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PubMed1994

A method for quantitative image assessment based on redundant feature measurements and statistical reasoning.

Advances in computer graphics and electronics have contributed significantly to the increased utilization of digital imaging throughout the scientific community. Recently, as the volume of data being gathered for biomedical applications has begun to approach the human capacity for processing, emphasis has been placed on developing an automated approach to assist health scientists in assessing images. Methods that are currently used for analysis often lack sufficient sensitivity for discriminating among elements that exhibit subtle differences in feature measurements. In addition, most approaches are highly interactive. This paper presents an automated approach to segmentation and object recognition in which the spectral and spatial content of images is statistically exploited. Using this approach to assess noisy images resulted in correct classification of more than 97% of the pixels evaluated during segmentation and in recognition of geometric shapes irrespective of variations in size, orientation, and translation. The software was subsequently used to evaluate digitized stained blood smears.

Algorithms↗
PubMed2004

Improvement and automation of artificial neural networks to estimate medical outcomes.

The lengthy process of manually optimizing a feedforward backpropagation artificial neural network (ANN) provided the incentive to develop an automated system that could fine-tune the network parameters without user supervision. A new stopping criterion was introduced--the logarithmic-sensitivity index--that manages a good balance between sensitivity and specificity of the output classification. The automated network automatically monitored the classification performance to determine when was the best time to stop training-after no improvement in the performance measure (either highest correct classification rate, lowest mean squared error or highest log-sensitivity index value) occurred in the subsequent 500 epochs. Experiments were performed on three medical databases: an adult intensive care unit, a neonatal intensive care unit and a coronary surgery patient database. The optimal network parameter settings found by the automated system were similar to those found manually. The results showed that the automated networks performed equally well or better than the manually optimized ANNs, and the best classification performance was achieved using the log-sensitivity index as a stopping criterion.

Cluster Analysis↗
PubMed2003

Recovery, visualization, and analysis of actin and tubulin polymer flow in live cells: a fluorescent speckle microscopy study.

Fluorescent speckle microscopy (FSM) is becoming the technique of choice for analyzing in vivo the dynamics of polymer assemblies, such as the cytoskeleton. The massive amount of data produced by this method calls for computational approaches to recover the quantities of interest; namely, the polymerization and depolymerization activities and the motions undergone by the cytoskeleton over time. Attempts toward this goal have been hampered by the limited signal-to-noise ratio of typical FSM data, by the constant appearance and disappearance of speckles due to polymer turnover, and by the presence of flow singularities characteristic of many cytoskeletal polymer assemblies. To deal with these problems, we present a particle-based method for tracking fluorescent speckles in time-lapse FSM image series, based on ideas from operational research and graph theory. Our software delivers the displacements of thousands of speckles between consecutive frames, taking into account that speckles may appear and disappear. In this article we exploit this information to recover the speckle flow field. First, the software is tested on synthetic data to validate our methods. We then apply it to mapping filamentous actin retrograde flow at the front edge of migrating newt lung epithelial cells. Our results confirm findings from previously published kymograph analyses and manual tracking of such FSM data and illustrate the power of automated tracking for generating complete and quantitative flow measurements. Third, we analyze microtubule poleward flux in mitotic metaphase spindles assembled in Xenopus egg extracts, bringing new insight into the dynamics of microtubule assemblies in this system.

Actins↗
PubMed2005

Automated derivation and refinement of sequence length patterns for protein sequences using evolutionary computation.

Several stratagems are used in protein bioinformatics for the classification of proteins based on sequence, structure or function. We explore the concept of a minimal signature embedded in a sequence that defines the likely position of a protein in a classification. Specifically, we address the derivation of sparse profiles for the G-protein coupled receptor (GPCR) clan of integral membrane proteins. We present an evolutionary algorithm (EA) for the derivation of sparse profiles (signatures) without the need to supply a multiple alignment. We also apply an evolution strategy (ES) to the problem of pattern and profile refinement. Patterns were derived for the GPCR 'superfamily' and GPCR families 1-3 individually from starting populations of randomly generated signatures, using a database of integral membrane protein sequences and an objective function using a modified receiver operator characteristic (ROC) statistic. The signature derived for the family 1 GPCR sequences was shown to perform very well in a stringent cross-validation test, detecting 76% of unseen GPCR sequences at 5% error. Application of the ES refinement method to a signature developed by a previously described method [Sadowski, M.I., Parish, J.H., 2003. Automated generation and refinement of protein signatures: case study with G-protein coupled receptors. Bioinformatics 19, 727-734] resulted in a 6% increase of coverage for 5% error as measured in the validation test. We note that there might be a limit to this or any classification of proteins based on patterns or schemata.

Algorithms↗
PubMed2004

Noise reduction in spine videofluoroscopic images using the undecimated wavelet transform.

Videofluoroscopy permits using sequences of low quality images to study the spine movement. In this work the problem of enhancing the quality of these images is considered in order to facilitate the extraction of kinematic parameters. The method is based on the undecimated wavelet transform and on a preliminary training of a sub-set of images. The anatomical features are preserved using a mask. Key element of the method is its fast and automated implementation. The concept of improving the extraction of kinematic parameters by improving the image representation instead of the technique to extract these is also innovative. The technique has been tested on two sequences of images and the results demonstrates that the method enhances images not related with training sub-set.

Algorithms↗
PubMed2006

SPECT imaging in the diagnosis of pulmonary embolism: automated detection of match and mismatch defects by means of image-processing techniques.

UNLABELLED: SPECT of ventilation/perfusion (V/Q) lung scans not only improves the diagnostic accuracy of the method but also facilitates the application of advanced image-processing techniques. On the basis of such techniques, our study aimed at developing a procedure that automatically analyzes V/Q lung scans with regard to match and mismatch defects. METHODS: Fifty-three patients with suspected pulmonary embolism had lung scans using the SPECT technique as well as 16-slice multidetector-row spiral CT within an interval of 48 h. After iterative image reconstruction and computerized linear registration of the V/Q scans, the ventilation was normalized to the perfusion. For the automated detection of mismatch defects, the perfusion was subtracted from the ventilation, whereas for the detection of match defects, the perfusion was subtracted from the inverted ventilation. Two experienced referees assessed all images. The final diagnosis was made at a consensus meeting while taking into account all of the imaging modalities, laboratory tests, clinical data, and evaluation of a follow-up period. RESULTS: The sensitivity, specificity, and accuracy of the conventional visual assessment were 0.91, 0.97, and 0.94, respectively, compared with 0.95, 0.84, and 0.89, respectively, for the automated algorithm. Artifacts imitating mismatch defects in the pulmonary recesses accounted for the relatively low specificity of the automated analysis. Artifacts of that kind were found in 15 patients and led to a false-positive diagnosis in 5 patients. However, by combining the visual and the automated approach, all artifacts could be easily identified leading to a sensitivity, specificity, and accuracy of 0.95, 1.0, and 0.98, respectively. Additionally, in all 12 patients of the cohort with highly heterogeneous ventilation and perfusion, the automated analysis made correct diagnoses. CONCLUSION: Because of the 3-dimensional properties of the SPECT data, the analysis of lung scans can be automated and objectified. The algorithm produces images that are easy to read and well suited for demonstration. Because of artifacts in the pulmonary recesses introduced by the automated approach, its diagnostic accuracy does not reach the level of the conventional analysis yet. Could these artifacts be overcome, the efficiency of the automated algorithm would be at least equivalent to that of conventional image interpretation. At present, best results can be achieved by combining both approaches.

Adult↗
PubMed2006

Dual-tracer dopamine transporter and perfusion SPECT in differential diagnosis of parkinsonism using template-based discriminant analysis.

UNLABELLED: Clinical differential diagnosis in parkinsonism can be difficult especially at early stages. We investigated whether combined perfusion and dopamine transporter (DAT) imaging can aid in the differential diagnosis of parkinsonian disorders: idiopathic Parkinson's disease (IPD), progressive supranuclear palsy (PSP), multiple system atrophy (MSA), dementia with Lewy bodies (LBD), and essential tremor (ET). METHODS: One hundred twenty-nine patients were studied, retrospectively (69 males; 24 MSA, 12 PSP, 8 LBD, 27 ET, and 58 IPD; mean disease duration, 3.5 +/- 3.7 y). Diagnosis was based on established clinical criteria after follow-up of 5.5 +/- 3.8 y in a university specialist movement disorders clinic. Group characterization was done using a categoric voxel-based design and, second, a predefined volume-of-interest approach along Brodmann areas (BA) and subcortical structures, including striatal asymmetry and anteroposterior indices. Stepwise forward discriminant analysis was performed with cross-validation (CV) using the leave-one-out technique. RESULTS: Characteristic patterns for perfusion and DAT were found for all pathologies. In the parkinson-plus group, MSA, PSP, and LBD could be discriminated in 100% (+CV) of the cases. When including IPD, discrimination accuracy was 82.4% (99% without CV). 2beta-Carbomethoxy-3beta-(4-iodophenyl)nortropane imaging as a single technique was able to discriminate between ET and neurodegenerative forms with an accuracy of 93.0% (+CV); inclusion of perfusion information augmented this slightly to 97.4% (+CV). CONCLUSION: Dual-tracer DAT and perfusion SPECT in combination with discrimination analysis allows an automated, accurate differentiation between the most common forms of parkinsonism in a clinically relevant setting.

Algorithms↗
PubMed2005

A word-oriented approach to alignment validation.

MOTIVATION: Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences. RESULTS: Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families. AVAILABILITY: On request from the authors.

Algorithms↗
PubMed2004

Clustering of diverse genomic data using information fusion.

MOTIVATION: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. RESULTS: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. AVAILABILITY: Software code is available by request from the first author. CONTACT: jkasturi@cse.psu.edu.

Algorithms↗
PubMed2002

DNASER I: layout and data analysis.

We present the DNA analyzer (DNASER), a novel bioinstrumentation for real-time acquisition and elaboration of images from fluorescent DNA microarrays. A white light beam illuminates the target sample allowing the images grabbing on a high sensibility and wide-band charge-coupled device camera (ORCA II-Hamamatsu). This high-performance device permits to acquire images faster and of higher quality than the traditional systems. The DNA microarrays images are processed to recognize the DNA chip spots, to analyze their superficial distribution on the glass slide and to evaluate their geometric and intensity properties. Differently form conventional techniques, the spots analysis is fully automated and the DNASER does not require any additional information about the DNA microarray geometry. The DNASER hardware and software architecture is illustrated. Preliminary results are shown from experiments performed on real DNA samples.

Algorithms↗
PubMed2004

Automated matching of temporally sequential CT sections.

In the evaluation of patient response to therapy through measurements on thoracic computed tomography (CT) scans, the selection of anatomically equivalent sections in temporally sequential scans is required. We developed an automated method based on normalized mutual information (NMI) to expedite the selection of anatomically equivalent sections. The method requires as input two temporally sequential CT scans from the same patient. A specified section from the baseline scan is then compared with the sections of a follow-up scan. Each section in the follow-up scan is successively translated and rotated relative to the baseline section, and NMI is calculated. The section in the follow-up scan that yields the highest NMI with respect to the baseline section is selected as the matching section. The method was applied to a database of 22 pairs of temporally sequential CT scans from mesothelioma patients. Five observers manually selected their choice of the best anatomically matched section for each of three predetermined sections in the 22 baseline scans, and the range of selected sections was recorded. The automated method was applied to the same baseline sections to determine the computer-based anatomically matched sections in the corresponding follow-up scan. The automated process was performed using both original CT sections and sections automatically segmented so that only intrathoracic pixels contributed to NMI calculations. The accuracy of the automated method was quantified on a section-by-section basis by comparison with the range of sections selected by the observers. The automated method without segmentation selected equivalent sections within the observers' range for 54 of the 66 matching tasks (81.8%). An 11% improvement was achieved when thoracic segmentation was performed as a pre-processing step.

Adult↗
PubMed2005

Automatic navigation path generation based on two-phase adaptive region-growing algorithm for virtual angioscopy.

In this paper, we propose a fast and automated navigation path generation algorithm to visualize inside of carotid artery using MR angiography images. The carotid artery is one of the body regions not accessible by real optical probe but can be visualized with virtual endoscopy. By applying two-phase adaptive region-growing algorithm, the carotid artery segmentation is started at the initial seed, which is located on the initially thresholded binary image. This segmentation algorithm automatically detects the branch position with stack feature. Combining with a priori knowledge of anatomic structure of carotid artery, the detected branch position is used to separate the carotid artery into internal carotid artery and external carotid artery. A fly-through path is determined to automatically move the virtual camera based on the intersecting coordinates of two bisectors on the circumscribed quadrangle of segmented carotid artery. In consideration of the interactive rendering speed and the usability of standard graphic hardware, endoscopic view of carotid artery is generated by using surface rendering algorithm with perspective projection method. In addition, the endoscopic view is provided with ray casting algorithm for off-line navigation of carotid artery. Experiments have been conducted on both mathematical phantom and clinical data sets. This algorithm is more effective than key-framing and topological thinning method in terms of automated features and computing time. This algorithm is also applicable to generate the centerline of renal artery, coronary artery, and airway tree which has tree-like cylinder shape of organ structures in the medical imagery.

Algorithms↗
PubMed2005

Cell detection in phase-contrast images used for alpha-particle track-etch dosimetry: a semi-automated approach.

A novel alpha-particle irradiator has recently been developed that provides the ability to characterize cell response. The irradiator is comprised of a collimated, planar alpha-particle source which, from below, irradiates cells cultured on a track-etch material. Cells are imaged using phase-contrast microscopy before and following irradiation to obtain geometric information and survival rates; these can be used with data from alpha-particle track images to assess cell response. A key step in this process is determining cell location within the pre-irradiation images. Although this can be done completely by a human observer, the number of images requiring analysis makes the process time-consuming and tedious. To reduce the potential human error and decrease user interaction time, a semi-automated, computer-aided method of cell detection has been developed. The method employs a two-level adaptive thresholding technique to obtain size and position information about potential cell cytoplasms and nuclei. Proximity and geometry-based thresholds are then used to mark structures as cells. False-positive detections from the automated algorithm are due mostly to imperfections in the track-etch background, camera effects and cellular residue. To correct for these, a human observer reviews all detected structures, discarding false positives. When analysing two randomly selected cell dish image databases, the semi-automated method detected 92-94% of all cells and 94-97% of cells with a well-defined cytoplasm and nucleus while reducing human workload by 32-83%.

Algorithms↗
PubMed2005

Matching and anatomical labeling of human airway tree.

Matching of corresponding branchpoints between two human airway trees, as well as assigning anatomical names to the segments and branchpoints of the human airway tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1% of the automatically assigned segment labels were found to be correct.

Algorithms↗
PubMed2003

Classification of breast masses in ultrasonic B scans using Nakagami and K distributions.

Classification of breast masses in greyscale ultrasound images is undertaken using a multiparameter approach. Five parameters reflecting the non-Rayleigh nature of the backscattered echo were used. These parameters, based mostly on the Nakagami and K distributions, were extracted from the envelope of the echoes at the site, boundary, spiculated region and shadow of the mass. They were combined to create a linear discriminant. The performance of this discriminant for the classification of breast masses was studied using a data set consisting of 70 benign and 29 malignant cases. The Az value for the discriminant was 0.96 +/- 0.02, showing great promise in the classification of masses into benign and malignant ones. The discriminant was combined with the level of suspicion values of the radiologist leading to an Az value of 0.97 +/- 0.014. The parameters used here can be calculated with minimal clinical intervention, so the method proposed here may therefore be easily implemented in an automated fashion. These results also support the recent reports suggesting that ultrasound may help as an adjunct to mammography in breast cancer diagnostics to enhance the classification of breast masses.

Adult↗
PubMed2005

Accuracy of short-axis cardiac MRI automatically derived from scout acquisitions in free-breathing and breath-holding modes.

To qualitatively assess the accuracy of automated cardiovascular magnetic resonance planning procedures devised from scout acquisitions in free-breathing and breath-holding modes, to quantitatively evaluate the accuracy of the derived left ventricular volumes, mass and function and compare these parameters with the ones obtained from the manually planned acquisitions. Ten healthy volunteers underwent cardiovascular MR (CMR) acquisitions for ventricular function assessment. Short-axis data sets of the left ventricle (LV) were manually planned and generated twice in an automatic fashion. Automated planning parameters were derived from gated scout acquisitions in free-breathing and breath-holding modes. End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) were measured. The agreement between the manual and automatic planning methods, as well as the variability of the aforementioned measurements were assessed. The differences between two automated planning methods were also compared. The mean differences between the manual and automated CMR planning derived from gated scouts in free-breathing mode were 8.05 ml (EDV), 1.84 ml (ESV), 0.69% (EF), and 4.72 g (LVM). The comparison between manual and automated CMR planning derived from gated scouts in breath-holding mode yielded the following differences: 4.22 ml (EDV), 0.34 ml (ESV), 0.3% (EF), and -0.72 mg (LVM). The variability coefficients were 3.72 and 3.66 (EDV), 5.6 and 8.19 (ESV), 3.46 and 4.31 (EF), 6.49 and 5.20 (LVM) for the automated CMR planning methods derived from scouts in free-breathing and breath-holding modes, respectively. Automated CMR planning methods can provide accurate measurements of LV dimensions in normal subjects, and therefore may be utilized in the clinical environment to provide a cost-effective solution for functional assessment of the human cardiovascular system.

Adult↗
PubMed2001

Automatic extraction of acronym-meaning pairs from MEDLINE databases.

Acronyms are widely used in biomedical and other technical texts. Understanding their meaning constitutes an important problem in the automatic extraction and mining of information from text. Here we present a system called ACROMED that is part of a set of Information Extraction tools designed for processing and extracting information from abstracts in the Medline database. In this paper, we present the results of two strategies for finding the long forms for acronyms in biomedical texts. These strategies differ from previous automated acronym extraction methods by being tuned to the complex phrase structures of the biomedical lexicon and by incorporating shallow parsing of the text into the acronym recognition algorithm. The performance of our system was tested with several data sets obtaining a performance of 72 % recall with 97 % precision. These results are found to be better for biomedical texts than the performance of other acronym extraction systems designed for unrestricted text.

Abbreviations as Topic↗