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Results for “Automated image segmentation”

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PubMed2005

The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine.

Automated tongue image segmentation, in Chinese medicine, is difficult due to two special factors: 1) there are many pathological details on the surface of the tongue, which have a large influence on edge extraction; 2) the shapes of the tongue bodies captured from various persons (with different diseases) are quite different, so they are impossible to describe properly using a predefined deformable template. To address these problems, in this paper, we propose an original technique that is based on a combination of a bi-elliptical deformable template (BEDT) and an active contour model, namely the bi-elliptical deformable contour (BEDC). The BEDT captures gross shape features by using the steepest decent method on its energy function in the parameter space. The BEDC is derived from the BEDT by substituting template forces for classical internal forces, and can deform to fit local details. Our algorithm features fully automatic interpretation of tongue images and a consistent combination of global and local controls via the template force. We apply the BEDC to a large set of clinical tongue images and present experimental results.

Algorithms↗

PubMed2004

Automated performance evaluation of range image segmentation algorithms.

Previous performance evaluation of range image segmentation algorithms has depended on manual tuning of algorithm parameters, and has lacked a basis for a test of the significance of differences between algorithms. We present an automated framework for evaluating the performance of range image segmentation algorithms. Automated tuning of algorithm parameters in this framework results in performance as good as that previously obtained with careful manual tuning by the algorithm developers. Use of multiple training and test sets of images provides the basis for a test of the significance of performance differences between algorithms. The framework implementation includes range images, ground truth overlays, program source code, and shell scripts. This framework should a) make it possible to objectively and reliably compare the performance of range image segmentation algorithms; b) allow informed experimental feedback for the design of improved segmentation algorithms. The framework is demonstrated using range images, but in principle it could be used to evaluate region segmentation algorithms for any type of image.

Journal Article↗

PubMed1997

Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks.

We present a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; seven male, seven female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (ri) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in five volunteers imaged five times each demonstrated high intrasubject reproducibility with a significance of at least p < 0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability.

Adult↗

PubMed2002

Automated segmentation of multispectral brain MR images.

This work presents a robust and comprehensive approach for the in vivo automated segmentation and quantitative tissue volume measurement of normal brain composition from multispectral magnetic resonance imaging (MRI) data. Statistical pattern recognition methods based on a finite mixture model are used to partition the intracranial volume into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) spaces. A masking algorithm initially extracts the brain volume from surrounding extrameningeal tissue. Radio frequency (RF) field inhomogeneity effects in the images are then removed using a recursive method that adapts to the intrinsic local tissue contrast. Our technique supports heterogeneous data with multispectral MR images of different contrast and intensity weighting acquired at varying spatial resolution and orientation. The proposed image segmentation methods have been tested using multispectral T1-, proton density-, and T2-weighted MRI data from young and aged non-human primates as well as from human subjects.

Algorithms↗

PubMed2003

Automated cell nuclear segmentation in color images of hematoxylin and eosin-stained breast biopsy.

OBJECTIVE: To develop an automated, reproducible epithelial cell nuclear segmentation method to quantify cytologic features quickly and accurately from breast biopsy. STUDY DESIGN: The method, based on fuzzy c-mean clustering of the hue-band of color images and the watershed transform, was applied to 39 images from 3 histologic types (typical hyperplasia, atypical hyperplasia, and ductal carcinoma in situ [cribriform and solid]). RESULTS: The performance of the segmentation algorithm was evaluated by visually determining the percentage of badly segmented nuclei (approximately 25% for all types), the percentage of nuclei that remained in clumps (4.5-16.7%) and the percentage of missed nuclei (0.4-1.5%) for each image. CONCLUSION: The segmentation algorithm was sensitive in that a small percentage of nuclei were missed. However, the percentage of badly segmented nuclei was on the order of 25%, and the percentage of nuclei that remained in clumps was on the order of 10% of the total number of nuclei in the duct. Even so, > 600 nuclei per duct, on average, were segmented correctly; that was a sufficient number by which to calculate accurate quantitative, cytologic, morphometric measurements of epithelial cell nuclei in stained tissue sections of breast biopsy.

Algorithms↗

PubMed2006

Constrained Gaussian mixture model framework for automatic segmentation of MR brain images.

An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains.

Algorithms↗

PubMed1995

Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT. Work in progress.

PURPOSE: To determine whether extraction of pulmonary vessels from computed tomographic (CT) images with automated segmentation improves the detection of pulmonary nodules. MATERIALS AND METHODS: Simulated nodules were superimposed on normal spiral CT images. Eight patients referred for CT assessment of pulmonary nodules were selected for clinical evaluation. Vessels were extracted from both the simulation and clinical study with a three dimensional seeded region-growing algorithm. Three experienced radiologists were asked to locate the nodules and assign a level of confidence to their findings. Sensitivity and proportion of false-positive results per case (FPC) were calculated. Observer performance was evaluated by alternate free-response receiver operating characteristic analysis. RESULTS: Extraction of vascular structures from CT scans improved sensitivity from 63% to 84% in the simulation study and from 58% to 78% in the clinical study. The proportion of FPC decreased from 52% to 24% and from 55% to 12%, respectively. Radiologists performed consistently better with the segmented images than with the original images in both the simulation (P = .006) and the clinical (P = .0013) study. CONCLUSION: Automated vessel subtraction and extraction improves detection of pulmonary nodules.

Adult↗

PubMed1998

Automated segmentation of muscle fiber images using active contour models.

The cross-sectional area of different fiber types is an important anatomic feature in studying the structure and function of healthy and diseased human skeletal muscles. However, such studies are hampered by the thousands of fibers involved when manual segmentation has to be used. We have developed a semiautomatic segmentation method that uses computational geometry and recent computer vision techniques to significantly reduce the time required to accurately segment the fibers in a sample. The segmentation is achieved by simply pointing to the approximate centroid of each fiber. The set of centroids is then used to automatically construct the Voronoi polygons, which correspond to individual fibers. Each Voronoi polygon represents the initial shape of one active contour model, called a snake. In the energy minimization process, which is executed in several stages, different external forces and problem-specific knowledge are used to guide the snakes to converge to fiber boundaries. Our results indicate that this approach for segmenting muscle fiber images is fast, accurate, and reproducible compared with manual segmentation performed by experts.

Adult↗

PubMed2001

Automating the segmentation of medical images for the production of voxel tomographic computational models.

Radiation dosimetry for the diagnostic medical imaging procedures performed on humans requires anatomically accurate, computational models. These may be constructed from medical images as voxel-based tomographic models. However, they are time consuming to produce and as a consequence, there are few available. This paper discusses the emergence of semi-automatic segmentation techniques and describes an application (iRAD) written in Microsoft Visual Basic that allows the bitmap of a medical image to be segmented interactively and semi-automatically while displayed in Microsoft Excel. iRAD will decrease the time required to construct voxel models.

Computer Simulation↗

PubMed1994

Integrated optical density (IOD), syntactic structure analysis, and survival in operated lung carcinoma patients.

Histological sections of formalin fixed, paraffin-embedded tissue comprising 195 specimens of human lung carcinomas were Feulgen stained. The nuclei of the histomorphological images were segmented using an automated image analyzing system, and the attributed minimum spanning trees (MST) were calculated. Features related to the DNA-content of the nuclei (integrated optical density (IOD), IOD-entropy, S-phase related fraction, percentage > 5C, etc.), and structural parameters (minimum distance between tumor cell nuclei, minimum distance between tumor cells and neighboring lymphocytes, MST-entropy, MST-current of entropy (entropiefluss), distance between neighboring proliferating tumor cells, etc.) were measured. The following results were obtained: the measured IOD and MST features showed significant differences between the primary carcinomas and metastatic carcinomas in the intrapulmonary lymph nodes. The survival of patients was remarkably improved if the carcinomas displayed a low S-phase related fraction, a low percentage of tumor cells > 5C, a low number of stem lines, and a low MST-current of entropy.

Carcinoma, Bronchogenic↗

PubMed1994

[DNA and MST entropy and current of entropy. New parameters of tumor biological characterization].

The clinical significance of the entropy of the integrated optical density E (IOD) and the corresponding entropiefluss EF (IOD) is discussed as well as the structural entropy E (MST) and its entropiefluss EF (MST). The E (IOD) is based upon the formula given by Stenkvist and Strande. It describes the statistical entropy of the DNA distribution of tumor cell nuclei; the E (MST) is based upon differences in IOD and distance between neighboring tumor cell nuclei and describes the deviation of tumor structure from a "biological constant" structure. The entropiefluss measures the current of entropy or irreversibly created heat which has to be removed through the surface of the tumor. It is a universally valid description of a thermodynamically open system and measures the distance of such a system from its equilibrium stage. The clinical significance of these parameters is tested on 241 specimens of human lung carcinomas and intrapulmonary metastases of extrapulmonary malignancies. The surgical specimens were fixed with buffered formalin. Histological sections cut from paraffin-embedded tissue were FEULGEN stained. The nuclei of the histomorphological images were segmented using an automated image analyzing system, and the attributed minimum spanning trees (MST) were calculated. The following results were obtained: The measured E (IOD) and E (MST) showed significant differences in the primary carcinomas compared to those measured in intrapulmonary lymph node metastases or intrapulmonary metastases. The survival of patients was remarkably improved if the carcinomas displayed a low E (MST), EF (IOD), and EF (MST).

Adenocarcinoma↗

PubMed2006

Predictive value of fundus autofluorescence for development of geographic atrophy in age-related macular degeneration.

PURPOSE: It has been suggested that lipofuscin accumulation, as measured by increased fundus autofluorescence (FAF), precedes progression or development of junctional zone geographic atrophy (GA) in age-related macular degeneration (AMD). The tools of biomedical image analysis were used to measure the probabilistic relationship of GA progression to increased FAF. METHODS: Serial AF images of eight eyes of six patients with AMD with GA were registered on computer. The images were leveled with a 12-zone quadratic polynomial mathematical model to minimize background variability. Semiautomated segmentation of GA was performed on the leveled images. Increased FAF was defined as a gray level greater than 2 standard deviations above the leveled image mean, identified on the initial image with automated segmentation, and measured as a fraction of the 250-microm border zone surrounding the initial GA lesion. Areas of GA lesions were identified on the final image. The positive predictive value (PPV) of increased FAF was determined as the probability that any pixel with increased FAF in the initial image would become part of new GA in the final image. Relative PPV was determined relative to the total quantity of new GA. The NPV (NPV) of increased FAF was calculated as the probability that any pixels without increased FAF would not become atrophic. The relative NPV was determined similarly. A similar analysis was also conducted with a 500-microm border zone to determine the predictive value of proximity to the original GA lesion ("proximity") for GA progression. RESULTS: As a fraction of the geographic atrophy border zone, the mean new GA was 0.44+/-0.20, and the mean increased FAF was 0.06+/-0.06. The mean PPV of increased FAF for new GA formation was 0.50+/-0.26. Compared with the relative PPV of chance of 1.0, the mean relative PPV of increased FAF was 1.15+/-0.28. The mean NPV of increased FAF was 0.57+/-0.20. The mean relative NPV of increased FAF was 1.00+/-0.02. In the 500-microm border zone, the mean relative PPV of FAF and of proximity were essentially equal (1.56+/-.70 and 1.52+/-0.26, respectively), whereas the mean relative NPV of proximity was significantly greater than that of FAF (1.26+/-0.19 and 1.01+/-0.01, respectively, P=0.02) CONCLUSIONS: The results of digital image analysis suggest that although increased FAF may have a modest PPV for new GA development, the relative PPV is generally no greater than chance. Similarly, the relative NPV demonstrates negligible difference from chance and is also lower than the relative NPV of proximity. This suggests that increased FAF, though a disease manifestation, is not a strong risk factor for development or extension of GA.

Aged↗

PubMed2000

Segmentation of color images from serous cytology for automated cell classification.

OBJECTIVE: To design an automated system for the classification of cells based on analysis of serous cytology, with the aim of segmenting both cytoplasm and nucleus using color information from the images as the main characteristic of the cells. STUDY DESIGN: The segmentation strategy uses color information coupled with mathematical morphology tools, such as watersheds. Cytoplasm and nuclei of all diagnostic cells are retained; erythrocytes and debris are eliminated. Special techniques are used for the separation of clustered cells. RESULTS: A large set of cells was assessed by experts to score the segmentation success rate. All cells were segmented whatever their spatial configurations. The average success rate was 92.5% for nuclei and 91.1% for cytoplasm. CONCLUSION: This color information-based segmentation of images of serous cells is accurate and provides a useful tool. This segmentation strategy will improve the automated classification of cells.

Ascitic Fluid↗

PubMed2026

A comparative evaluation of multiple enlarged perivascular space segmentation tools.

BACKGROUND: Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool. METHODS: Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female). RESULTS: DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency. CONCLUSIONS: DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.

Humans↗

PubMed1999

Bone image segmentation.

Characteristics of microscopic structures in bone cross sections carry essential clues in age determination in forensic science and in the study of age-related bone developments and bone diseases. Analysis of bone cross sections represents a major area of research in bone biology. However, traditional approaches in bone biology have relied primarily on manual processes with very limited number of bone samples. As a consequence, it is difficult to reach reliable and consistent conclusions. In this paper we present an image processing system that uses microstructural and relational knowledge present in the bone cross section for bone image segmentation. This system automates the bone image analysis process and is able to produce reliable results based on quantitative measurements from a large number of bone images. As a result, using large databases of bone images to study the correlation between bone structural features and age-related bone developments becomes feasible.

Adolescent↗

PubMed2004

Automated segmentation of human brain MR images using a multi-agent approach.

Image interpretation consists in finding a correspondence between radiometric information and symbolic labeling with respect to specific spatial constraints. It is intrinsically a distributed process in terms of goals to be reached, zones in the image to be processed and methods to be applied. To cope the the difficulty inherent in this process, several information processing steps are required to gradually extract information. In this paper we advocate the use of situated cooperative agents as a framework for managing such steps. Dedicated agent behaviors are dynamically adapted depending on their position in the image, of their topographic relationships and of the radiometric information available. The information collected by the agents is gathered, shared via qualitative maps, or used as soon as available by acquaintances. Incremental refinement of interpretation is obtained through a coarse to fine strategy. Our work is essentially focused on radiometry-based tissue interpretation where knowledge is introduced or extracted at several levels to estimate models for tissue-intensity distribution and to cope with noise, intensity non-uniformity and partial volume effect. Several experiments on phantom and real images were performed. A complete volume can be segmented in less than 5 min with about 0.84% accuracy of the segmented reference. Comparison with other techniques demonstrates the potential interest of our approach for magnetic resonance imaging (MRI) brain scan interpretation.

Artificial Intelligence↗

PubMed2005

Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images.

A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking.

Algorithms↗

PubMed1990

The Athena semi-automated karyotyping system.

In this article we describe Athena, a system that provides for semi-automated karyotyping of metaphase spreads. The system is based upon the Macintosh II computer. It uses software that is written entirely in C and consists of approximately 200 Kbytes of executable code. Athena provides automated segmentation of metaphase images into individual chromosomes, automated measurements on each banded chromosome, and automated classification into the standard Paris-convention karyotype. Furthermore, the system provides the ability to construct one or more chromosome data bases to represent the types of metaphase spreads and staining techniques that may be used in a given laboratory. Because we believe that it is impossible to construct a system that can achieve perfect segmentation, perfect separation of touching and overlapping chromosomes, perfect localization of the centromeres, and perfect classification, the system offers the possibility for interaction at each of the above stages using the well-accepted Macintosh user interface.

Centromere↗