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Results for “Automated image segmentation”

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PubMed2006

A completely automated CAD system for mass detection in a large mammographic database.

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Algorithms↗

PubMed1995

Prostatic intraepithelial neoplasia. Quantitation of the basal cell layer with machine vision system.

The aim of this paper is to report on a fully automated procedure to quantify the epithelial cell components in prostatic intraepithelial neoplasia with particular emphasis on the basal cell layer (BCL). Scene segmentation was guided by a knowledge-based system of digitized images recorded from histological section immunostained against high molecular weight keratin and counterstained with hematoxylin and eosin. Scene segmentation involved two major stages. First, the system located and identified the duct and segmented the scene, resulting in "intermediate segmentation products." This stage was followed by a reconstruction process in which the segmentation products (i.e., the lumen and the darkly and lightly stained epithelial cell components) were assembled to form the microscopic structure to achieve working unity. Following this, histometric measurements were made of the reconstructed scene. Computed were the percentage of the duct contour with BCL (90%), and the number and length of gaps in the BCL (19, ranging from 10 to 90 microns). Automated analysis of the BCL is accurate and provides information not readily identified by human examination.

Basement Membrane↗

PubMed1999

Reconstruction of the human cerebral cortex from magnetic resonance images.

Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. In this paper we address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.

Algorithms↗

PubMed2006

Computerized craniofacial reconstruction using CT-derived implicit surface representations.

In forensic craniofacial reconstruction, facial features of an unknown individual are estimated from an unidentified skull, based on a mixture of experimentally obtained guidelines on the relationship between soft tissues and the underlying skeleton. In this paper, we investigate the possibility of using full 3D cross-sectional CT images for establishing a reference database of densely sampled distances between the external surfaces of the skull and head for automated craniofacial reconstruction. For each CT image in the reference database, the hard tissue (skull) and soft tissue (head) volumes are automatically segmented and transformed into signed distance transform (sDT) images, representing for each voxel in this image the Euclidean distance to the closest point on the skull and head surface, respectively, distances being positive (negative) for voxels inside (outside) the skull/head. Multiple craniofacial reconstructions are obtained by first warping each reference skull sDT maps to the target skull sDT using a B-spline based free form deformation algorithm and subsequently applying these warps to the reference head sDT maps. A single reconstruction of the target head surface is defined as the zero level set of the arithmetic average of all warped reference head sDT maps, but other reconstructions are possible, biasing the result to subject specific attributes (age, BMI, gender). Both qualitative and quantitative tests (measuring the similarity between the 3D reconstructed and corresponding original head surface) on a small (N = 20) database are presented to proof the validity of the concept.

Databases as Topic↗

PubMedDate not supplied

A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images.

While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.

Algorithms↗

PubMed1984

A quantitative analysis of the human bone marrow granulocytic cell lineage using the SAMBA 200 cell image processor. I. The normal maturation sequence.

A quantitative image analysis of the human normal bone marrow granulocytic line was performed using the SAMBA 200 image analyzer. The steps of image acquisition, preprocessing, segmentation and parametrization are described. Forty-one parameters were computed on 941 cell images belonging to the various maturation stages. The automated classification of these cells based upon a stepwise linear discriminant analysis resulted in 77% correctly classified cells; the five most discriminating parameters were the nuclear area, the nuclear convexity degree, the average cytoplasmic hue, the regularity of the nuclear boundary and the average cytoplasmic luminance. The evolution of the parameters correlates well with the cytologic evolution and the biochemical and functional events during the maturation process. It can be inferred from our results that the maturation sequence can be subdivided into two phases according to the evolution of the cell profiles. The first phase, from myeloblast to myelocyte, is discontinuous and appears as the critical point with regard to the expression of genes. The second phase, from myelocyte to polymorphonuclear cell, is a continuous sequence of transformations leading to the functional granulocyte.

Autoanalysis↗

PubMed1998

Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis.

A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign. The RBST transforms a band of pixels surrounding a segmented mass onto the Cartesian plane (the RBST image). The border of a mammographic mass appears approximately as a horizontal line, and possible speculations resemble vertical lines in the RBST image. In this study, the effectiveness of a set of directional textures extracted from the images before the RBST. A database of 168 mammograms containing biopsy-proven malignant and benign breast masses was digitized at a pixel size of 100 microns x 100 microns. Regions of interest (ROIs) containing the biopsied mass were extracted from each mammogram by an experienced radiologist. A clustering algorithm was employed for automated segmentation of each ROI into a mass object and background tissue. Texture features extracted from spatial gray-level dependence matrices and run-length statistics matrices were evaluated for three different regions and representations: (i) the entire ROI; (ii) a band of pixels surrounding the segmented mass object in the ROI; and (iii) the RBST image. Linear discriminant analysis was used for classification, and receiver operating characteristic (ROC) analysis was used to evaluate the classification accuracy. Using the ROC curves as the performance measure, features extracted from the RBST images were found to be significantly more effective than those extracted from the original images. Features extracted from the RBST images yielded an area (Az) of 0.94 under the ROC curve for classification of mammographic masses as malignant and benign.

Biopsy↗

PubMed1999

Automated morphometric analysis in peripheral neuropathies.

We describe a three-step algorithm for the morphometric analysis of color images of nerve specimens, currently used in the diagnosis of peripheral neuropathies. The algorithm first segments the images by applying a clustering method in the color space. It then identifies and eliminates irrelevant regions and, in the final step, calculates the diagnostic parameters required for clinical analysis. The results obtained on 25 images are reported and compared with corresponding measurements made by neurologists.

Algorithms↗

PubMed2004

Automatic pectoral muscle segmentation on mediolateral oblique view mammograms.

Mammograms are X-ray images of the breast which are used to detect breast cancer. When mammograms are analyzed by computer, the pectoral muscle should preferably be excluded from processing intended for the breast tissue. For this and other reasons, it is important to identify and segment out the pectoral muscle. In this paper, a new, adaptive algorithm is proposed to automatically extract the pectoral muscle on digitized mammograms; it uses knowledge about the position and shape of the pectoral muscle on mediolateral oblique views. The pectoral edge is first estimated by a straight line which is validated for correctness of location and orientation. This estimate is then refined using iterative "cliff detection" to delineate the pectoral margin more accurately. Finally, an enclosed region, representing the pectoral muscle, is generated as a segmentation mask. The algorithm was found to be robust to the large variations in appearance of pectoral edges, to dense overlapping glandular tissue, and to artifacts like sticky tape. The algorithm has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 images. The segmentation results were evaluated by two expert mammographic radiologists, who rated 83.9% of the curve segmentations to be adequate or better.

Algorithms↗

PubMed2006

Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement.

The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.

Algorithms↗

PubMed2001

Integrated approach for segmentation of 3-D confocal images of a tissue specimen.

In this article we have proposed an integrated approach for segmentation of cells in volumetric image data obtained using the Confocal Microscope. The volumetric images are the stack of two-dimensional (2-D) images. Segmentation of cells in such an image stack is a difficult problem due to the complex structure of the objects and the spatial relationship of the object signatures in different image slices of the image stack. Here we have proposed a segmentation technique, which is a combination of several known and novel segmentation methods. Low-level techniques such as edge operators, middle-level techniques such as 3-D watershed, rule-based merging, and a high level technique, active surface model optimization, are integrated in one approach to get better segmentation with less human interaction. Some image enhancement and noise reduction techniques are also used to reduce the error in intermediate stages and speed up the segmentation process. Results are shown on 3-D images of prostate cancer tissue specimen.

Automation↗

PubMed1999

Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal a priori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computer-aided diagnostic scheme.

Databases as Topic↗

PubMed2001

[A software tool for automatic image-based ventilation analysis using dynamic chest CT-scanning in healthy and in ARDS lungs].

PURPOSE: Density measurements in dynamic CT image series of the lungs allow one to quantify ventilated, hyperinflated, and atelectatic pulmonary compartments with high temporal resolution. Fast automatic segmentation of lung parenchyma and a subsequent evaluation of it's respective density values are a prerequisite for any clinical application of this technique. MATERIAL AND METHODS: For automatic lung segmentation in thoracic CT scans, an algorithm was developed which uses (a) different density masks, and (b) anatomic knowledge to differentiate heart, diaphragm and chest wall from ventilated and atelectatic lung parenchyma. With Animal Care Committee approval, the automated technique was tested in 8 anaesthetized ventilated pigs undergoing dynamic CT before and after induction of lavage-ARDS. Images were acquired in one supradiaphragmatic, cross-sectional slice (temporal resolution of 100 ms; slice thickness of 1 mm, high resolution reconstruction algorithm). In 120 CT images the total pixel number and the calculated MLD from the automatically segmentated lung were compared to the values obtained from an interactive lung segmentation. RESULTS: The software tool was able to read all image series (DICOM standard). Automatic and interactive segmentation were in high agreement (R(2) = 0.99 for the total number of pixels and the MLD). Originally, the most frequent error was misclassification of atelectasis as extrapulmonary solid tissue. CONCLUSION: An automatic software tool is presented for lung segmentation in healthy lungs and in ARDS. Aerated lung and atelectasis were identified with high accuracy. This post-processing tool allows for a quantitative, CT based assessment of ventilation and recruitment processes in the lung. Thus, it may help to optimize ventilation patterns in patients with ARDS.

Animals↗

PubMed2005

An automated approach to connectivity-based partitioning of brain structures.

We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different areas of the brain, allowing them to interact with each other. Diffusion-tensor MRI measures the orientation of white-matter fibres in vivo, allowing us to perform connectivity-based partitioning non-invasively. Our new approach leverages atlas-based segmentation to automate anatomical labeling of the cortex. White-matter connectivities are inferred using a probabilistic tractography algorithm that models crossing pathways explicitly. The method is demonstrated with the partitioning of the corpus callosum of eight healthy subjects.

Algorithms↗

PubMed2005

A 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy.

This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.

Algorithms↗

PubMed2005

An evaluation of contrast enhancement techniques for mammographic breast masses.

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.

Algorithms↗

PubMed1996

Automated classification of renal interstitium and tubules by local texture analysis and a neural network.

OBJECTIVE: To segment renal interstitial space in order to automatically quantify renal cortical interstitial volume fraction (Vvint/cortex) by means of image analysis techniques. STUDY DESIGN: The study group consisted of 35 renal biopsies with different degrees of chronic interstitial damage. Biopsies were stained with Sirius red and digitized under polarized light. Two methods were employed to segment interstitial space: (1) interstitial bright particles were thresholded, and afterwards interstitial space was reconstructed with a morphologic operation, and (2) the texture of the surroundings of each pixel was quantified by means of local granulometry, and this information was employed as the input of a neural network in order to classify interstitial and tubular pixels. RESULTS: The correlation between Vvint/cortex obtained manually and both methods was r = .92. The first method produced some deformation of tubular contours and underestimated Vvint/cortex (beta = .70) when compared to the second approach (beta = .95) (P < .05). CONCLUSION: Two different algorithms based on image analysis techniques allow the classification of renal interstitial and tubular structures and consequently allow the automated and precise estimation of renal Vvint/cortex.

Adult↗

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↗