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Harold L Kundel

Publications and source records attributed to Harold L Kundel.

10 recordsLinked to original sources
PubMed2006

Pulmonary nodule detection in CT images with quantized convergence index filter.

A novel filter termed quantized convergence index filter (QCI filter) that is capable of enhancing the conspicuity of rounded lesions is proposed as part of a CAD (computer-aided diagnosis) scheme for detecting pulmonary nodules in computed tomography (CT) images. In this filter and its predecessor, the convergence index filter (CI filter), the output at a pixel represents the degree of convergence toward the pixel shown by the directions of gray-level gradients at surrounding pixels. The QCI filter and the CAD scheme were evaluated using five clinical datasets containing 50 nodules. With the support region of 9 x 9 pixels, the QCI filter showed more selective response to the nodules than the CI filter. In the CAD scheme, intermediate nodule candidates are generated based on the QCI filter output and then classified using linear discriminant analysis of eight features that are attributed to each intermediate nodule candidate. The QCI filter output level itself was used as one of the features. The scheme achieved a sensitivity of 90% with 1.67 false positives per slice. The QCI filter output level was most effective among the features in correctly classifying intermediate nodule candidates. The QCI filter is promising as a tool of preprocessing for automated pulmonary nodule detection in CT images.

Algorithms↗
PubMed2005

Gadodiamide T1 relaxivity in brain tissue in vivo is lower than in saline.

In vivo measurements of gadodiamide (Gd-DTPA-BMA) T(1) relaxivity were performed at 4.7 T in injured and normal rat brains. Cerebral lesions were induced in nine rats by a localized freezing method. T(1) maps of the lesions were generated before and after injection of Gd-DTPA-BMA (0.1-0.6 mmol/kg). Samples of normal and necrotic brain were collected postmortem; the wet and dry weights were determined, and Gd content was measured by inductively coupled plasma mass spectroscopy. The in vivo relaxivity was determined by a linear fit of a plot of the change in relaxation rate following injection of the contrast agent as a function of Gd content. This analysis yielded a relaxivity in the injured brain of 2.8 sec(-1) mmol(-1) kg tissue water at 36 degrees C. The water weight fraction was 0.90 +/- SD 0.02 wt/wt in injured brain and 0.79 +/- 0.02 in normal brain. Relaxivity measurements were also performed on solutions of Gd-DTPA-BMA (0.0-0.6 mmol) and albumin (0-30% wt/wt) in normal saline at room and physiologic temperatures. The relaxivity in the albumin/saline increased with increasing solids content with values of 4.0-4.9 sec(-1) mmol(-1)kg at 21 degrees C and 3.4-4.5 sec(-1) mmol(-1) kg at 37 degrees C. The relaxivity of the tissues differed significantly from that of the saline solutions of comparable solids content, suggesting that the solids content of a tissue is not the only factor that determines in vivo relaxivity.

Animals↗
PubMed2005

Perceptually based FROC analysis.

RATIONALE AND OBJECTIVES: Analysis of reading data when cases have multiple targets and/or the reader is required to localize targets is difficult. One approach to this free-response operating characteristic (FROC) problem is for images to be segmented (eg, with quadrants) by the investigator and a segment-level analysis be conducted with the case as a nesting factor. In this report, we introduce an alternative method that uses the visual scan path of the reader to segment the image. We evaluate the new method by applying it to data from a mammography reading experiment. MATERIALS AND METHODS: The gaze scan path of one radiologist was recorded as she scanned 40 mammograms for masses and microcalcifications. The observer is an experienced mammographer and was not one of the authors. In addition, the reader provided a rating indicating the degree of suspicion for any suspected targets she identified and localized. We then established "perceptual regions" by using a clustering algorithm on the visual fixations. We combined ratings given to specific locations indicated by the reader with the segmentation from the visual scan to generate a series of ratings classified for whether the perceptually based region associated with the rating contained or did not contain a known target. We analyzed data generated by our method from all 40 cases by using the conventional maximum-likelihood method based on the binormal model. Finally, we tested goodness-of-fit of the binormal model to the data by using chi-square. RESULTS: Maximum-likelihood estimation led to a model that did not fit the data (P < .001). However, examination of the observed and expected counts suggests that the binormal assumption does not hold for segments that contain targets and a bimodal distribution model might be preferred. CONCLUSION: Our new method provides an alternative approach to analysis of the FROC experiment. It needs to be developed further. Specifically, we propose that a mixture model extension of the binormal model be developed for ratings data arising from perceptually based FROC experiments. A disadvantage to our method is the requirement to record the scan path of the reader. However, we believe that adding such information to receiver operating characteristic (ROC) curve analysis will pay off when appropriate statistical models have been identified because we believe our data support our hypothesis that the perceptual scanning of images by humans deconvolves interpretation correlation. If true, this hypothesis implies that conventional statistical methods for ROC analysis based on independent data can be applied to the analysis of FROC data after conditioning on the scan path of the observer.

Data Interpretation, Statistical↗
PubMed2004

A novel method for analysis of TOMROP data.

PURPOSE: To develop an efficient method for extracting maps of the corrected T1 from images generated using the T One by Multiple Read Out Pulses (TOMROP) sequence. MATERIALS AND METHODS: An expression is developed for the true T1 in terms of the parameters from a three-parameter fit of the TOMROP data. Solutions of gadodiamide in normal saline with concentrations of 0.0, 0.06, 0.11, 0.23, 0.46, and 0.91 mM were prepared and T1 measurements were performed using both the inversion recovery (IR) and the TOMROP methods. The TOMROP data were analyzed using the proposed technique and the results compared to those from the IR measurements. RESULTS: The T1 estimates generated from the TOMROP data using the proposed method were consistent with the IR results. However, systematic errors were observed in the T1 estimates when the repetition time was not sufficient for full recovery of axial magnetization. Relaxation times determined using the proposed method were within 1% of the spectroscopiclly determined values for T1 values in the range of 0.28-2.8 seconds when a suitable delay was employed. CONCLUSION: The proposed method of analysis was found to yield accurate T1 estimates when the assumptions used in the analysis were not violated.

Contrast Media↗
PubMed2003

Measurement of observer agreement.

Statistical measures are described that are used in diagnostic imaging for expressing observer agreement in regard to categorical data. The measures are used to characterize the reliability of imaging methods and the reproducibility of disease classifications and, occasionally with great care, as the surrogate for accuracy. The review concentrates on the chance-corrected indices, kappa and weighted kappa. Examples from the imaging literature illustrate the method of calculation and the effects of both disease prevalence and the number of rating categories. Other measures of agreement that are used less frequently, including multiple-rater kappa, are referenced and described briefly.

Data Interpretation, Statistical↗
PubMed2003

The perception of breast cancers--a spatial frequency analysis of what differentiates missed from reported cancers.

The primary detector of breast cancer is the human eye. Radiologists read mammograms by mapping exogenous and endogenous factors, which are based on the image and observer, respectively, into observer-based decisions. These decisions rely on an internal schema that contains a representation of possible malignant and benign findings. Thus, to understand the hits and misses made by the radiologists, it is important to model the interactions between the measurable image-based elements contained in the mammogram and the decisions made. The image-based elements can be of two types, i.e., areas that attracted the visual attention of the radiologist, but did not yield a report, and areas where the radiologist indicated the presence of an abnormal finding. In this way, overt and covert decisions are made when reading a mammogram. In order to model this decision-making process, we use a system that is based upon the processing done by the human visual system, which decomposes the areas under scrutiny in elements of different sizes and orientations. In our system, this decomposition is done using wavelet packets (WPs). Nonlinear features are then extracted from the WP coefficients, and an artificial neural network is trained to recognize the patterns of decisions made by each radiologist. Afterwards, the system is used to predict how the radiologist will respond to visually selected areas in new mammogram cases.

Artificial Intelligence↗
PubMed2002

The perception of breast cancer: what differentiates missed from reported cancers in mammography?

RATIONALE AND OBJECTIVES: Mammographers map endogenous and exogenous factors into decisions whether to report the presence of a malignant finding in a mammogram case. Thus, to understand how image-based elements are translated into observer-based decisions, the authors used spatial frequency analysis to model the areas on mammograms that attracted visual attention, in addition to the areas localized as abnormal. MATERIALS AND METHODS: Four mammographers read 40 two-view mammogram cases, of which 30 contained at least one malignant lesion visible on one or two views. Their eye positions were recorded during visual search. Once the mammographer felt confident enough to provide an initial impression of the case ("normal" or "abnormal"), the eye position monitoring was turned off and the mammographer indicated, with a mouse-controlled cursor, the location and nature of any malignant findings. Regions that elicited an overt or a covert response by the mammographers were extracted for processing by means of wavelet packets and artificial neural networks. RESULTS: Different decision outcomes yielded different energy representations, in the spatial frequency domain. These energy representations were used by an artificial neural network to predict decision outcome in areas of interest, derived from eye position analysis, on mammograms from new cases. Individual trends were observed for each mammographer. CONCLUSION: Spatial frequency representation of regions that attracted a given mammographer's visual attention may be useful for characterizing how that mammographer will respond to the visually selected areas.

Analysis of Variance↗
PubMed2002

Time course of perception and decision making during mammographic interpretation.

OBJECTIVE: This article describes the time course of lesion detection on digital mammograms using data about both eye position and decision time to compare performance between experienced mammographers and trainees. Research indicates that a longer decision time works against performance in the interpretation of chest radiographs because the likelihood of error is increased, particularly for trainees. Is this relation between decision time and performance also true for interpreting mammograms? Is there an optimal decision time-performance trade-off for detecting breast lesions? MATERIALS AND METHODS: Six radiology trainees (experience, 302-976 cases) and three mammographers (experience, 3000-5000 cases per year) reviewed 40 test cases. Each test case was represented by two mammograms that showed different views of the same breast. Twenty breasts contained suspicious lesions, and 20 were lesion-free. An interactive computer display system with an eye-head tracker measured the timing of decisions, where visual attention was directed, and how much time was spent fixating on a region of interest for each decision. Eye position was monitored during an initial-decision phase, and decision times were measured throughout a final-decision phase during which suspicious lesions recognized initially were interpreted and localized. Performance was analyzed using localization receiver operating characteristic curves. RESULTS: The time course of interpreting mammograms is similar to that for interpreting chest radiographs. Mammographers detected 71% of the true lesions within 25 sec, and trainees detected 46% within 40 sec. Both a fixation dwell time of 1000 msec and a high level of confidence in the decision were associated with the detection of true lesions for the mammographers but not for the trainees. CONCLUSION: Mammographers detected most breast lesions by global recognition within 25 sec, but trainees took more time. Prolonging one's search beyond the global recognition phase yielded few new lesions and increased the risk of error.

Breast Neoplasms↗