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Results for “Automated image segmentation”

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PubMed2006

Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A(z)) of 0.83 +/- 0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.

Biopsy↗

PubMed2006

The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks.

To assess the value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy (MPS) and an artificial neural network. A total of 422 patients referred for MPS were studied using a one day (99m)Tc-tetrofosmin protocol. Adenosine stress combined with submaximal dynamic exercise was used. The images were interpreted by one of three experienced clinicians and these interpretations regarding the presence or absence of myocardial infarction were used as the standard. A fully automated method using artificial neural networks was compared with the clinical interpretation. Either perfusion data alone or a combination of perfusion and function from ECG-gated images were used as input to different artificial neural networks. After a training session, the two types of neural networks were evaluated in separate test groups using an eightfold cross-validation procedure. The neural networks trained with both perfusion and ECG-gated images had a 4-7% higher specificity compared with the corresponding networks using perfusion data only, in four of five segments compared at the same level of sensitivity. The greatest improvement in specificity, from 70% to 77%, was seen in the inferior segment. In the septal and lateral segments the specificity rose from 73% to 77% and from 81% to 85%, respectively. In the anterior segment, the increase in specificity from 93% to 94% by adding functional data was not significant. The addition of functional information from ECG-gated MPS is of value for the diagnosis of myocardial infarction using an automated method of interpreting myocardial perfusion images.

Adenosine↗

PubMed2007

Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.

Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media-adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove noise from the images without blurring the edges. Segmentation of media-adventitia contour is achieved using active contours (snakes). In particular, we use the gradient vector flow (GVF) as external force for the snakes. The detection of lumen border is obtained taking into account gray-level information of the inner part of the previously detected contours. A knowledge-based approach is used to determine which level of gray corresponds statistically to the different regions of interest: intima, plaque and lumen. The catheter region is automatically discarded. An estimate of plaque type is also given. Finally, 3D reconstruction of all detected regions is made. The suitability of this methodology has been verified for the analysis and visualization of plaque length, stenosis severity, automatic detection of the most problematic regions, calculus of plaque volumes and a preliminary estimation of plaque type obtaining for automatic measures of lumen and vessel area an average error smaller than 1mm(2) (equivalent aproximately to 10% of the average measure), for calculus of plaque and lumen volume errors smaller than 0.5mm(3) (equivalent approximately to 20% of the average measure) and for plaque type estimates a mismatch of less than 8% in the analysed frames.

Blood Vessels↗

PubMed2006

Quantitative evaluation of left ventricle performance from two dimensional echo images.

OBJECTIVES: We sought to quantify the left ventricle systolic dysfunction by a geometric index from two-dimensional (2D) echocardiography by implementing an automated fuzzy logic edge detection algorithm for the segmentation. BACKGROUND: The coronary injuries have repercussions on the left ventricle producing changes on wall contractility, the shape of the cavity, and as a whole changes on the ventricular function. METHODS: 2D echocardiogram and M-mode recordings were performed over the control group and those with the dysfunctions. From 2D recordings, individual frames were extracted for at least five cardiac cycles and then segmentation of left ventricle was done by automated fuzzy systems. In each frame, the volumes are measured and a geometric index, eccentricity ratio (ER), was derived. The endocardial fractional shortening (FS), midwall fractional shortening (mFS), and the relative wall thickness (RWT) were also measured in each case. RESULTS: Depressed value of endocardial FS (20.39 +/- 5.43 vs 34.28 +/- 9.36, P = 0.0046), mFS (33 +/- 8.3 vs 52.5 +/- 11.7, P = 0.0047), and the RWT (0.337 +/- 0.096 vs 0.525 +/- 0.119, P = 0.0002) was observed with dysfunction. ER measured at end-diastole (2.86 +/- 0.703 vs 4.14 +/- 0.38) and end-systole (3.14 +/- 0.79 vs 5.48 +/- 0.74) was found to be decreased in the dysfunction group and more significant at the end-systole (P = 0.00017 vs 6.6E-06). CONCLUSION: This work concludes that the regional and global left ventricle systolic dysfunction can be assessed by the ER measured at end-diastole and end-systole from 2D echocardiogram and may contribute to the high rate of cardiovascular disorders.

Adult↗

PubMed2001

Spectral imaging of multi-color chromogenic dyes in pathological specimens.

We have investigated the use of spectral imaging for multi-color analysis of permanent cytochemical dyes and enzyme precipitates on cytopathological specimens. Spectral imaging is based on Fourier-transform spectroscopy and digital imaging. A pixel-by-pixel spectrum-based color classification is presented of single-, double-, and triple-color in situ hybridization for centromeric probes in T24 bladder cancer cells, and immunocytochemical staining of nuclear antigens Ki-67 and TP53 in paraffin-embedded cervical brush material (AgarCyto). The results demonstrate that spectral imaging unambiguously identifies three chromogenic dyes in a single bright-field microscopic specimen. Serial microscopic fields from the same specimen can be analyzed using a spectral reference library. We conclude that spectral imaging of multi-color chromogenic dyes is a reliable and robust method for pixel color recognition and classification. Our data further indicate that the use of spectral imaging (a) may increase the number of parameters studied simultaneously in pathological diagnosis, (b) may provide quantitative data (such as positive labeling indices) more accurately, and (c) may solve segmentation problems currently faced in automated screening of cell- and tissue specimens.

Carcinoma, Transitional Cell↗

PubMed1999

Model-based quantitation of 3-D magnetic resonance angiographic images.

Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA's) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments directly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accurately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.

Algorithms↗

PubMed1988

Application of the method of multiple thresholding to white blood cell classification.

A new method of image segmentation based on the principle of multiple grey level thresholding has been applied to a data set consisting of 1149 white blood cells of 13 different, clinically important types, randomly chosen on 20 blood smears from leukemia patients. Classification of these cells on the basis of quantitative measurements in the segmented images yields an accuracy of 82.6%. Some of the erroneous classifications must be attributed to intrinsic problems in the assignment of a priori labels. Correcting for such cases, the performance of the method, as measured on the present data set, increases to 89.8%. This illustrates the practical applicability of the segmentation method in automated white blood cell and possibly other cytological and histological analysis systems.

Acute Disease↗

PubMed2006

Medical image segmentation using minimal path deformable models with implicit shape priors.

This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.

Algorithms↗

PubMed1997

Automated computer-assisted detection of follicles in ultrasound images of ovary.

Monitoring the follicles in women's ovaries is especially important in human reproduction. Today, the monitoring of follicles is done with human interaction. Such monitoring can be very demanding and inaccurate, and in most cases signifies additional burdens for the experts. In this paper, a new algorithm for automated computer-assisted detection of follicles in the ultrasound images of the ovary is proposed. It has a typical object recognition scheme (preprocessing, segmentation, and classification). The algorithm is based on the following idea: first, the ovary is estimated (coarsely) and then follicles are searched for. The methods used are known from literature (despeckle filter, Kirsch's operator, optimal thresholding, thinning, shape descriptions, classification), and the majority of our work was done experimenting with these methods and selecting the appropriate thresholds. The algorithm's computational complexity is of order of O(n2), which means about 6 min of processing time per an ultrasound image of dimensions of 768 x 576 pixels on HP 715 machines. It has been tested on a set of 20 real ultrasound images of the ovary. The recognition rate of follicles with these procedures was around 62%. The algorithm is not perfect, but it will be further modified and improved, as indicated in our conclusions.

Algorithms↗

PubMed2002

Structural gray matter differences between first-episode schizophrenics and normal controls using voxel-based morphometry.

The aim of this study was to compare the gray matter segments from T1 structural MR images of the brain in first-episode schizophrenic subjects (n = 34) and normal control subjects (n = 36) using automated voxel-based morphometry (VBM). This study is novel in that few studies have examined subjects in their first episode of schizophrenia. The subjects were recruited for the Edinburgh High Risk project and regional brain volumes were previously measured using a semi-automated volumetric region of interest (ROI) method of analysis. The primary interest was to compare the results from the compatible parts of the ROI study and the primary VBM approach. Our secondary interest was to compare the results of a study-specific template that was constructed from the control group to those using the generic T1 template (152 Montreal Neurological Institute brains) supplied with SPM99 (statistical parametric mapping). The images were processed and statistically analyzed using the SPM99 program. VBM analysis identified significant decreases in gray matter in the schizophrenics relative to the normal control group at the corrected voxel level (P < 0.05) in the right anterior cingulate, right medial frontal lobe, left middle temporal gyrus, left postcentral gyrus, and the left limbic lobe. There were no increases in gray matter in the schizophrenics relative to the control group. The construction of a customized template appeared to improve the detection of structural abnormalities. The analyses were subsequently restricted to voxels within the amygdala-hippocampal complex using the SPM small-volume correction. This identified gray matter decreases in the schizophrenics, at the corrected voxel level (P < 0.05), in the left and right uncus and parahippocampal gyri and the right amygdala. These results are compatible with and extend the relevant findings of the previous volumetric ROI analysis, when allowing for the differences between the methods and interpretation of their results.

Adult↗

PubMed2000

Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data.

This paper presents a new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The technique was inspired by procedures developed for analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness over the whole time series. Thus, static regions remain unchanged over time, whereas changes in tissue characteristics produce typical intensity variations in the voxel's time series. A set of features was derived from the time series, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster-Shafer's theory. The project was driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with a high degree of automation. The new approach replaces conventional segmentation of series of 3-D data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set. The new method has been applied to a total of 11 time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of about 1 year. The results demonstrate that time evolution is a highly sensitive feature for detection of fluctuating structures.

Brain↗

PubMed2001

Effects of abstinence on the brain: quantitative magnetic resonance imaging and magnetic resonance spectroscopic imaging in chronic alcohol abuse.

BACKGROUND: Structural brain damage, especially to white matter, is well documented in chronic alcohol abuse. There is also evidence for brain metabolic abnormalities in this condition. It is unknown, however, to what extent these structural and metabolic changes are present in treated alcohol abusers who achieve long-term abstinence versus treatment-naïve, heavily drinking individuals. METHODS: This study compared 12 recovering alcoholics with 8 actively heavily drinking subjects. Participants underwent magnetic resonance (MR) imaging and proton MR spectroscopic imaging of the brain. Semiautomated image segmentation techniques yielded volumes for gray matter, white matter, white matter lesions, and cerebral spinal fluid in multiple brain regions defined by Talairach stereotaxic coordinates. Automated spectral processing methods yielded gray and white matter concentrations of the metabolites N-acetylaspartate, creatine, and choline for the same regions. RESULTS: Recovering alcoholics had greater volumes of frontal white matter, but the opposite was true for white matter in a "remainder" region encompassing the basal frontal and temporal lobes, the cerebellum, and the brainstem. Recovering alcoholics also had smaller volumes of white matter lesions in whole brain, in occipital and mesial parietal regions, and in the remainder region. Recovering alcoholics had greater gray matter volumes in the orbital frontal pole and postcentral gyrus, but smaller gray matter volumes in the anterior cingulate. Whole-brain and regional metabolite concentrations did not differ significantly between the two groups. CONCLUSIONS: White and gray matter volumes in different regions of the brain were greater or smaller in recovering, treated alcoholics. The findings suggest region-specific structural recovery from chronic alcohol-induced brain injury, but also region-specific long-term structural damage in abstinent alcoholics. White matter lesions were widespread in active drinkers and may partly resolve during long-term abstinence. Proton MR spectroscopic measures, as applied in this cross-sectional study, were largely ineffective in revealing metabolic effects of abstinence on the alcohol-damaged brain.

Adult↗

PubMed2004

Diagnostic fidelity of the Z-buffer segmentation algorithm: preliminary assessment based on intracranial aneurysm detection.

We have developed an algorithm known as the Z-buffer segmentation (ZBS) algorithm for segmenting vascular structures from 3D MRA images. Previously we evaluated the accuracy of the ZBS algorithm on a voxel level in terms of inclusion and exclusion of vascular and background voxels. In this paper we evaluate the diagnostic fidelity of the ZBS algorithm. By diagnostic fidelity we mean that the data preserves the structural information necessary for diagnostic evaluation. This evaluation is necessary to establish the potential usefulness of the segmentation for improved image display, or whether the segmented data could form the basis of a computerized analysis tool. We assessed diagnostic fidelity by measuring how well human observers could detect aneurysms in the segmented data sets. ZBS segmentation of 30 MRA cases containing 29 aneurysms was performed. Image display used densitometric reprojections with shaded surface highlighting that were generated from the segmented data. Three neuroradiologists independently reviewed the generated ZBS images for aneurysms. The observers had 80% sensitivity (90% for aneurysms larger than 2mm) with 0.13 false positives per image. Good agreement with the gold standard for describing aneurysm size and orientation was shown. These preliminary results suggest that the segmentation has diagnostic fidelity with the original data and may be useful for improved visualization or automated analysis of the vasculature.

Algorithms↗

PubMed2002

Quantitative MRI assessment of leukoencephalopathy.

Quantitative MRI assessment of leukoencephalopathy is difficult because the MRI properties of leukoencephalopathy significantly overlap those of normal tissue. This report describes the use of an automated procedure for longitudinal measurement of tissue volume and relaxation times to quantify leukoencephalopathy. Images derived by using this procedure in patients undergoing therapy for acute lymphoblastic leukemia (ALL) are presented. Five examinations from each of five volunteers (25 examinations) were used to test the reproducibility of quantitated baseline and subsequent, normal-appearing images; the coefficients of variation were less than 2% for gray and white matter. Regions of leukoencephalopathy in patients were assessed by comparison with manual segmentation. Two radiologists manually segmented images from 15 randomly chosen MRI examinations that exhibited leukoencephalopathy. Kappa analyses showed that the two radiologists' interpretations were concordant (kappa = 0.70) and that each radiologist's interpretations agreed with the results of the automated procedure (kappa = 0.57 and 0.55). The clinical application of this method was illustrated by analysis of images from sequential MR examinations of two patients who developed leukoencephalopathy during treatment for ALL. The ultimate goal is to use these quantitative MR imaging measures to better understand therapy-induced neurotoxicity, which can be limited or even reversed with some combination of therapy adjustments and pharmacological and neurobehavioral interventions.

Adult↗

PubMed2004

Region-specific cell grafting into cervical and lumbar spinal cord in rat: a qualitative and quantitative stereological study.

In the present study, we have characterized an atraumatic grafting technique which permits multiple, segmental, and lamina-specific injections into cervical or lumbar spinal cord. Cell injections were performed in spinally mounted rats of different ages and spinal cord size, using a micromanipulator and glass microcapillary connected to a digital microinjector. For grafting, we used human neuroteratoma (hNT) cells, BrdU-labeled rat spinal precursors or primary embryonic spinal cord neurons isolated from E14 spinal cord of the eGFP+ rat. Systematic quantification of grafted cells was performed using stereological principles of systematic random sampling and semi-automated optical Disector software. Volume reconstruction was performed using serial sections from grafted areas and custom-developed software (Ellipse) which permits "two reference points" semi-automated alignment of images, as well as volume reconstruction and calculation. By coupling these techniques, it is possible to achieve a relatively precise and atraumatic cell delivery into multiple spinal cord segments and specific spinal laminae. Consistency of the multiple grafts position in the targeted laminar areas was verified by a systematic volume reconstruction. Good survival of implanted cells for the three different cell lines used indicate that this grafting technique coupled with a systematic analysis of the individual grafting sites can represent a valuable implantation-analytical system.

Animals↗

PubMedDate not supplied

Semi-automated color segmentation of anatomical tissue.

We propose a semi-automated region-based color segmentation algorithm to extract anatomical structures, including soft tissues, in the color anatomy slices of the Visible Human data. Our approach is based on repeatedly dividing an image into regions using Voronoi diagrams and classifying the regions based on experimental classification statistics. The user has the option of reclassifying regions in order to improve the final boundary. Our results indicate that the algorithm can find accurate outlines in a small number of iterations and that manual interaction can markedly improve the outline. This approach can be extended to 3D color segmentation.

Algorithms↗

PubMed2004

Quantitative assessment of pain-related thermal dysfunction through clinical digital infrared thermal imaging.

BACKGROUND: The skin temperature distribution of a healthy human body exhibits a contralateral symmetry. Some nociceptive and most neuropathic pain pathologies are associated with an alteration of the thermal distribution of the human body. Since the dissipation of heat through the skin occurs for the most part in the form of infrared radiation, infrared thermography is the method of choice to study the physiology of thermoregulation and the thermal dysfunction associated with pain. Assessing thermograms is a complex and subjective task that can be greatly facilitated by computerised techniques. METHODS: This paper presents techniques for automated computerised assessment of thermal images of pain, in order to facilitate the physician's decision making. First, the thermal images are pre-processed to reduce the noise introduced during the initial acquisition and to extract the irrelevant background. Then, potential regions of interest are identified using fixed dermatomal subdivisions of the body, isothermal analysis and segmentation techniques. Finally, we assess the degree of asymmetry between contralateral regions of interest using statistical computations and distance measures between comparable regions. RESULTS: The wavelet domain-based Poisson noise removal techniques compared favourably against Wiener and other wavelet-based denoising methods, when qualitative criteria were used. It was shown to improve slightly the subsequent analysis. The automated background removal technique based on thresholding and morphological operations was successful for both noisy and denoised images with a correct removal rate of 85% of the images in the database. The automation of the regions of interest (ROIs) delimitation process was achieved successfully for images with a good contralateral symmetry. Isothermal division complemented well the fixed ROIs division based on dermatomes, giving a more accurate map of potentially abnormal regions. The measure of distance between histograms of comparable ROIs allowed us to increase the sensitivity and specificity rate for the classification of 24 images of pain patients when compared to common statistical comparisons. CONCLUSIONS: We developed a complete set of automated techniques for the computerised assessment of thermal images to assess pain-related thermal dysfunction.

Female↗

PubMed2004

Parametric shape modeling using deformable superellipses for prostate segmentation.

Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32 +/- 0.62 mm and mean absolute distance error of 0.54 +/- 0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36 +/- 0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve.

Algorithms↗