Search PubMed⌕ Search

Biomedical subjects

L P Clarke

Publications and source records attributed to L P Clarke.

At least 19 recordsLinked to original sources
PubMed2001

National Cancer Institute initiative: Lung image database resource for imaging research.

Preliminary clinical studies suggest that spiral computed tomography (CT) of the lungs can improve early detection of lung cancer in high-risk individuals. More clinical studies are needed, however, before public health recommendations can be proposed for population-based screening. Spiral CT generates large-volume data sets and thus poses problems in terms of implementation of efficient and cost-effective screening methods. Image processing algorithms such as computer assisted diagnostic (CAD) methods have the potential to assist in lesion (eg, nodule) detection on spiral CT studies. CAD methods may also be used to characterize nodules by either assessing the stability or change in size of lesions based on evaluation of serial CT studies, or quantitatively measuring the temporal parameters related to contrast dynamics when using contrast material-enhanced CT studies. CAD methods therefore have the potential to enhance the sensitivity and specificity of spiral CT lung screening studies. Lung cancer screening studies now under investigation create an opportunity to develop an image database that will allow comparison and optimization of CAD algorithms. This database could serve as an important national resource for the academic and industrial research community that is currently involved in the development of CAD methods. The National Cancer Institute request for applications (RFA) (CA-01-001) has already been announced (April 2000) to establish and support a consortium of academic centers to develop this database, the consortium to be referred to as the Lung Image Database Consortium (LIDC). This RFA is now closed. Five academic sites have been selected to be members of the LIDC, the first meeting of this consortium is planned for spring of 2001, and a public meeting is to be held in 2002. This report is abstracted from the previously published RFA to serve as an example of how an initiative is developed by the National Cancer Institute to support a research resource. For specific details of the RFA, please access the following Internet site: http://www. nci.nih.gov/bip/NCI-DIPinisumm.htm#a11.

Algorithms↗
PubMed2001

NCI image archive management workshop: a preliminary report.

The National Cancer Institute organized a workshop entitled "Image Archive Management" that was presented on August 28 and 29, 2000, at the Natcher Conference Center on the National Institutes of Health (NIH) campus. The purpose of this workshop was to solicit expert input for the planned development of an archival system to make imaging databases readily accessible by the broad scientific community. The specific goals were to (a) define the technical requirements for a virtual archive of images used in oncology, (b) define the policy issues for access to these images, (c) recommend a process and phases for implementation of a robust imaging archival system, (d) review how this effort could be expanded and coupled with other ongoing efforts by NIH and other organizations interested in imaging, and (e) form an overall plan and policy to allow interoperability of image data archives. Representatives who attended the workshop came from academia, government agencies, and large and small businesses. A preliminary report was generated, as outlined herein, and additional reports are anticipated from the steering committee being organized as one of this workshop's recommendations, which is expected to be active by summer 2001. Additional information, including the list of participants in this workshop, is available at the Biomedical Imaging Program Web site (http://www.nci.nih.gov/bip/).

Humans↗
PubMed1999

Feature extraction for MRI segmentation.

Magnetic resonance images (MRIs) of the brain are segmented to measure the efficacy of treatment strategies for brain tumors. To date, no reproducible technique for measuring tumor size is available to the clinician, which hampers progress of the search for good treatment protocols. Many segmentation techniques have been proposed, but the representation (features) of the MRI data has received little attention. A genetic algorithm (GA) search was used to discover a feature set from multi-spectral MRI data. Segmentations were performed using the fuzzy c-means (FCM) clustering technique. Seventeen MRI data sets from five patients were evaluated. The GA feature set produces a more accurate segmentation. The GA fitness function that achieves the best results is the Wilks's lambda statistic when applied to FCM clusters. Compared to linear discriminant analysis, which requires class labels, the same or better accuracy is obtained by the features constructed from a GA search without class labels, allowing fully operator independent segmentation. The GA approach therefore provides a better starting point for the measurement of the response of a brain tumor to treatment.

Adult↗
PubMed1999

On the statistical nature of mammograms.

We show that digitized mammograms can be considered as evolving from a simple process. A given image results from passing a random input field through a linear filtering operation, where the filter transfer function has a self-similar characteristic. By estimating the functional form of the filter and solving the corresponding filtering equation, the analysis shows that the input field gray value distribution and spectral content can be approximated with parametric methods. The work gives a simple explanation for the variegated image appearance and multimodal character of the gray value distribution common to mammograms. Using the image analysis as a guide, a simulated mammogram is generated that has many statistical characteristics of real mammograms. Additional benefits may follow from understanding the functional form of the filter in conjunction with the input field characteristics that include the approximate parametric description of mammograms, showing the distinction between homogeneously dense and nondense images, and the development of mass analysis methods.

Breast Neoplasms↗
PubMed1998

MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation.

An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multispectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to "ground truth," (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measurement of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials.

Adult↗
PubMed1998

Digital mammography: hybrid four-channel wavelet transform for microcalcification segmentation.

RATIONALE AND OBJECTIVES: The authors evaluated an algorithm for the automatic segmentation of microcalcification clusters (MCCs) at digital mammography. Two- and four-channel wavelet transforms were evaluated to determine whether sensitivity in the detection of MCCs can be improved and if the selective reconstruction of the higher-order M2 subimages allows better preservation of the segmented MCCs, which is required for their classification. MATERIALS AND METHODS: The hybrid method involved the use of a nonlinear filter for image noise suppression coupled with wavelet transforms for image decomposition and an adaptive method for selective subimage reconstruction as a basis for segmentation of MCCs. The two- and four-channel wavelet transforms were implemented with different filter bank structures (i.e., polyphase quadrature mirror filters [QMFs], tree structure, and lattice structure) to determine if their computational efficiency can be improved while retaining properties such as near-perfect reconstruction. The hybrid wavelet transforms were applied to a common image database of biopsy-proved MCCs (100 images, 105-micron resolution, 12 bits deep; 52 cases with at least one MCC of varying subtlety [46 malignant and six benign cases] and eight normal cases). RESULTS: The two- and four-channel wavelet transforms yielded sensitivities of 93% and 94% and false-positive (PP) detection rates of 1.58 and 1.35 MCCs per image, respectively. The lattice structure provided greater than fivefold improvement in computational speed compared to the polyphase QMF structure, particularly for the higher order of channels (M = 4). CONCLUSION: The four-channel wavelet transform provided better sensitivity and FP detection rates and greater image detail preservation for the segmented MCCs.

Algorithms↗
PubMed1998

Fragmentary window filtering for multiscale lung nodule detection: preliminary study.

RATIONALE AND OBJECTIVES: The authors evaluated computer-assisted diagnostic (CAD) methods used to detect suspicious areas on lung radiographs. MATERIALS AND METHODS: The authors designed a fragmentary window filtering (FWF) algorithm for detecting lung nodule patterns, which generally appear as circular areas of high opacity on the chest radiograph. The FWF algorithm helps differentiate circular patterns from overlapping radiographic background. A multiscale analysis was performed to locate multiscale nodules. Receiver operating characteristic analysis was performed by using a lung nodule that was extracted from a chest radiograph. The nodule underwent scalings and subsequent superimposition onto 140 normal regions of interest from six chest radiographs. RESULTS: The FWF method was superior to the matched filtering method in the detection of suspicious areas. CONCLUSION: The proposed FWF-based method should provide improved detection of lung nodules on chest radiographs.

Algorithms↗
PubMed1998

Review and evaluation of MRI nonuniformity corrections for brain tumor response measurements.

Current MRI nonuniformity correction techniques are reviewed and investigated. Many approaches are used to remedy this artifact, but it is not clear which method is the most appropriate in a given situation, as the applications have been with different MRI coils and different clinical applications. In this work four widely used nonuniformity correction techniques are investigated in order to assess the effect on tumor response measurements (change in tumor volume over time): a phantom correction method, an image smoothing technique, homomorphic filtering, and surface fitting approach. Six brain tumor cases with baseline and follow-up MRIs after treatment with varying degrees of difficulty of segmentation were analyzed without and with each of the nonuniformity corrections. Different methods give significantly different correction images, indicating that rf nonuniformity correction is not yet well understood. No improvement in tumor segmentation or in tumor growth/shrinkage assessment was achieved using any of the evaluated corrections.

Biophysical Phenomena↗
PubMed1997

Normal brain volume measurements using multispectral MRI segmentation.

The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

Adult↗
PubMed1997

Monitoring brain tumor response to therapy using MRI segmentation.

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.

Adult↗
PubMed1997

Digital mammography: computer-assisted diagnosis method for mass detection with multiorientation and multiresolution wavelet transforms.

RATIONALE AND OBJECTIVES: The authors evaluated a modular computer-assisted diagnosis (CAD) method for mass detection that uses computation of features in three domains (gray level, morphology, and directional texture). Their objectives were to improve the sensitivity of detection and reduce the false-positive (FP) detection rate. MATERIALS AND METHODS: The directional wavelet transform (DWT) method, which uses both multiorientation and multiresolution wavelet transforms to improve image preprocessing and segmentation of suspicious areas and to extract both morphologic and directional texture features, was evaluated with a previously reported image database containing 50 normal and 45 abnormal digitized screen-film mammograms. The mammograms contained all mass types and included 16 minimal cancers. This method was compared with the Markov random field (MRF) method to avoid issues related to case selection criteria. Free-response receiver operating characteristic curves were compared for both DWT and MRF methods. RESULTS: For the DWT method, the sensitivity was 98% and the FP detection rate was 1.8 FP findings per image. For the MRF method, the sensitivity was 90% and the FP detection rate was 2.0 FP findings per image. CONCLUSION: The CAD method applied to the full mammographic image is automatic and independent of mass type. The segmentation of masses as performed with this method may potentially allow visual interpretation according to American College of Radiology criteria.

Algorithms↗
PubMed1997

Multiresolution statistical analysis of high-resolution digital mammograms.

A multiresolution statistical method for identifying clinically normal tissue in digitized mammograms is used to construct an algorithm for separating normal regions from potentially abnormal regions; that is, small regions that may contain isolated calcifications. This is the initial phase of the development of a general method for the automatic recognition of normal mammograms. The first step is to decompose the image with a wavelet expansion that yields a sum of independent images, each containing different levels of image detail. When calcifications are present, there is strong empirical evidence that only some of the image components are necessary for the purpose of detecting a deviation from normal. The underlying statistic for each of the selected expansion components can be modeled with a simple parametric probability distribution function. This function serves as an instrument for the development of a statistical test that allows for the recognition of normal tissue regions. The distribution function depends on only one parameter, and this parameter itself has an underlying statistical distribution. The values of this parameter define a summary statistic that can be used to set detection error rates. Once the summary statistic is determined, spatial filters that are matched to resolution are applied independently to each selected expansion image. Regions of the image that correlate with the normal statistical model are discarded and regions in disagreement (suspicious areas) are flagged. These results are combined to produce a detection output image consisting only of suspicious areas. This type of detection output is amenable to further processing that may ultimately lead to a fully automated algorithm for the identification of normal mammograms.

Algorithms↗
PubMed1997

Medical image analysis with fuzzy models.

This paper updates several recent surveys on the use of fuzzy models for segmentation and edge detection in medical image data. Our survey is divided into methods based on supervised and unsupervised learning (that is, on whether there are or are not labelled data available for supervising the computations), and is organized first and foremost by groups (that we know of!) that are active in this area. Our review is aimed more towards 'who is doing it' rather than 'how good it is'. This is partially dictated by the fact that direct comparisons of supervised and unsupervised methods is somewhat akin to comparing apples and oranges. There is a further subdivision into methods for two- and three-dimensional data and/or problems. We do not cover methods based on neural-like networks or fuzzy reasoning systems. These topics are covered in a recently published companion survey by keller et al.

Algorithms↗
PubMed1997

Medical image databases for CAD applications in digital mammography: design issues.

The evaluation of algorithms' developed for computer assisted diagnosis in digital mammography requires image databases that allow relative comparisons and assessment of algorithms clinical value. A review of the literature indicates that there is no consensus on the guidelines of how databases should be established. Image selection is usually done based on subjective criteria or availability. The generation of common database(s) available to the research community makes relative evaluations of algorithms with similar properties easier. However, questions regarding the "right database size," the "right image resolution," and the "right contents" remain. In this paper, database issues are reviewed and discussed and possible remedies to the various problems are proposed.

Algorithms↗
PubMed1996

Interpretation of calcifications in screen/film, digitized, and wavelet-enhanced monitor-displayed mammograms: a receiver operating characteristic study.

RATIONALE AND OBJECTIVES: The acceptance of filmless digital mammography is currently limited by digitization and display drawbacks, as well as bias toward hard-copy interpretation. In the current study, we evaluated a wavelet-based image enhancement method for the filmless interpretation of breast calcifications. METHODS: A set of 100 mammograms (58 with calcification clusters) was digitized at 105 microns and 4,096 gray levels per pixel and was processed with nonlinear filters and wavelets. Standard receiver operating characteristic analysis was performed by four radiologists, who independently read the films, the unprocessed digital images, and unprocessed and wavelet-enhanced digital images presented simultaneously. RESULTS: Statistical differences were observed between screen/film and unprocessed digitized mammography displayed on monitors. Differences were not significant when wavelet enhancement was included in the monitor display. Interobserver variation in the digitized reading was greater than in film reading, but the wavelet enhancement reduced the difference. CONCLUSION: Wavelet-enhanced digital mammograms may assist radiologists in diagnosing calcifications directly from computer monitors and may compensate for current technologic limitations. A study with a larger data-base is needed before this method is accepted for clinical use.

Breast Diseases↗