Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “image analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36Linked to original sources
PubMed1998

Automated image analysis for bladder cancer.

The genetic and epigenetic changes that occur during cancer development result in apparent morphological changes. Light microscopic image analysis provides objective assessment of cellular and nuclear morphology. The complexity of changes reflects the basic nature of dedifferentitation: a multi-hit process. Image analysis methods proved valuable for the assessment of malignancy in bladder cancer. Clinically applicable systems have been developed to diagnose urothelial cell cancer and predict prognosis. What is the place of these systems in daily practice?

Cell Nucleus↗
PubMed1993

Discrepancies of DNA content of various solid tumours before and after culture measured by image analysis. Comparison of cytogenetical data.

Parallel cytophotometric ploidy studies and cytogenetic analysis were performed on 15 various human solid tumours. The quantification of DNA by image analysis was carried out on cytological imprints of fresh tumours and on smears obtained after cell culture. The results obtained by both sets of calculations were compared with each other and with the cytogenetic results. 6 cases (40%) showed concordance between the 3 techniques. One case was aneuploid for both DNA image analysis measurements but the cytogenetic data showed only a diploid stem line. In 3 cases out of 15 (20%), smears DNA analysis and cytogenetic results were concordant: in 2 tumours, the culture step failed to preserve aneuploid stem lines that were present in the imprint analysis. In the third one, a minority tetraploid peak observed after culture was absent on the imprint slide. Concordance between imprints and cytogenetic data and discordance with smears' analysis was observed in 3 cases (20%). These 3 cases were diploid or near diploid but the DNA analysis on the smears after culture showed an aneuploid stem line in each case. The last 2 cases showed a total disagreement between the 3 techniques. By measuring the DNA content with an image analyser, the observer can ensure that only tumoral cells are taken into account. The present study revealed that cytogenetic data represent only about 60% of the population that is effectively present in the culture dish and that the cultured population represents only 47% of the population present on the fresh tumour imprint.

Cells, Cultured↗
PubMed1994

Quantitative measurement of DNA content in gastric carcinoma; flow cytometry and video image analysis.

The DNA content of 48 gastric carcinomas from archival material was analysed by static and flow cytometry. By image analysis 81.3% of the tumours were aneuploid and ploidy was related to stage (P = 0.024) and lymph node metastasis. A trend for better survival (> 12 months) was observed in patients with diploid tumours (P = 0.058). The mean 5c exceeding rate (5cER) was significantly related to tumour stage (P < 0.05) and patient's survival (P = 0.018). In contrast, by flow cytometry only 43.7% of these tumours were aneuploid and these were more often associated with lymph node metastasis (59.3%) but no relationship was observed with any other parameters or patient's survival. In this series, image analysis appears to be more sensitive than flow cytometry in detecting small aneuploid populations. It may give additional prognostic information. It is, however, a time-consuming technique.

Aneuploidy↗
PubMed1998

Quantitative assessment of basement membranes in soft tissue tumours. Computerized image analysis of laminin and type IV collagen.

Basement membranes (BMs) in 201 soft tissue tumours were quantified using computerized image analysis of tissues immunostained for laminin and type IV collagen. The purpose of the study was to compare and quantify the extent of BM deposition in a large and varied group of benign and malignant tumours. Laminin and type IV collagen gave similar results. The difference between benign and malignant was statistically highly significant (P = 0.0001), with greater deposition in benign tumours. BM deposition was homogeneous in benign tumours and heterogeneous in sarcomas and appeared to correlate with the degree of differentiation. Some poorly differentiated sarcomas showed cytoplasmic laminin staining but little or no extracellular BM. Immunohistochemical evaluation of BM has some advantages over electron microscopy; specialized equipment is not needed and since large samples can be studied with little sampling error, heterogeneity can be studied more readily. Subjective visual assessment gives a good overall indication of the extent of BM deposition and in many situations is likely to be a suitable alternative to image analysis. Because of staining heterogeneity, BM immunohistochemistry is unlikely to be of significant value in the diagnosis of specific types of sarcoma.

Basement Membrane↗
PubMed1990

Assessment of nucleolar organizer regions by automatic image analysis in breast cancer: correlation with DNA content, proliferation rate, receptor status and histopathological grading.

The value of automatic image analysis in the investigation of nucleolus regions (AgNOR) has been examined in tissue sections of 52 malignant and 30 benign breast lesions. Determination of the AgNOR number per cell alone revealed a considerable overlap between benign (range 1.2-3.8) and malignant specimens (range 1.5-16.2). They differed however, highly significantly (P less than 0.001) in their AgNOR sizes. In benign breast disorders the mean AgNOR area per tumour ranged from 0.22 microns2 to 1.07 microns2 (mean 0.39 microns2), whereas in carcinomas AgNOR sites ranged from 0.05 microns2 to 0.22 microns2 (mean 0.09 microns2). AgNOR counts showed a good correlation with histopathological grade (P less than 0.05), aneuploidy (P less than 0.01), proliferation rate as determined by Ki67 immunostaining (P less than 0.01), as well as oestrogen and progesterone receptor content (P less than 0.01). Image analysis proved to be advantageous over AgNOR counting alone as it facilitated the standardization of the AgNOR technique itself and thus, significantly improved its diagnostic specifity.

Breast Neoplasms↗
PubMedDate not supplied

A microscopical assay using a densitometric application of image analysis to quantify neurotransmitter dynamics.

We have attempted to demonstrate the technical requirements and performance of a microscopical assay using a densitometric application of image analysis to measure immunohistochemical stain intensity. Not surprisingly, the techniques required were more demanding than those used for the quantification of field and object parameters in the nervous system. The following areas of methodology have been shown to be important: (1) use of buffers free of metallic ions for tissue processing, (2) selection and titration of first and second layer antibodies, (3) reduction and control of fading of fluorescence, (4) selection of microscopical and imaging equipment to give accurate, sensitive and uniform representations of low-light biological images, and (5) use of appropriate image analysis algorithms in order to generate binary images that match the spatial and intensity distributions of immunostaining. Incorporation of these techniques into our assay system gave sensitive measurements of the time-scale of uptake of 5-hydroxy-tryptamine (5-HT) into sympathetic nerve terminals. The microscopical assay appears to have advantages over alternative approaches used for studies of neurotransmitter dynamics, particularly in small, heterogeneous tissue samples.

Algorithms↗
PubMed2005

Strategies to configure image analysis algorithms for clinical usage.

Medical imaging informatics must exceed the mere development of algorithms. The discipline is also responsible for the establishment of methods in clinical practice to assist physicians and improve health care. From our point of view, it is commonly accepted that model-based analysis of medical images is superior to other concepts, but only a few applications are found in daily clinical use. The gap between development of model-based image analysis and its routine application can be addressed by identifying four necessary transfer steps: formulation, parameterization, instantiation, and validation. Usually, computer scientists formulate the model and define its parameterization, i.e., configure a model to handle a selected subset of clinical data. During instantiation, the algorithm adapts the model to the actual data, which is validated by physicians. Since medical a priori knowledge and particular knowledge on technical details are required for parameterization and validation, these steps are considered to be bottlenecks. In this paper, we propose general schemes that allow an application- or image-specific parameterization to be performed by medical users. Combining noncontextual and contextual approaches, we also suggest a reliable scheme that allows application-specific validation, even if a gold standard is unavailable. To emphasize our point of view, we provide examples based on unsupervised segmentation in medical imagery, which is one of the most difficult tasks. Following the proposed schemes, an exact delineation of cells in micrographs is parameterized, validated, and successfully established in daily clinical use, while automatic determination of body regions in radiographs cannot be configured to support reliable and robust clinical use. The results stress that parameterization and validation must be based on clinical data that show all potential variations and artifact sources.

Algorithms↗
PubMed2005

Color image analysis for quantifying renal tumor angiogenesis.

OBJECTIVE: To segment and quantify microvessels in renal tumor angiogenesis based on a color image analysis method and to improve the accuracy and reproducibility of quantifying microvessel density. STUDY DESIGN: The segmentation task was based on a supervised learning scheme. First, 12 color features (RGB, HSI, I1I2I3 and L*a*b*) were extracted from a training set. The feature selection procedure selected I2L*S features as the best color feature vector. Then we segmented microvessels using the discriminant function made using the minimum error rate classification rule of Bayesian decision theory. In the quantification step, after applying a connected component-labeling algorithm, microvessels with discontinuities were connected and touching microvessels separated. We tested the proposed method on 23 images. RESULTS: The results were evaluated by comparing them with manual quantification of the same images. The comparison revealed that our computerized microvessel counting correlated highly with manual counting by an expert (r = 0.95754). The association between the number of microvessels after the initial segmentation and manual quantification was also assessed using Pearson's correlation coefficient (r = 0.71187). The results indicate that our method is better than conventional computerized image analysis methods. CONCLUSION: Our method correlated highly with quantification by an expert and could become a way to improve the accuracy, feasibility and reproducibility of quantifying microvessel density. We anticipate that it will become a useful diagnostic tool for angiogenesis studies.

Algorithms↗
PubMedDate not supplied

An automated form of video image analysis applied to classification of movement disorders.

Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videotaped while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practise. We have begun development of intelligent software which aims to provide a more flexible system able to quantify human posture and movement directly from whole-body images without markers and in an unstructured environment. The steps involved are to extract complete human profiles from video frames, to fit skeletal frameworks to the profiles and derive joint angles and swing distances. By this means a given posture is reduced to a set of basic parameters that can provide input to a neural network classifier. To test the system's performance we videotaped patients with dopa-responsive Parkinsonism and age-matched normals during several gait cycles, to yield 61 patient and 49 normal postures. These postures were reduced to their basic parameters and fed to the neural network classifier in various combinations. The optimal parameter sets (consisting of both swing distances and joint angles) yielded successful classification of normals and patients with an accuracy above 90%. This result demonstrated the feasibility of the approach. The technique has the potential to guide clinicians on the relative sensitivity of specific postural/gait features in diagnosis. Future studies will aim to improve the robustness of the system in providing accurate parameter estimates from subjects wearing a range of clothing, and to further improve discrimination by incorporating more stages of the gait cycle into the analysis.

Algorithms↗
PubMed1990

Comparative assessment of proliferation and DNA content in breast carcinoma by image analysis and flow cytometry.

Although tumor DNA content and proliferation are usually determined by flow cytometry (FCM), quantitative microscopic image analysis is a viable alternative technique that also provides important histologic correlations. To compare these methods, we measured DNA content and proliferation in 54 consecutive breast cancers and 15 benign breast lesions by FCM and IA. DNA content determination was concordant in 49 of 54 cancers measured by FCM and IA. Four of the discordant cases were aneuploid by IA and diploid by FCM. There was good correlation between the DNA index (DI) measured by FCM and IA (r = 0.89, P less than 0.0001). Proliferation was assessed by IA quantitation of Ki-67 and PCNA/Cyclin antibody staining, as well as by flow cytometric S-phase fraction (SPF). Ki-67 positivity was greater in breast cancer than in benign controls (21.6% +/- 13.1% vs. 7.9% +/- 5.6% [P less than 0.0001]), as was PCNA/Cyclin positivity (10.2 +/- 6.7% vs. 2.7 +/- 2.5% [P less than 0.0001]). S-phase fraction measured by FCM was 7.9% +/- 5.7% for carcinomas and 3.17% +/- 2.1% for benign controls (P less than 0.003). Ki-67 and Cyclin staining, as well as SPF, were significantly increased in aneuploid compared to diploid tumors, and increased staining was associated with worsening nuclear grade. There were significant correlations between SPF and Ki-67 staining (r = 0.48, P less than 0.0001) and SPF and Cyclin staining (r = 0.48, P less than 0.0001). We conclude that FCM and IA provide comparable measurements of DNA content, although occasional discrepancies occur. Image analysis provides a valuable alternative method for assessing tumor cell proliferation and may offer certain advantages over FCM.

Antibodies↗
PubMed1995

A new method of image analysis of fluorescein angiography applied to age-related macular degeneration.

Quantitative analysis of retinal and choroidal abnormalities using current photographic techniques is complex and laborious. Digital image analysis techniques using the scanning laser ophthalmoscope overcome many of the problems with present techniques and allow reliable quantitation. A prerequisite of quantitation is accurate image acquisition and registration. The authors describe a reliable method of image analysis and apply it to the quantitation of hyperfluorescence in scanning laser fluorescein angiograms of different forms of age-related macular degeneration. Retrospective analysis of scanning laser fluorescein angiograms obtained using a standardised technique was undertaken. Eighty-six angiograms from patients with age-related macular degeneration were analysed and categorised as dry maculopathy, geographic atrophy of the retinal pigment epithelium, retinal pigment epithelial detachment (PED) or subretinal neovascularisation (SRNV). Fluorescein characteristics of both SRNV and PED showed a characteristic pattern of fluorescence. The advantages and disadvantages of the technique are discussed.

Aged↗
PubMed2001

An assessment of the feasibility of using image analysis in the oyster embryo-larval development test.

In this study the feasibility of using the latest image capture, processing, and analysis techniques in the oyster embryo-larval development (OEL) test was assessed. This initially involved determining whether the OEL test could be carried out in multiwell plates (which would assist in the application of the image analysis technique), based on data from tests with the reference toxicant zinc and industrial effluents. The study then ascertained which of the 31 image analysis parameters of the Image Pro Plus software used was most appropriate for differentiating between the D larvae and non-D larvae at the end of the test procedure in a manner similar to that of visual observations. On the basis of the zinc reference toxicant and effluent test data derived in this study, the OEL test can be effectively carried out in 24 chamber multiwell plates, which provides the opportunity to count objects with image analysis software. The use of the image analysis parameters area and size (length) in combination resulted in mean control abnormalities and EC50 values in zinc reference toxicant tests which were not significantly different statistically from corresponding values derived using visual observations. Discrimination using the area and length parameters may be improved by the inclusion of other parameters in a suite of measurements which would reduce interference from extraneous material or lighting artefacts. Furthermore, the use of multiwell plates and image analysis can eliminate the variability associated with sub-sampling and inter-operator differences in the counts of D larvae and non-D larvae which is evident with the current visual observation method.

Animals↗
PubMed1991

Differentiation of cancellous bone and medullary bone in laying hens: a novel technique for image analysis.

A selective staining technique for the identification and differentiation of cancellous bone from medullary bone of the laying hen by image analysis is described. Undecalcified Polymaster resin sections were oxidized in acidified potassium permanganate and oxalic acid before being immersed in an ammoniacal silver solution. The sections were reduced in formalin, fixed in sodium thiosulfate and counterstained in naphthalene black 10B which was dissolved in picric and acetic acids. Intensely stained cancellous bone was prominent with this technique compared with a paler medullary bone component which permitted the former to be easily recognized and measured by image analysis.

Animals↗
PubMed2007

Chemiluminescent imaging analysis of interferon alpha in serum samples.

Enzyme-linked immunosorbent assay (ELISA), horseradish peroxidase (HRP)-catalyzed fluorescent reaction, and oxalate chemiluminescence imaging analysis have been combined to develop a sensitive, simple, and rapid method for analysis of interferon alpha (alpha-IFN) in human serum samples. A typical "sandwich type" immunoassay was used. Reaction of o-phenylenediamine (OPD) with hydrogen peroxide (H(2)O(2)), catalyzed by HRP, produced 2,3-diaminophenazine (PDA), which was detected by chemiluminescence imaging analysis with the bis(2,4,6-trichlorophenyl)oxalate (TCPO)-H(2)O(2)-glyoxaline-PDA chemiluminescent system. The TCPO chemiluminescent imaging system is more sensitive and the chemiluminescence quantum yield is at least five times higher than for the luminol-H(2)O(2)-HRP-PIP (p-iodophenol) chemiluminescent imaging system. The results showed there was a very good linear correlation between response and amount of alpha-IFN in the range 1.3-156.0 pg mL(-1) (R = 0.9991) and the detection limit was 0.8 pg mL(-1) (S/N=3). The relative standard deviation (n = 9) was 4.7%. The proposed method has been used for successful analysis of the amount of alpha-IFN in human serum. The results obtained compared well with those obtained by conventional colorimetric ELISA and luminol chemiluminescent ELISA.

Catalysis↗
PubMed1994

Image analysis and diagnostic classification of hepatocellular carcinoma using neural networks and multivariate discriminant functions.

BACKGROUND: Hepatocellular carcinoma (HCC) is often difficult to diagnose in cytologic material and small tissue biopsies since histomorphologic information is minimal or absent. The potential for misdiagnosis is greatest in attempting to discriminate well-differentiated HCC from dysplastic hepatocytes in cirrhosis. We investigated the feasibility of developing artificial intelligence classification methods based on nuclear image analysis data for use as adjuncts to the morphologic diagnosis of HCC. EXPERIMENTAL DESIGN: Ninety hematoxylin-eosin stained histologic slides including 56 with well- to poorly differentiated HCC and 34 showing a morphologic continuum from normal to markedly dysplastic benign hepatocytes were assembled from four laboratories. A relatively inexpensive PC-based image analysis system was used to measure 35 nuclear morphometric and densitometric parameters of 100 nuclei in each specimen. The data were randomized into classification training and testing sets containing equal numbers of benign and HCC samples. Objective diagnostic classification criteria for HCC based on neural networks and multivariate discriminant functions (DFs) were developed for the most discriminatory subsets of morphometric, densitometric, and combined morphometric/densitometric variables as selected by stepwise discriminant analysis of training data. RESULTS: Morphometric parameters provided the best results with the following testing data positive and negative predictive values (PV+ and PV-) for HCC classification: 86.2% PV+ and 81.3% PV- for a linear DF, 85.7% PV+ and 76.5% PV- for a quadratic DF and 100% PV+ and 85.0% PV- for a neural network. CONCLUSIONS: Our results demonstrate that nuclear image analysis-based objective classification criteria for HCC can be developed using artificial intelligence methods and that histologic material prepared at different institutions can be reliably classified. Neural networks for HCC classification were superior to linear and quadratic DFs. Morphometric data yielded the best results compared with densitometric or combined morphometric/densitometric data.

Carcinoma, Hepatocellular↗
PubMed2001

[Key technologies in image analysis of comparative genomic hybridization].

Comparative genomic hybridization has become a new technique in molecular-cytogenetics, and it has found significant applications in tumor pathology. Image analysis is an important part of comparative genomic hybridization. It analyzes the fluorescent images by many steps and finally determines whether there is any abnormality in the copy number of the test tumor tissue. This paper expatiates on the key steps in the image analysis of comparative genomic hybridization, including background correction, chromosome segmentation, chromosome axis determination, karyotyping and determining ratio profile. The future trend is also discussed.

Cytogenetics↗
PubMed1995

Quantification of dendritic spine populations using image analysis and a tilting disector.

A series of image analysis routines, stochastic geometry methodology, and a design-based stereological procedure have been developed to quantify objectively the length, layout, and the true density of neuronal dendritic spines observed at the light (or confocal) microscope level. First, the image of a dendritic fragment of interest (in the plane of view) is scaled to a standard brightness scale, and the dendritic profile is separated from the background using a computerized thresholding algorithm that analyzes the histogram of grey levels. Secondly, the resulting binary image of the dendrite is transformed to a midline skeleton that underlies the dendritic geometry. Thirdly, skeletal branch lengths are directly computed (in pixels), thus giving objective measures of visible spine lengths and inter-spine distances along the dendritic stem. These raw data are the basis for (1) an estimation of the distribution of 3D spine lengths, and (2) a nearest neighbour analysis of the spine layout along the dendrite. A design-based stereological routine, the tilting disector, is suggested for unbiased estimation of the true (3D) density of spines along dendrites. The routine involves tilting the dendritic fragment of interest around its longitudinal axis for a known angular sector and scoring the number of spines seen in one angular position and unseen in the other position. Data from a study of neuronal dendrites in the chick forebrain are presented.

Algorithms↗
PubMed2005

Fluorescence-microscopy-based image analysis for analyte-dependent particle doublet detection in a single-step immunoagglutination assay.

A novel fluorescence-microscopy-based image analysis method for classification of singlet and doublet latex particles is demonstrated and applied to a particle-based immunoagglutination assay for quantification of biomolecules in microliter-volume bulk samples. The image analysis method, verified by flow cytometric agglutination analysis, is based on a pattern recognition algorithm employing Gaussian-base-function fitting which allows robust identification and counting of singlets, doublets, and higher agglomerates of fluorescent microparticles. The immunoagglutination assay is experimentally modeled by a biotin-streptavidin interaction, with the goal of both theoretically and experimentally investigating the performance of a general immunoagglutination-based assay. For this purpose a theoretical model of the initial agglutination kinetics, based on particle diffusion combined with a steric factor determined by the level of specific and nonspecific agglutination, was developed. The theoretical model combined with the experimental data can be used to optimize an agglutination-based assay with regard to sensitivity and dynamic range and to estimate the affinity, receptor surface density, molecular and binding site sizes, and level of nonspecific binding that is present in the assay. The experimental results are in good agreement with the theoretical model, indicating the usefulness of the model for immunoagglutination assay optimization.

Agglutination Tests↗