancer Progress Group, National Cancer Institute, February 2001. 3 Kuvshinoff, B. W. and Bryer, M. P., Treatment of resectable and locally advanced pancreatic cancer, Cancer Control, Vol. 7, No. 5, pp. 428-436, 2000. 4 Lin, Y., Tamakoshi, A., Kawamura, T., Inaba, Y.,...

Introduction

Vascular disease, stroke, and arterial dissection or rupture of coronary arteries are considered some of the main causes of mortality in present days. The behavior of the atherosclerotic lesions depends not only on the degree of lumen narrowing but also on the histological composition that causes that narrowing. Therefore, tissue characterization is a fundamental tool for studying and diagnosing the pathologies and lesions associated to the vascular tree. Although important, tissue...

Multiscale Computation and Integration of Filter Responses

Local structures can exist at various scales. For example, vessels and bone cortices can, respectively, be regarded as line and sheet structures with various widths. In order to make filter responses tunable to a width of interest, the derivative computation for the gradient vector and the Hessian matrix is combined with Gaussian convolution. By adjusting the standard deviation of Gaussian convolution, local structures with a specific range of widths can be enhanced. The Gaussian function is...

Subvoxel Edge Localization and Width Measurement

An adaptive directional second derivative is applied at each voxel based on its corresponding moving frame. The directional derivative is taken along the perpendicular from the voxel to the medial axis or surface. The zero-crossing points of the directional second derivatives are localized at subvoxel resolution to determine the precise region boundaries and quantitate the widths. At every voxel within the candidate regions, the directional second derivative is calculated depending on its...

Double Network Mapping Overview

The double network mapping DNM method is used to predict the target contrast enhancement using two ANNs for each enhancement method. The aim is to learn a mapping based on a set of gray-scale features FROI from a given mammogram, with a quantitative measure of segmentation performance, S. The segmentation performance is quantified following contrast enhancement, for each enhancement method m, where 1 lt m lt M from a set of M enhancement methods. The two submappings are detailed below 1....