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Results for “Automated image segmentation”

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PubMed1999

Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering.

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D2/D3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [123I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r2>0.99, P<0.0001). Similarly, FCM gave segmentation of the [123I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r2=0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner.

Animals↗

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↗

PubMed2004

Visualization of the variability of 3D statistical shape models by animation.

Models of the 3D shape of anatomical objects and the knowledge about their statistical variability are of great benefit in many computer assisted medical applications like images analysis, therapy or surgery planning. Statistical model of shapes have successfully been applied to automate the task of image segmentation. The generation of 3D statistical shape models requires the identification of corresponding points on two shapes. This remains a difficult problem, especially for shapes of complicated topology. In order to interpret and validate variations encoded in a statistical shape model, visual inspection is of great importance. This work describes the generation and interpretation of statistical shape models of the liver and the pelvic bone.

Humans↗

PubMed2006

Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral.

We describe a method of automatically calculating hippocampal atrophy rates on T1-weighted MR images without manual delineation of hippocampi. This method was applied to a group of Alzheimer's disease (AD) (n=36) and control (n=19) subjects and compared with manual methods (manual segmentation of baseline and repeat-image hippocampi) and semi-automated methods (manual segmentation of baseline hippocampi only). In controls, mean (S.D.) atrophy rates for manual, semi-automated, and automated methods were 18.1 (53.5), 15.3 (50.2) and 11.3 (50.4) mm3 loss per year, respectively. In AD patients these rates were 174.6 (106.5) 159.4 (101.2) and 172.1 (123.1) mm3 loss per year, respectively. The automated method was a significant predictor of disease (p=0.001) and gave similar group discrimination compared with both semi-automated and manual methods. The automated hippocampal analysis in this small study took approximately 20 min per hippocampal pair on a 3.4 GHz Intel Xeon server, whereas manual delineation of each hippocampal pair took approximately 90 min of operator-intensive labour. This method may be useful diagnostically or in studies where analysis of many scans may be required.

Aged↗

PubMed1997

Method for segmenting chest CT image data using an anatomical model: preliminary results.

We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.

Computer Simulation↗

PubMed1998

T1 hypointense lesion load in secondary progressive multiple sclerosis: a comparison of pre versus post contrast loads and of manual versus semi automated threshold techniques for lesion segmentation.

Magnetic resonance imaging (MRI) is increasingly being used as a monitoring tool for disease activity in therapeutic trials in multiple sclerosis. There is, however, only a limited relationship between MRI findings and clinical outcome measurements. It has been suggested that hypointense lesion load on T1 weighted imaging has a better correlation with disability than the more conventional T2 hyper intense lesion load. This study was undertaken to (i) evaluate different measurement techniques used to quantify T1 hypointense lesion load, and (ii) to compare lesion load as measured using different parameters and disability. Twenty-five patients with secondary progressive multiple sclerosis, mean age of 40 years (23-57), mean EDSS 5.7 (4-7) were analysed. T2 lesion load on FSE correlated well with both the hypointense lesion load on T1 pre-gadolinium (r = 0.8, P < 0.0001) and T1 post-gadolinium (r = 0.8, P < 0.0001) but less so with the enhancing lesion load (r = 0.4, P < 0.05). There was a very strong correlation with T1 hypo-intense lesion volume pre and post gadolinium (r = 0.96, P < 0.001). However, the EDSS was not correlated with the T2 lesion load (r = -0.27, P = 0.2), T1 pre-gadolinium load (r = -0.3, P = 0.1), T1 post gadolinium load (r = -0.4, P = 0.7) and enhancing lesion load (r = -0.28, P = 0.2), or with the degree of hypointensity of T1 weighted images determined using the threshold technique. There is a strong correlation between T1 hypointense lesion volume both pre and post gadolinium and also between T1 and T2 lesion volumes.

Adult↗

PubMed1985

Automated recognition of atypical nuclei in breast cancer cytology specimens by iterative image transformations.

In order to develop an objective grading system for nuclear atypia in breast cancer, an image analysis technique has been applied for the automated recognition of enlarged and hyperchromatic nuclei in cytology specimens. The image segmentation algorithm, based on the 'top hat' image transformation developed in mathematical morphology, is implemented on the LEYTAS automated microscope system. The performance of the segmentation algorithm has been evaluated for fifty malignant and eighty-five benign breast lesions by visual inspection of the displayed 'flagged' objects. The mean number of flagged objects per 1600 image fields for breast cancers was 887 (range 0-7920) of which 87% consisted of single, atypical nuclei. For benign lesions the mean number was 30 (range 0-307) of which 20% were single nuclei. By adaptation of the 'top hat' parameter values, a more extreme subpopulation of atypical nuclei could be discriminated. The large interspecimen variation in the breast cancer results was related to differences in DNA content distribution and mean nuclear area, determined independently with scanning cytophotometry, and to some extent with the histological type.

Breast Neoplasms↗

PubMed2004

Interactive 3D editing tools for image segmentation.

Segmentation is an important part of image processing, which often has a large impact on quantitative image analysis results. Fully automated operator independent segmentation procedures that successfully work in a population with a larger biological variation are extremely difficult to design and usually some kind of operator intervention is required, at least in pathological cases. We developed a variety of 3D editing tools that can be used to correct or improve results of initial automatic segmentation procedures. Specifically we will discuss and show examples for three types of editing tools that we termed: hole-filling (tool 1), point-bridging (tool 2), and surface-dragging (tool 3). Each tool comes in a number of flavors, all of which are implemented in a truly 3D manner. We describe the principles, evaluate efficiency and flexibility, and discuss advantages and disadvantages of each tool. We further demonstrate the superiority of the 3D approach over the time-consuming slice-by-slice editing of 3D datasets, which is still widely used in medical image processing today. We conclude that performance criteria for automatic segmentation algorithms may be eased significantly by including 3D editing tools early in the design process.

Algorithms↗

PubMed2006

Marker-controlled watershed for lymphoma segmentation in sequential CT images.

Segmentation of lymphoma containing lymph nodes is a difficult task because of multiple variables associated with the tumor's location, intensity distribution, and contrast to its surrounding tissues. In this paper, we present a reliable and practical marker-controlled watershed algorithm for semi-automated segmentation of lymphoma in sequential CT images. Robust determination of internal and external markers is the key to successful use of the marker-controlled watershed transform in the segmentation of lymphoma and is the focus of this work. The external marker in our algorithm is the circle enclosing the lymphoma in a single slice. The internal marker, however, is determined automatically by combining techniques including Canny edge detection, thresholding, morphological operation, and distance map estimation. To obtain tumor volume, the segmented lymphoma in the current slice needs to be propagated to the adjacent slice to help determine the external and internal markers for delineation of the lymphoma in that slice. The algorithm was applied to 29 lymphomas (size range, 9-53 mm in diameter; mean, 23 mm) in nine patients. A blinded radiologist manually delineated all lymphomas on all slices. The manual result served as the "gold standard" for comparison. Several quantitative methods were applied to objectively evaluate the performance of the segmentation algorithm. The algorithm received a mean overlap, overestimation, and underestimation ratios of 83.2%, 13.5%, and 5.5%, respectively. The mean average boundary distance and Hausdorff boundary distance were 0.7 and 3.7 mm. Preliminary results have shown the potential of this computer algorithm to allow reliable segmentation and quantification of lymphomas on sequential CT images.

Algorithms↗

PubMed2001

Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.

The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.

Algorithms↗

PubMed1991

Three-dimensional reconstruction of human coronary and peripheral arteries from images recorded during two-dimensional intravascular ultrasound examination.

BACKGROUND: Intravascular ultrasound provides high-resolution images of vascular lumen, plaque, and subjacent structures in the vessel wall; current instrumentation, however, limits the operator to viewing a single, tomographic, two-dimensional image at any one time. Comparative analysis of serial two-dimensional images requires repeated review of the video playback recorded during the two-dimensional examination, followed by a "mind's eye" type of imagined reconstruction. METHODS AND RESULTS: Computer-based, automated three-dimensional reconstruction was used to generate a tangible format with which to assess and compare a "stacked" series of two-dimensional images. Three-dimensional representations were prepared from sequential images obtained during intravascular ultrasound examination in 52 patients, 50 of whom were studied before and/or after percutaneous revascularization. Conventional two-dimensional ultrasound images were acquired by means of a systematic, timed pullback of the ultrasound catheter through the respective vascular segments. Images were then assembled in automated fashion to create a three-dimensional depiction of the vessel lumen and wall. Computer-enhanced three-dimensional reconstructions were generated in both sagittal and cylindrical formats. The sagittal format resulted in a longitudinal profile similar to that obtained during angiographic examination; in contrast to angiography, however, the sagittal reconstruction offered 360 degrees of limitless orthogonal views of the plaque and arterial wall as well as the vascular lumen. The cylindrical format yielded a composite view of a given vascular segment, and a hemisected version of the cylindrical reconstruction enabled en face inspection of the reconstructed luminal surface. Sagittal reconstructions facilitated analysis of dissections and plaque fractures resulting from percutaneous revascularization, and the hemisected cylindrical reconstructions enhanced analysis of endovascular prostheses. CONCLUSIONS: This preliminary experience demonstrates that computer-based three-dimensional reconstruction may further augment the use of intravascular ultrasound in assessing vascular pathology and guiding interventional therapy.

Aged↗

PubMed2002

Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images.

Current semiautomated magnetic resonance (MR)-based brain segmentation and volume measurement methods are complex and not sufficiently accurate for certain applications. We have developed a simpler, more accurate automated algorithm for whole-brain segmentation and volume measurement in T(1)-weighted, three-dimensional MR images. This histogram-based brain segmentation (HBRS) algorithm is based on histograms and simple morphological operations. The algorithm's three steps are foreground/background thresholding, disconnection of brain from skull, and removal of residue fragments (sinus, cerebrospinal fluid, dura, and marrow). Brain volume was measured by counting the number of brain voxels. Accuracy was determined by applying HBRS to both simulated and real MR data. Comparing the brain volume rendered by HBRS with the volume on which the simulation is based, the average error was 1.38%. By applying HBRS to 20 normal MR data sets downloaded from the Internet Brain Segmentation Repository and comparing them with expert segmented data, the average Jaccard similarity was 0.963 and the kappa index was 0.981. The reproducibility of brain volume measurements was assessed by comparing data from two sessions (four total data sets) with human volunteers. Intrasession variability of brain volumes for sessions 1 and 2 was 0.55 +/- 0.56 and 0.74 +/- 0.56%, respectively; the mean difference between the two sessions was 0.60 +/- 0.46%. These results show that the HBRS algorithm is a simple, fast, and accurate method to determine brain volume with high reproducibility. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving MRI data are needed.

Adult↗

PubMed2002

Fast robust automated brain extraction.

An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods.

Algorithms↗

PubMed2006

A knowledge-guided active model method of cortical structure segmentation on pediatric MR images.

PURPOSE: To develop an automated method for quantification of cortical structures on pediatric MR images. MATERIALS AND METHODS: A knowledge-guided active model (KAM) approach was proposed with a novel object function similar to the Gibbs free energy function. Triangular mesh models were transformed to images of a given subject by maximizing entropy, and then actively slithered to boundaries of structures by minimizing enthalpy. Volumetric results and image similarities of 10 different cortical structures segmented by KAM were compared with those traced manually. Furthermore, the segmentation performances of KAM and SPM2, (statistical parametric mapping, a MATLAB software package) were compared. RESULTS: The averaged volumetric agreements between KAM- and manually-defined structures (both 0.95 for structures in healthy children and children with medulloblastoma) were higher than the volumetric agreement for SPM2 (0.90 and 0.80, respectively). The similarity measurements (kappa) between KAM- and manually-defined structures (0.95 and 0.93, respectively) were higher than those for SPM2 (both 0.86). CONCLUSION: We have developed a novel automatic algorithm, KAM, for segmentation of cortical structures on MR images of pediatric patients. Our preliminary results indicated that when segmenting cortical structures, KAM was in better agreement with manually-delineated structures than SPM2. KAM can potentially be used to segment cortical structures for conformal radiation therapy planning and for quantitative evaluation of changes in disease or abnormality.

Algorithms↗

PubMed1997

Groping for quantitative digital 3-D image analysis: an approach to quantitative fluorescence in situ hybridization in thick tissue sections of prostate carcinoma.

In molecular pathology numerical chromosome aberrations have been found to be decisive for the prognosis of malignancy in tumours. The existence of such aberrations can be detected by interphase fluorescence in situ hybridization (FISH). The gain or loss of certain base sequences in the desoxyribonucleic acid (DNA) can be estimated by counting the number of FISH signals per cell nucleus. The quantitative evaluation of such events is a necessary condition for a prospective use in diagnostic pathology. To avoid occlusions of signals, the cell nucleus has to be analyzed in three dimensions. Confocal laser scanning microscopy is the means to obtain series of optical thin sections from fluorescence stained or marked material to fulfill the conditions mentioned above. A graphical user interface (GUI) to a software package for display, inspection, count and (semi-)automatic analysis of 3-D images for pathologists is outlined including the underlying methods of 3-D image interaction and segmentation developed. The preparative methods are briefly described. Main emphasis is given to the methodical questions of computer-aided analysis of large 3-D image data sets for pathologists. Several automated analysis steps can be performed for segmentation and succeeding quantification. However tumour material is in contrast to isolated or cultured cells even for visual inspection, a difficult material. For the present a fully automated digital image analysis of 3-D data is not in sight. A semi-automatic segmentation method is thus presented here.

Humans↗