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Volumetric analysis of liver metastases in computed tomography with the fuzzy C-means algorithm.

Tumor size is often determined from computed tomography (CT) images to assess disease progression. A study was conducted to demonstrate the advantages of the fuzzy C-means (FCM) algorithm for volumetric analysis of colorectal liver metastases in comparison with manual contouring. Intra-and interobserver variability was assessed for manual contouring and the FCM algorithm in a study involving contrast-enhanced helical CT images of 43 hypoattenuating liver lesions from 15 patients with a history of colorectal cancer. Measurement accuracy and interscan variability of the FCM and manual methods were assessed in a phantom study using paraffin pseudotumors. In the clinical imaging study, intra-and interobserver variability was reduced using the FCM algorithm as compared with manual contouring (P = 0.0070 and P = 0.0019, respectively). Accuracy of the measurement of the pseudotumor volume was improved using the FCM method as compared with the manual method (P = 0.047). Interscan variability of the pseudotumor volumes was measured using the FCM method as compared with the manual method (P = 0.04). The FCM algorithm volume was highly correlated with the manual contouring volume (r = 0.9997). Finally, the shorter time spent in calculating tumor volume using the FCM method versus the manual contouring method was marginally statistically significant (P = 0.080). These results suggest that the FCM algorithm has substantial advantages over manual contouring for volumetric measurement of colorectal liver metastases from CT.

Algorithms↗

PubMed1999

Kinetic parameter estimation from compartment models using a genetic algorithm.

Kinetic parameters were estimated from a three-compartment fluorodeoxyglucose model with three rate constants using a genetic algorithm. The performance of the genetic algorithm was investigated by simulation studies, in which brain time-activity data (TAD) were generated using cited mean values of rate constants and the plasma TAD obtained from positron emission tomographic studies. The accuracy of kinetic parameter estimation using the genetic algorithm was compared with that using the non-linear least-squares (NLSQ) method. The margin of error in the parameters estimated using the genetic algorithm tended to be smaller than that obtained by the NLSQ method. Although not statistically significant at a noise level of 5% in the brain TAD, the difference between the two methods became significant for all parameters at a noise level of 15% or higher. Our results suggest that the genetic algorithm is a promising means of estimating kinetic parameters from compartment models, because it is more robust against statistical noise than the NLSQ method and it can be rendered highly parallel for processing.

Algorithms↗

PubMed2004

Accuracy of commercially available processing algorithms for planar radionuclide ventriculography using data for a dynamic left ventricular phantom.

BACKGROUND: Automatic and semi-automatic algorithms to calculate ejection fraction (EF) from planar radionuclide ventriculography (PRV) have been used for many years in nuclear medicine. Validation of these algorithms is scarce and often performed on outdated versions of the software. Nevertheless, clinical trials where PRV is being used as the 'gold standard' for EF are numerous. Because of the importance attributed to the EF calculated by these programs, the accuracy of the resulting EF was assessed with a dynamic left ventricular physical phantom. METHODS: A dynamic left ventricular phantom was used to simulate 21 combinations of various ejection fractions (7-66%) and end diastolic volumes (27-290 ml). For each combination, a planar radionuclide ventriculograph was acquired, converted to an interfile format and transferred into processing stations with 10 different contemporaneously available commercial algorithms. The gold standard was the 'real' EF of the phantom, derived from the exact volume of the ventricle in end diastolic and end systolic position. Correlation and Bland-Altman analysis was performed between the real EF and the calculated EF. RESULTS: The correlation for all data was excellent (r=0.98), the mean difference was very acceptable (0.98%). Nevertheless, Bland-Altman analysis showed a significant trend in the difference between real and calculated EF, with a growing underestimation for higher ranges of EF, due to an overestimation of background in larger volumes compared to smaller ones. CONCLUSION: The determination of EF from PRV, calculated with commercially available algorithms, correlates closely to the real EF of a dynamic left ventricular phantom. This phantom can be used in the development and validation of algorithms for PRV studies, in software audits and in quality assurance procedures.

Algorithms↗

PubMed1997

Evaluation of a screening algorithm for the diagnosis of genital infections with Neisseria gonorrhoeae and Chlamydia trachomatis among female sexworkers in Bénin.

BACKGROUND AND OBJECTIVES: In developing countries, simple and cheap procedures for the diagnosis of sexually transmitted diseases (STDs) are urgently needed, especially for screening purposes in high risk groups. GOALS: To evaluate the sensitivity and specificity of a screening algorithm for STDs among 364 female sex workers in Bénin, in comparison with reference laboratory tests. STUDY DESIGN: The algorithm relied on the following criteria, which were evaluated in sequence: the presence of endocervical mucopus on visual inspection of the cervix, a positive swab test, or a microscopic examination of vaginal fluid showing more than 10 polymorphonuclear cells per field. The algorithm diagnosed an infection if any one of these criteria was fulfilled. True infectious status was determined by the combined results of culture for Neisseria gonorrhoeae, enzyme immunoassay for Chlamydia trachomatis, and polymerase chain reaction assays for both infections. RESULTS: Gonococcal or chlamydial infection was diagnosed in 39.8% of the study population according to the reference tests. The algorithm had a sensitivity of 57.9% and a specificity of 61.2%. In the presence of Candida sp or Trichomonas vaginalis, specificity decreased to 39.1%, but sensitivity increased to 67.5%. CONCLUSIONS: These results underscore the limitations of simple, nonlaboratory diagnostic tools for screening STDs in high-risk groups in developing countries. Further research is needed to increase the validity--especially the sensitivity--of these algorithms.

Algorithms↗

PubMed2002

Identifying sources of variability in scoliosis classification using a rule-based automated algorithm.

STUDY DESIGN: Use of a rule-based automated algorithm to determine sources of variability in radiographic classification. OBJECTIVES: To determine whether unambiguous rules encoded in a computer program would ensure reliable classification. SUMMARY OF BACKGROUND DATA: Reliability problems have been identified in classifications used in surgical planning for patients with thoracic idiopathic scoliosis, but the sources of unreliability are not understood. METHODS: Objective classification methodology was tested on the King et al (1983) scheme. There were two novel components: 1) positions of the corners of vertebrae in radiographs were digitized relative to a defined axis system and used in automated evaluation of spinal shape parameters required for classification; and 2) the assignment of a classification was done with a rule-based algorithm. The algorithm was implemented after some ambiguities and absence of precise definitions in the King et al classification scheme had been resolved. The algorithm was tested with radiographs of patients having adolescent idiopathic scoliosis. RESULTS: The automated procedure could encounter reliability problems in cases in which a lumbar curve was very close to crossing the midline, thoracic and lumbar curves were of approximately equal value, when the apex level in the thoracolumbar region was ambiguous, when a Cobb angle was close to 10 degrees, or when the flexibility index was close to unity. CONCLUSION: Objective measurements and rule-based algorithms can eliminate some sources of interobserver and intraobserver errors in classification of spinal deformity. When classification parameters fall close to the boundaries for classification, reliability problems will persist.

Algorithms↗

PubMed2006

An examination of the reliability of a classification algorithm for subgrouping patients with low back pain.

STUDY DESIGN: Test-retest design to examine interrater reliability. OBJECTIVE: Examine the interrater reliability of individual examination items and a classification decision-making algorithm using physical therapists with varying levels of experience. SUMMARY OF BACKGROUND DATA: Classifying patients based on clusters of examination findings has shown promise for improving outcomes. Examining the reliability of examination items and the classification decision-making algorithm may improve the reproducibility of classification methods. METHODS: Patients with low back pain less than 90 days in duration participating in a randomized trial were examined on separate days by different examiners. Interrater reliability of individual examination items important for classification was examined in clinically stable patients using kappa coefficients and intraclass correlation coefficients. The findings from the first examination were used to classify each patient using the decision-making algorithm by clinicians with varying amounts of experience. The reliability of the classification algorithm was examined with kappa coefficients. RESULTS: A total of 123 patients participated (mean age 37.7 [+/-10.7] years, 44% female), 60 (49%) remained stable between examinations. Reliability of range of motion, centralization/peripheralization judgments with flexion and extension, and the instability test were moderate to excellent. Reliability of centralization/peripheralization judgments with repeated or sustained extension or aberrant movement judgments were fair to poor. Overall agreement on classification decisions was 76% (kappa = 0.60, 95% confidence interval 0.56, 0.64), with no significant differences based on level of experience. CONCLUSION: Reliability of the classification algorithm was good. Further research is needed to identify sources of disagreements and improve reproducibility.

Adolescent↗

PubMed2000

Fast quantum search algorithms in protein sequence comparisons: quantum bioinformatics.

Quantum search algorithms are considered in the context of protein sequence comparison in bioinformatics. Given a sample protein sequence of length m (i.e., m residues), the problem considered is to find an optimal match in a large database containing N residues. Initially, Grover's quantum search algorithm is applied to a simple illustrative case-namely, where the database forms a complete set of states over the 2(m) basis states of a m qubit register, and thus is known to contain the exact sequence of interest. This example demonstrates explicitly the typical O(square root of [N]) speedup on the classical O(N) requirements. An algorithm is then presented for the (more realistic) case where the database may contain repeat sequences, and may not necessarily contain an exact match to the sample sequence. In terms of minimizing the Hamming distance between the sample sequence and the database subsequences the algorithm finds an optimal alignment, in O(square root of [N]) steps, by employing an extension of Grover's algorithm, due to Boyer et al. for the case when the number of matches is not a priori known.

Algorithms↗

PubMed2004

A subgroup algorithm to identify cross-rotation peaks consistent with non-crystallographic symmetry.

Molecular replacement (MR) often plays a prominent role in determining initial phase angles for structure determination by X-ray crystallography. In this paper, an efficient quaternion-based algorithm is presented for analyzing peaks from a cross-rotation function in order to identify model orientations consistent with proper non-crystallographic symmetry (NCS) and to generate proper NCS-consistent orientations missing from the list of cross-rotation peaks. The algorithm, CRANS, analyzes the rotation differences between each pair of cross-rotation peaks to identify finite subgroups. Sets of rotation differences satisfying the subgroup axioms correspond to orientations compatible with the correct proper NCS. The CRANS algorithm was first tested using cross-rotation peaks computed from structure-factor data for three test systems and was then used to assist in the de novo structure determination of dihydrofolate reductase-thymidylate synthase (DHFR-TS) from Cryptosporidium hominis. In every case, the CRANS algorithm runs in seconds to identify orientations consistent with the observed proper NCS and to generate missing orientations not present in the cross-rotation peak list. The CRANS algorithm has application in every molecular-replacement phasing effort with proper NCS.

Algorithms↗

PubMed1989

Development and evaluation of spectral classification algorithms for fluorescence guided laser angioplasty.

Laser angioplasty, or the ablation of atherosclerotic plaque using laser energy, has tremendous potential to expand the scope of nonsurgical treatment of obstructive vascular disease. Clinical laser angioplasty, however, has been hindered by an unacceptable risk of vessel perforation. Laser-induced fluorescence spectroscopy can discriminate atherosclerotic from normal artery and may therefore be capable of guiding selective plaque ablation. To assess the feasibility of utilizing spectral information to discriminate arterial tissue type, several classification algorithms were developed and evaluated. Arterial fluorescence spectra from 350 to 700 nm were obtained from 100 human aortic specimens. Seven spectral classification algorithms were developed with the following techniques: multivariate linear regression, stepwise multivariate linear regression, principal components analysis, decision plane analysis, Bayes decision theory, principal peak ratio, and spectral width. The classification ability of each algorithm was evaluated by its application to the training set and to a validation set containing 82 additional spectra. All seven spectral classification algorithms prospectively classified atherosclerotic and normal aorta with an accuracy greater than 80 percent (range: 82-96 percent). Laser angioplasty systems incorporating spectral classification algorithms may therefore be capable of detection and selective ablation of atherosclerotic plaque.

Algorithms↗

PubMed1998

Image processing algorithms for retinal montage synthesis, mapping, and real-time location determination.

Although laser retinal surgery is the best available treatment for choridal neovascularization, the current procedure has a low success rate (50%). Challenges, such as motion-compensated beam steering, ensuring complete coverage and minimizing incidental photodamage, can be overcome with improved instrumentation. This paper presents core image processing algorithms for 1) rapid identification of branching and crossover points of the retinal vasculature; 2) automatic montaging of video retinal angiograms; 3) real-time location determination and tracking using a combination of feature-tagged point-matching and dynamic-pixel templates. These algorithms tradeoff conflicting needs for accuracy, robustness to image variations (due to movements and the difficulty of providing steady illumination) and noise, and operational speed in the context of available hardware. The algorithm for locating vasculature landmarks performed robustly at a speed of 16-30 video image frames/s depending upon the field on a Silicon Graphics workstation. The montaging algorithm performed at a speed of 1.6-4 s for merging 5-12 frames. The tracking algorithm was validated by manually locating six landmark points on an image sequence with 180 frames, demonstrating a mean-squared error of 1.35 pixels. It successfully detected and rejected instances when the image dimmed, faded, lost contrast, or lost focus.

Algorithms↗

PubMed1991

A fast and simple algorithm for the calculation of convective heat transfer by large vessels in three-dimensional inhomogeneous tissues.

A fast and simple algorithm has been presented for the calculation of time-dependent temperature distributions in inhomogeneous vascularized tissue. Three-dimensional anatomical data of tissues and vessel structures are decomposed into elementary cubic nodes by a special digitizing routine with vessels represented by connected strings of vessel nodes. Vessel cross sections may be irregular shaped and/or tapered. Conductive and convective heat transfer was calculated through use of the heat balance technique on each cubic node resulting in an explicit finite difference computational scheme. Employing a three time level scheme, the Fourier stability criterion is circumvented allowing arbitrary time steps to be defined in the algorithm. Time steps as large as 100 times the Fourier restricted one still result in stable and convergent calculations of the stationary temperature distribution. Vessels with different flows and diameters are incorporated by performing a vessel specific second discretization step in time. Using the new algorithm as a mathematical tool the thermal equilibration length of vessel segments have been established under a broad range of geometrical and flow conditions. Validation followed from comparing transient and stationary temperature distributions derived by the proposed algorithm to those from an accurate cylindrical numerical model. Predicted values for the thermal equilibration lengths are compared to an analytical expression and phantom experiments. The algorithm is incorporated in a thermal model being the main part of our hyperthermia treatment planning system.

Algorithms↗

PubMed1999

A cone-beam reconstruction algorithm for circle-plus-arc data-acquisition geometry.

In cone-beam computerized tomography (CT), projections acquired with the focal spot constrained on a planar orbit cannot provide a complete set of data to reconstruct the object function exactly. There are severe distortions in the reconstructed noncentral transverse planes when the cone angle is large. In this work, a new method is proposed which can obtain a complete set of data by acquiring cone-beam projections along a circle-plus-arc orbit. A reconstruction algorithm using this circle-plus-arc orbit is developed, based on the Radon transform and Grangeat's formula. This algorithm first transforms the cone-beam projection data of an object to the first derivative of the three-dimensional (3-D) Radon transform, using Grangeat's formula, and then reconstructs the object using the inverse Radon transform. In order to reduce interpolation errors, new rebinning equations have been derived accurately, which allows one-dimensional (1-D) interpolation to be used in the rebinning process instead of 3-D interpolation. A noise-free Defrise phantom and a Poisson noise-added Shepp-Logan phantom were simulated and reconstructed for algorithm validation. The results from the computer simulation indicate that the new cone-beam data-acquisition scheme can provide a complete set of projection data and the image reconstruction algorithm can achieve exact reconstruction. Potentially, the algorithm can be applied in practice for both a standard CT gantry-based volume tomographic imaging system and a C-arm-based cone-beam tomographic imaging system, with little mechanical modification required.

Algorithms↗

PubMed2000

Fast maximum entropy approximation in SPECT using the RBI-MAP algorithm.

In this work, we present a method for approximating constrained maximum entropy (ME) reconstructions of SPECT data with modifications to a block-iterative maximum a posteriori (MAP) algorithm. Maximum likelihood (ML)-based reconstruction algorithms require some form of noise smoothing. Constrained ME provides a more formal method of noise smoothing without requiring the user to select parameters. In the context of SPECT, constrained ME seeks the minimum-information image estimate among those whose projections are a given distance from the noisy measured data, with that distance determined by the magnitude of the Poisson noise. Images that meet the distance criterion are referred to as feasible images. We find that modeling of all principal degrading factors (attenuation, detector response, and scatter) in the reconstruction is critical because feasibility is not meaningful unless the projection model is as accurate as possible. Because the constrained ME solution is the same as a MAP solution for a particular value of the MAP weighting parameter, beta, the constrained ME solution can be found with a MAP algorithm if the correct value of beta is found. We show that the RBI-MAP algorithm, if used with a dynamic scheme for estimating beta, can approximate constrained ME solutions in 20 or fewer iterations. We compare results for various methods of achieving feasible images on a simulation of Tl-201 cardiac SPECT data. Results show that the RBI-MAP ME approximation provides images and quantitative estimates close to those from a slower algorithm that gives the true ME solution. Also, we find that the ME results have higher spatial resolution and greater high-frequency noise content than a feasibility-based stopping rule, feasibility-based low-pass filtering, and a quadratic Gibbs prior with beta selected according to the feasibility criterion. We conclude that fast ME approximation is possible using either RBI-MAP with the dynamic procedure or a feasibility-based stopping rule, and that such reconstructions may be particularly useful in applications where resolution is critical.

Algorithms↗

PubMed2001

Comments on the filtered backprojection algorithm, range conditions, and the pseudoinverse solution.

The filtered backprojection (FBP) algorithm is widely used in computed tomography for inverting the two-dimensional Radon transform. In this paper, we analyze the processing of an inconsistent data function by the FBP algorithm (in its continuous form). Specifically, we demonstrate that an image reconstructed using the FBP algorithm can be represented as the sum of a pseudoinverse solution and a residual image generated from an inconsistent component of the measured data. This reveals that, when the original data function is in the range of the Radon transform, the image reconstructed using the FBP algorithm corresponds to the pseudoinverse solution. When the data function is inconsistent, we demonstrate that the FBP algorithm makes use of a nonorthogonal projection of the data function to the range of the Radon transform.

Algorithms↗

PubMed2001

An artificial intelligent algorithm for tumor detection in screening mammogram.

Cancerous tumor mass is one of the major types of breast cancer. When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are very difficult to be visually detected on mammograms. This paper presents an algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms. The AI techniques include fractal dimension analysis, multiresolution markov random field, dogs-and-rabbits algorithm, and others. The fractal dimension analysis serves as a preprocessor to determine the approximate locations of the regions suspicious for cancer in the mammogram. The dogs-and-rabbits clustering algorithm is used to initiate the segmentation at the LL subband of a three-level DWT decomposition of the mammogram. A tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. We have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database. The verification results show that the proposed algorithm has a sensitivity of 97.3% and the number of false positive per image is 3.92.

Algorithms↗

PubMed2001

An iterative maximum-likelihood polychromatic algorithm for CT.

A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.

Algorithms↗

PubMed2000

Genetic algorithms for a robust 3-D MR-CT registration.

The aim of this paper is to present an original usage of genetic algorithms as a robust search space sampler in application to 3-D medical image elastic registration. An overview of the standard steps of a registration algorithm is given. We focus on the genetic algorithms use and particularly on the problem of extraction of the optimal solution among the final genetic population. We provide an original encoding scheme relying on a structural approach of point matching and then point out the need for a local optimization process. We then illustrate the algorithm with a concrete registration example and assert the results with a direct multivolume rendering tool. Finally, the algorithm is applied on the vanderbilt medical image database to assert the robustness and in order to compare it with other techniques.

Algorithms↗

PubMed2003

A comparison of algorithms for detection of spikes in the electroencephalogram.

Identification of the short transient waveform, called a spike, in the cortical electroencephalogram (EEG) plays an important role during diagnosis of neurological disorders such as epilepsy. It has been suggested that artificial neural networks (ANN) can be employed for spike detection in the EEG, if suitable features are provided as input to an ANN. In this paper, we explore the performance of neural network-based classifiers using features selected by algorithms suggested by four previous investigators. Of these, three algorithms model the spike by mathematical parameters and use them as features for classification while the fourth algorithm uses raw EEG to train the classifier. The objective of this paper is to examine if there is any inherent advantage to any particular set of features, subject to the condition that the same data are used for all feature selection algorithms. Our results suggest that artificial neural networks trained with features selected using any one of the above three algorithms as well as raw EEG directly fed to the ANN will yield similar results.

Algorithms↗