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Results for “Automated image segmentation”

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PubMed2006

Automated classification and quantification of F-actin-containing ruffles in confocal micrographs.

Membrane ruffles are actin-rich protrusions of the plasma membrane that can be observed on the surface of many cell types. Phase contrast and fluorescent microscopy are widely used in the analysis of ruffles, which are commonly identified in cells stained with fluorescently labeled phalloidin. Currently, comparison of cellular ruffle formation under different experimental conditions is generally qualitative or semiquantitative. Ruffle structures are often defined using manual tracing and thresholding methods. Here, we report the rapid and accurate segmentation of ruffles from two-dimensional confocal projections of cells using automated method based on well-established image processing and analysis methods. Line-shaped ruffles were detected using line detectors and were then separated from the filtered images. Automated categorizing of the segmented line structures enabled accurate quantification of the ruffles. This automated approach is efficient and reliable and hence can serve as a powerful tool in studies of the mechanism of ruffle formation.

Actins↗

PubMed2003

Automated segmentation and analysis of vascular structures in magnetic resonance angiographic images.

The accurate assessment of the presence and extent of vascular disease, and planning of vascular interventions based on MRA requires the determination of vessel dimensions. The current standard is based on measuring vessel diameters on maximum intensity projections (MIPs) using calipers. In order to increase the accuracy and reproducibility of the method, automated analysis of the 3D MR data is required. A novel method for automatically determining the trajectory of the vessel of interest, the luminal boundaries, and subsequent the vessel dimensions is presented. The automated segmentation in 3D uses deformable models, combined with knowledge of the acquisition protocol. The trajectory determination was tested on 20 in vivo studies of the abdomen and legs. In 93% the detected trajectory followed the vessel. The luminal boundary detection was validated on contrast-enhanced (CE) MRA images of five stenotic phantoms. The results from the automated analysis correlated very well with the true diameters of the phantoms used in the in vitro study (r = 0.999, P < 0.001). MRA and x-ray angiography (XA) of the phantoms also correlated well (r = 0.895, P < 0.001). The average unsigned difference between the MRA and XA measurements was 0.08 +/- 0.05 mm. In conclusion, the automated approach allows the accurate assessment of vessel dimensions in MRA images.

Abdomen↗

PubMed1999

Objective evaluation of regional left ventricular wall motion during dobutamine stress echocardiographic studies using segmental analysis of color kinesis images.

OBJECTIVES: To test the feasibility of objective and automated evaluation of echocardiographic stress tests, we studied the ability of segmental analysis of color kinesis (CK) images to detect dobutamine-induced wall motion abnormalities and compared this technique with inexperienced reviewers of conventional gray-scale images. BACKGROUND: Conventional interpretation of stress echocardiographic studies is subjective and experience dependent. METHODS: CK images were obtained in 89 of 104 consecutive patients undergoing clinical dobutamine stress studies and were analyzed using custom software to calculate regional fractional area change in 22 segments in four standard views. Each patient's data obtained at rest was used as a control for automated detection of dobutamine-induced wall motion abnormalities. Independently, studies were reviewed without CK overlays by two inexperienced readers who classified each segment's response to dobutamine. A consensus reading of two experienced reviewers was used as the gold standard for comparisons. In a subgroup of 16 patients, these consensus readings and CK detection of wall motion abnormalities were compared with coronary angiography. RESULTS: The consensus reading detected ischemic response to dobutamine in 43 of 1958 segments in 23 of 89 patients. Automated detection of stress-induced wall motion abnormalities correlated more closely with the standard technique than the inexperienced reviewers (sensitivity 0.76 vs. 0.55, specificity 0.98 vs. 0.94 and accuracy 0.97 vs. 0.92). When compared with coronary angiography in a subgroup of patients, analysis of CK images differentiated between normal and abnormal wall motion more accurately than expert readers of gray-scale images (accuracy of 0.93 vs. 0.82). CONCLUSIONS: Analysis of CK images allows fast, objective and automated evaluation of regional wall motion, sensitive enough for clinical dobutamine stress data and more accurate than inexperienced readers. This method may result in a valuable adjunct to conventional visual interpretation of dobutamine stress echocardiography.

Aged↗

PubMed2005

Autofluorescence characteristics of normal foveas and reconstruction of foveal autofluorescence from limited data subsets.

PURPOSE: To develop mathematical and geometric models of the nonuniform autofluorescence (AF) patterns of foveas of normal subjects and to reconstruct these models from limited subsets of data. METHODS: Confocal scanning laser ophthalmoscope (cSLO) AF fundus images of normal maculae were obtained from both eyes of 10 middle-aged subjects. They were filtered and contrast enhanced, to obtain elliptical isobars of equal gray levels (GLs) and determine the isobars' resolutions, eccentricities, and angles of orientation. The original image data were fit with a mathematical model of elliptic quadratic polynomials in two equal zones: the center and the remaining annulus. RESULTS: The AF images segmented into nested concentric GL isobars with GLs that increased radially from the least-fluorescent center. The mean isobar resolution was 31 +/- 7 mum. The geometric eccentricity of the ellipses increased from 0.42 +/- 0.12 centrally to 0.52 +/- 0.14 peripherally (P = 0.0005), with mean axes of orientation peripherally 97.12 +/- 15.46 degrees . The model fits to the complete image data had mean absolute normalized errors ranging from 3.6% +/- 3.7% to 7.3% +/- 7.1%. The model fits to small subsets (1% to 2% of total image data) had mean absolute errors ranging from 3.7% +/- 3.8% to 7.3% +/- 7.2%. CONCLUSIONS: Normal AF fundus images show finely resolved, concentric, elliptical foveal patterns consistent with the anatomic distribution of fluorescent lipofuscin, light-attenuating macular pigment (MP), cone photopigment, and retinal pigment epithelial (RPE) pigment in the fovea. A two-zone, elliptic, quadratic polynomial model can accurately model foveal data. This model may be useful for image analysis and for automated segmentation of pathology.

Adult↗

PubMed2003

[Development of automated segmentation method for the posterior portion of the temporal lobe on coronal MR images].

Brain MRI is an important method for examining the diseases caused by various cerebral pathologies, and the measurement of temporal lobe volume is useful for identifying dementia and temporal lobe abnormalities. However, no segmentation algorithm for the temporal lobe on coronal MR images has been established. Such an algorithm is needed because the shape of the temporal lobe on coronal images varies from area to area. The purpose of this research was to develop a segmentation method for the posterior portion of the temporal lobe on coronal MR images. The subjects were 11 normal patients, whose coronal T(1)-weighted images were selected for this study. The preprocessing algorithm for segmentation consists of smoothing, binarization, and thinning. The first step of the segmentation process consists of recognition techniques for the temporal lobe region on thinning images. The next step is distance transformation on identified thinning images. Finally, the temporal lobe was segmented by using the original images and distance transformation images and employing the newly developed algorithm. The rate of accuracy of automated recognition was over 74% for all cases, while the average rate of accuracy was 83.2+/-4.0%. These results suggest that this segmentation method can clearly segment the temporal lobe and has potential for clinical use. Based on this study, although it included only 11 normal patients, we have started applying this segmentation method to many patients, with or without temporal lobe disease.

Adult↗

PubMed2025

MetaChrome: An Open-Source, User-Friendly Tool for Automated Metaphase Chromosome Analysis.

DNA Fluorescence In Situ Hybridization (FISH) is an essential technique to study chromosome biology and genetics, enabling precise visualization of specific genomic loci to study structural abnormalities, gene mapping, and chromosomal rearrangements. High-Throughput Imaging (HTI) can automate the analysis of DNA-FISH chromosome images, but the accurate and automated segmentation of mitotic chromosomes and simultaneous colocalization of FISH signals remains a challenge. While several commercial automated karyotyping tools partially solve these issues, open-source software that effectively combines robust chromosome segmentation with comprehensive colocalization analysis capabilities remains necessary. To address this unmet need, we developed MetaChrome, an open-source software platform built around a graphical user interface and explicitly designed for automated metaphase chromosome analysis. MetaChrome leverages fine-tuned deep learning models to automate metaphase chromosome segmentation, together with colocalization analysis of chromosome-specific FISH probes and immunofluorescent-labeled proteins. Importantly, MetaChrome achieves enhanced segmentation accuracy compared to traditional image processing methods by adopting a Cellpose segmentation model fine-tuned with manually annotated metaphase chromosome datasets. The fine-tuned model ensures precise assignment of DNA-FISH spots to individual chromosomes in an automated manner. This facilitates rapid identification of chromosomal abnormalities, reduces human error, and advances high-throughput chromosome analysis workflows, addressing a key bottleneck in chromosome biology research.

Chromosome segmentation↗

PubMed2001

Multi-slice CT for visualization of pulmonary embolism using perfusion weighted color maps.

PURPOSE: The purpose of our preliminary study was to evaluate the feasibility of a new technique for the perfusion weighted color display of the density of lung parenchyma derived from multi-slice CT (MSCT) data sets of clinical routine examinations for visualization of pulmonary embolism (PE). MATERIALS AND METHODS: Imaging of patients with suspected PE was performed on a commercially available MSCT (Somatom Volume Zoom; Siemens, Forchheim, Germany) after intravenous application of 120 cc of contrast-medium using a power injector. Scan parameters were 140 kV and 100 mAs, using a thin collimation of 4 x 1 mm and a table speed of 7 mm (pitch: 1.75). Derived from thin collimation axial slices (slice thicknesseff. 1.25 mm, reconstruction increment 0.8 mm), a new image processing technique was deployed. Based on these source images, an automated 3D-segmentation of the lungs was performed followed by threshold based extraction of major airways and vascular structures. The filtered volume data were color encoded and finally overlayed onto the original CT images. This color encoded display of parenchymal density distribution of the lungs was shown in axial, coronal and sagittal plane orientation. In four patients with excluded PE as well as in two patients with proven PE this new technique was performed. RESULTS: In the four patients that were considered negative regarding PE on MSCT, lung densitometry showed a homogeneous distribution of color encoded densities without circumscribed decreased or increased areas, beside the usually present gravity-dependent gradient in ventro-dorsal direction. In the two patients with proven PE, low density values on perfusion weighted color maps were found distally to the occluded pulmonary arteries. CONCLUSIONS: Our initial experience indicates that lung densitometry with an optimized display of the density distribution within the lung parenchyma may provide additional information in patients with suspected or proven PE. However, a comparison with ventilation/perfusion scintigraphy and a larger number of patients are necessary for the full clinical evaluation of this new functional imaging methodology.

Aged↗

PubMedDate not supplied

Toward an automated analysis system for nuclear magnetic resonance imaging. II. Initial segmentation algorithm.

Image segmentation algorithms based on hierarchical clustering have been developed for analysis of T1 and T2 nuclear magnetic resonance images. Application of these algorithms to simultaneous T1-T2 images of healthy volunteers extracted fundamental tissue types in the brain. These algorithms also were used both to identify the extent of the region of involvement of a subject with a history of a grade 3 astrocytoma of the right frontal lobe of the brain, and to characterize the tissue within the region of involvement. These results suggest that a simple segmentation algorithm can produce reasonable clustering of tissue types within the brain.

Biometry↗

PubMed2005

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images.

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R2 = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.

Adult↗

PubMed2000

Current methods in medical image segmentation.

Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.

Algorithms↗

PubMed2002

Feature-based, automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging.

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.

Algorithms↗

PubMed2000

Mesial temporal sclerosis and temporal lobe epilepsy: MR imaging deformation-based segmentation of the hippocampus in five patients.

In five patients with mesial temporal sclerosis, the authors verified the precision and reproducibility of hippocampal segmentations with deformation-based magnetic resonance (MR) imaging. The overall percentage overlap between automated segmentations was 92.8% (SD, 3.5%), between manual segmentations was 73.1% (SD, 9.5%), and between automated and manual segmentations was 74.8% (SD, 10.3%). Deformation-based hippocampal segmentations provided a precise method of hippocampal volume measurement in this patient population.

Adult↗

PubMed1996

Validation of Automated Border Detection in Intravascular Ultrasound Images.

Intravascular ultrasound (IVUS) imaging provides cross-sectional views of the vessel lumen; however, lumen measurements still rely on operator-dependent border delineation and time-consuming lumen tracings. We tested a new system for automated lumen border detection in IVUS images based on acoustic quantification of blood and vessel wall. In 10 rabbits, 29 segments of the aorta were imaged in vivo using a 2.9-Fr IVUS catheter. IVUS images were obtained during motorized pullbacks of aortic segments of 18 mm length. Automated measurements of lumen dimensions were compared to automated measurements of a second pullback through the same segment, lumen measurements derived from visual border tracings in IVUS images, and to quantitative angiography. The automated system showed good reproducibility: Correlations for repeated measurements of lumen area, maximal and minimal lumen diameters were r = 0.97, r = 0.91, and r = 0.93, respectively. Automated measurements also correlated well to visual image analysis (lumen area, r = 0.97; maximal lumen diameter, r = 0.89; minimal lumen diameter, r = 0.89) and to angiographic measurements (lumen area, r = 0.93; lumen diameter, r = 0.95). In 12% of the images, the automated system overestimated lumen dimensions because of weak wall signals in the presence of echolucent structures next to the wall. Signal artifacts from the IVUS catheter itself or strong blood backscatter resulted in lumen underestimation in 6% of the images. Over- and underestimation of lumen by the border detection system were often associated with eccentric catheter position. Thus, lumen measurements in vivo IVUS images can be performed using an automated border detection system based on acoustic quantification of blood and vessel wall. The system allows reproducible and accurate measurements of lumen area and diameters. (ECHOCARDIOGRAPHY, Volume 13, November 1996)

Journal Article↗

PubMed1998

Semiautomatic segmentation of brain exterior in magnetic resonance images driven by empirical procedures and anatomical knowledge.

This work demonstrates encouraging results for increasing the automation of a practical and precise magnetic resonance brain image segmentation method. The intensity threshold for segmenting the brain exterior is determined automatically by locating the choroid plexus. This is done by finding peaks in a series of histograms taken over regions specified using anatomical knowledge. Intensity inhomogeneities are accounted for by adjusting the global intensity to match the white matter peak intensity in local regions. Automated results are incorporated into the established manually guided segmentation method by providing a trained expert with the automated threshold. The results from 20 different brain scans (over 1000 images) obtained under different conditions are presented to validate the method which was able to determine the appropriate threshold in approximately 80% of the data.

Brain↗

PubMed1996

Accuracy of least squares designed spatial FIR filters for segmentation of images of fluorescence stained cell nuclei.

A method for accurate, real-time image segmentation is needed for the development of a fully automated image cytometer that combines the speed and case-of-use of flow cytometry with the detailed morphometry of imaging. Object intensity variation and inherent optical blur make real-time segmentation challenging. The best spatial finite impulse response (FIR) filter, implemented as a convolution, was tested for sharpening edges and creating the required contrast. The filter and threshold segmentation steps were treated as a two-category linear classifier. Best 3 x 3 through 25 x 25 filters were designed utilizing the perceptron criterion and nonlinear least squares, and tested on ten montage images of a combined 1,070 manually segmented DAPI stained cell nuclei. The resulting image contrast, or class separation, led to simple automatic thresholding via the histogram intermodal minimum. Image segmentation accuracy began to plateau at 7 x 7 filters and did not increase above 15 x 15. Little loss in accuracy occurred with application to the images not used for design. This segmentation method provides a systematic, fast and accurate means of creating binary object maps useful for subsequent measurement, processing and cell classification.

3T3 Cells↗

PubMed1991

Histometric features for the grading of prostatic carcinoma.

Histometric features for the objective grading of prostatic adenocarcinoma in histologic specimens were analyzed in five cases each of well, moderately and poorly differentiated lesions. Tissue sections from the selected cases were stained by the Feulgen method and digitized by a video-based microphotometer. Twenty total fields were recorded for each grade: ten at high resolution (an image sampling of 0.5 micron per pixel) and ten at low resolution (0.8 micron per pixel), with two fields per case recorded at each resolution. The images were segmented by an automated expert system-guided scene segmentation procedure. The performance of that procedure was measured by comparing the automated counts of nuclei in the segmented fields to the visual counts made by a pathologist in the same fields. For well, moderately and poorly differentiated cases, respectively, the nuclear counts made by the expert system at high resolution were 2.7%, 4.2% and 4.7% higher than the visual counts (as estimated from a total of 6,628 nuclei), but 1.2%, 2.5% and 1.1% lower at low resolution (10,329 nuclei). High-resolution features and tissue textural features were computed for each case. The high-resolution features showed good separation between the three groups of cases. The tissue textural features showed consistent separation between well and moderately differentiated cases. The relaxation of the spatial resolution (to 0.8 micron/pixel spacing) did not affect the selection of features, but led to less separation between the data from different grades. In conclusion, the automated system performed satisfactorily in distinguishing sections of prostatic tumors of varying degrees of differentiation.

Adenocarcinoma↗

PubMed2003

Automated segmentation of acetabulum and femoral head from 3-D CT images.

This paper describes several new methods and software for automatic segmentation of the pelvis and the femur, based on clinically obtained multislice computed tomography (CT) data. The hip joint is composed of the acetabulum, cavity of the pelvic bone, and the femoral head. In vivo CT data sets of 60 actual patients were used in the study. The 120 (60 x 2) hip joints in the data sets were divided into four groups according to several key features for segmentation. Conventional techniques for classification of bony tissues were first employed to distinguish the pelvis and the femur from other CT tissue images in the hip joint. Automatic techniques were developed to extract the boundary between the acetabulum and the femoral head. An automatic method was built up to manage the segmentation task according to image intensity of bone tissues, size, center, shape of the femoral heads, and other characters. The processing scheme consisted of the following five steps: 1) preprocessing, including resampling 3-D CT data by a modified Sinc interpolation to create isotropic volume and to avoid Gibbs ringing, and smoothing the resulting images by a 3-D Gaussian filter; 2) detecting bone tissues from CT images by conventional techniques including histogram-based thresholding and binary morphological operations; 3) estimating initial boundary of the femoral head and the joint space between the acetabulum and the femoral head by a new approach utilizing the constraints of the greater trochanter and the shapes of the femoral head; 4) enhancing the joint space by a Hessian filter; and 5) refining the rough boundary obtained in step 3) by a moving disk technique and the filtered images obtained in step 4). The above method was implemented in a Microsoft Windows software package and the resulting software is freely available on the Internet. The feasibility of this method was tested on the data sets of 60 clinical cases (5000 CT images).

Acetabulum↗

PubMed1995

Automated segmentation of digitized mammograms.

RATIONALE AND OBJECTIVES: Fast and reliable segmentation of digital mammograms into breast and nonbreast regions is an important prerequisite for further image analysis. We are developing a segmentation algorithm that is fully automated and can operate independent of type of digitizing system, image orientation, and image projection. METHODS: The algorithm identifies unexposed and direct-exposure image regions and generates a border surrounding the valid breast region, which can then be used as input for further image analysis. The program was tested on 740 digitized mammograms; the segmentation results were evaluated by two expert mammographers and two medical physicists. RESULTS: In 97% of the mammograms, the segmentation results were rated as acceptable for use in computer-aided diagnostic schemes. Segmentation problems encountered in the remaining 22 images (2.9%) were most often caused by digitization artifacts or poor mammographic technique. CONCLUSION: The developed algorithm can serve as a component of an "intelligent" workstation for computer-aided diagnosis in mammography.

Algorithms↗