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Nico Karssemeijer

Publications and source records attributed to Nico Karssemeijer.

18 recordsLinked to original sources
PubMed2007

Gray-scale and geometric registration of full-field digital and film-screen mammograms.

During the transition from traditional film-screen (FS) mammography to full-field digital (FFD) mammography, images from both modalities are used in hospitals and in mammography screening centers, as comparison of mammograms from subsequent examinations of a client is an important part of the diagnostic procedure. A parametric method is presented to register a FS mammogram and a FFD mammogram of the same woman with respect to geometry and gray-scales. The main motivation for the study is to lessen irrelevant differences between mammograms due to acquisition. First, a technique like this might increase the radiologist's ability to detect relevant differences like abnormal growth in breast tissue that signal breast cancer. Second, applications may be found in subtraction radiology or in computer-aided detection of abnormalities in temporal mammograms. The proposed method is based on a parametric model of the most important aspects of acquisition, which relates the pixel values of two images. This encompasses (1) breast positioning; (2) breast compression; (3) exposure time; (4) incident radiation intensity; and (5a) film properties and digitization for FS mammograms, or (5b) detector response for FFD mammograms. The method does not require a priori knowledge about specific settings of acquisition; the parameters are estimated from the two mammograms themselves.

Female↗
PubMed2007

Importance of comparison of current and prior mammograms in breast cancer screening.

PURPOSE: To retrospectively determine the influence of comparing current mammograms with prior mammograms on breast cancer detection in screening and to investigate a protocol in which prior mammograms are viewed only when necessary. MATERIALS AND METHODS: Institutional review board approval was not required. Participants gave written informed consent. Twelve experienced screening radiologists read 160 soft-copy screening mammograms twice, once with and once without prior mammograms. Eighty mammograms were obtained in women in whom breast cancer was diagnosed later; the other 80 mammograms had been reported as normal or benign. All cancers were visible in retrospect. Readers located potential abnormalities, estimated likelihood of malignancy for each finding, and indicated whether prior mammograms were considered necessary. The effect of prior mammograms on detection was determined by computing the mean lesion localized fraction in a range of low fractions of nonlesion locations corresponding to operating points in screening. Scores for both reading sessions were combined to assess the effect of making prior mammograms available only when requested. Data were analyzed by comparing the number of localized lesions between the two reading conditions with a paired two-tailed Student t test and applying a linear mixed model to test differences in average mean lesion localized fraction between reading conditions. P values less than .05 indicated statistical significance. RESULTS: Without prior mammograms, significantly more annotations were made. When only positive cases were considered, no difference was observed. Reading performance was significantly better when prior screening mammograms were available. At fixed lesion localized fraction, nonlesion localized fraction was reduced by 44% (P<.001) on average when prior mammograms were read. Performance was also increased for combined reading mode (ie, when prior mammograms were available on request only). However, this increase was smaller than that when prior mammograms were always available. Prior mammograms were requested in 24%-33% of all cases and were requested more often in positive cases. CONCLUSION: Comparison with prior mammograms significantly improves overall performance and can reduce referrals due to nonlesion locations. Limiting the availability of prior mammograms to cases selected by the reader reduces the beneficial effect of prior mammograms.

Aged↗
PubMed2006

Parameter estimation in stochastic mammogram model by heuristic optimization techniques.

The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or intervention programs. However, the efficiency of such a stochastic model depends on the accuracy of estimation of the model's parameter set. We propose a new approach-heuristic optimization-to estimate more accurately the model parameter set as compared to the conventional and popular expectation-maximization (EM) algorithm. After initial segmentation of a given mammogram, the finite generalized Gaussian mixture (FGGM) model is constructed by computing the statistics associated with different image regions. The model parameter set thus obtained is estimated by particle swarm optimization (PSO) and evolutionary programming (EP) techniques, where the objective function to be minimized is the relative entropy between the image histogram and the estimated density distributions. When our heuristic approach was applied to different categories of mammograms from the Mini-MIAS database, it yielded lower floor of estimation error in 109 out of 112 cases (97.3 %), and 101 out of 102 cases (99.0%), for the number of image regions being five and eight, respectively, with the added advantage of faster convergence rate, when compared to the EM approach. Besides, the estimated density model preserves the number of regions specified by the information-theoretic criteria in all the test cases, and the assessment of the segmentation results by radiologists is promising.

Algorithms↗
PubMed2006

Volumetric breast density estimation from full-field digital mammograms.

A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.

Adult↗
PubMed2006

Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views.

In this paper we present a method to link potentially suspicious mass regions detected by a Computer-Aided Detection (CAD) scheme in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For all possible combinations of mass candidate regions, a number of features are determined. These features include the difference in the radial distance from the candidate regions to the nipple, the gray scale correlation between both regions, and the mass likelihood of the regions determined by the single view CAD scheme. Linear Discriminant Analysis (LDA) is used to discriminate between correct and incorrect links. The method was tested on a set of 412 cancer cases. In each case a malignant mass, architectural distortion, or asymmetry was annotated. In 92% of these cases the candidate mass detections by CAD included the cancer regions in both views. It was found that in 82% of the cases a correct link between the true positive regions in both views could be established by our method. Possible applications of the method may be found in multiple view analysis to improve CAD results, and for the presentation of CAD results to the radiologist on a mammography workstation.

Algorithms↗
PubMed2005

Effect of soft-copy display supported by CAD on mammography screening performance.

Diagnostic performance and reading speed for conventional mammography film reading is compared to reading digitized mammograms on a dedicated workstation. A series of mammograms judged negative at screening and corresponding priors were collected. Half were diagnosed as cancer at the next screening, or earlier for interval cancers. The others were normal. Original films were read by fifteen experienced screening radiologists. The readers annotated potential abnormalities and estimated their likelihood of malignancy. More than 1 year later, five radiologists reread a subset of 271 cases (88 cancer cases having visible signs in retrospect and 183 normals) on a mammography workstation after film digitization. Markers from a computer-aided detection (CAD) system for microcalcifications were available to the readers. Performance was evaluated by comparison of A(z)-scores based on ROC and multiple-Reader multiple-case (MRMC) analysis, and localized receiver operating characteristic (LROC) analysis for the 271 cases. Reading speed was also determined. No significant difference in diagnostic performance was observed between conventional and soft-copy reading. Average A(z)-scores were 0.83 and 0.84 respectively. Soft-copy reading was only slightly slower than conventional reading. Using a mammography workstation including CAD for detection of microcalcifications, soft-copy reading is possible without loss of quality or efficiency.

Aged↗
PubMed2005

Interval change analysis to improve computer aided detection in mammography.

We are developing computer aided diagnosis (CAD) techniques to study interval changes between two consecutive mammographic screening rounds. We have previously developed methods for the detection of malignant masses based on features extracted from single mammographic views. The goal of the present work was to improve our detection method by including temporal information in the CAD program. Toward this goal, we have developed a regional registration technique. This technique links a suspicious location on the current mammogram with a corresponding location on the prior mammogram. The novelty of our method is that the search for correspondence is done in feature space. This has the advantage that very small lesions and architectural distortions may be found as well. Following the linking process several features are calculated for the current and prior region. Temporal features are obtained by combining the feature values from both regions. We evaluated the detection performance with and without the use of temporal features on a data set containing 2873 temporal film pairs from 938 patients. There were 589 cases in which the current mammogram contained exactly one malignant mass. Cross validation was used to partition the data set into a train set and a test set. The train set was used for feature selection and classifier training, the test set for classifier evaluation. FROC (free response operating characteristic) analysis showed an improvement in detection performance with the use of temporal features.

Aged↗
PubMed2005

Effect of recall rate on earlier screen detection of breast cancers based on the Dutch performance indicators.

BACKGROUND: The recall rate (i.e., the rate at which mammographically screened women are recalled for additional assessment) in the Dutch breast screening program (0.89% in 2000 for subsequent examinations) is the lowest worldwide, with possible consequences including higher rates of late-detected (i.e., "missed") interval and screen-detected cancers. To estimate the effect of changes in recall rate on earlier detection of cancers, we carried out a blinded review of interval and screen-detected cancers in the Dutch screening program. METHODS: A total of 495 sets of screen-negative mammograms (prediagnostic mammogram and the immediate previous mammogram) were collected from women participating in the biennial Dutch screening program. Of these, 250 were from control subjects, and 245 were from women who were subsequently diagnosed with breast cancer (123 interval and 122 screen-detected cancers). These mammograms were read by 15 radiologists who specialize in screening mammography and were blinded to outcome. Mean detection sensitivities for different false-positive rates were calculated using a linear mixed model. These results were used to calculate the effect of recall rate adjustment on earlier detection of cancers and numbers of false-positives. RESULTS: Increasing the recall rate to 2.0% would increase the detection rate from 4.20 per thousand to 4.52 per thousand due to the earlier detection of interval cancers. Moreover, 0.54 per thousand of the screen-detected cancers would be detected 2 years earlier (late screen-detected cancers). At recall rates of 3.0% and 4.0% the detection rate would increase to 4.58 per thousand and 4.63 per thousand, respectively, and 0.64 per thousand and 0.72 per thousand, respectively, of the screen-detected cancers would be detected 2 years earlier. For each 1.0% incremental increase in recall rate above 5.0%, the detection rate would increase by approximately 0.03 per thousand, with positive predictive values decreasing to below 10%. CONCLUSION: Breast cancer can be detected earlier by lowering the threshold for recall, especially for recall rates of 1%-4%. With further recall rate increases, cancer detection levels off with a disproportionate increase of false-positive rates.

Breast Neoplasms↗
PubMed2005

Use of prior mammograms in the classification of benign and malignant masses.

The purpose of this study was to determine the importance of using prior mammograms for classification of benign and malignant masses. Five radiologists and one resident classified mass lesions in 198 mammograms obtained from a population-based screening program. Cases were interpreted twice, once without and once with comparison of previous mammograms, in a sequential reading order using soft copy image display. The radiologists' performances in classifying benign and malignant masses without and with previous mammograms were evaluated with receiver operating characteristic (ROC) analysis. The statistical significance of the difference in performances was calculated using analysis of variance. The use of prior mammograms improved the classification performance of all participants in the study. The mean area under the ROC curve of the readers increased from 0.763 to 0.796. This difference in performance was statistically significant (P = 0.008).

Aged↗
PubMed2005

A regional registration method to find corresponding mass lesions in temporal mammogram pairs.

In this paper we develop an automatic regional registration method to find corresponding masses on prior and current mammograms. The method contains three steps. In the first, we globally align both images. Then, for each mass lesion on the current view, we define a search area on the prior view, which is likely to contain the same mass lesion. Third, at each location in this search area we calculate a registration measure to quantify how well this location matches the mass lesion on the current view. Finally we select the best location. To determine the performance of our method we compare it to several other registration methods. On a dataset of 389 temporal mass pairs our method correctly links 82% of prior and current mass lesions, whereas other methods achieve at most 72%.

Algorithms↗
PubMed2004

Noise equalization for detection of microcalcification clusters in direct digital mammogram images.

Equalizing image noise is shown to be an important step in the automatic detection of microcalcifications in digital mammography. This study extends a well established film-screen noise equalization scheme developed by Veldkamp et al. for application to full-field digital mammogram (FFDM) images. A simple noise model is determined based on the assumption that quantum noise is dominant in direct digital X-ray imaging. Estimation of the noise as a function of the gray level is improved by calculating the noise statistics using a truncated distribution method. Experimental support for the quantum noise assumption is presented for a set of step wedge phantom images. Performance of the noise equalization technique is also tested as a preprocessing stage to a microcalcification detection scheme. It is shown that the square root model based approach which FFDM allows leads to a robust estimation of the high frequency image noise. This provides better microcalcification detection performance when compared to the film-screen noise equalization method developed by Veldkamp. Substantially better results are obtained than when noise equalization is omitted. A database of 124 direct digital mammogram images containing 28 microcalcification clusters was used for evaluation of the method.

Algorithms↗
PubMed2004

Thickness correction of mammographic images by means of a global parameter model of the compressed breast.

Peripheral enhancement and tilt correction of unprocessed digital mammograms was achieved with a new reversible algorithm. This method has two major advantages for image visualization. First, the display dynamic range can be relatively small, and second, adjustment of the overall luminance to inspect details is not required in most cases. The correction is useful for preprocessing in computer-aided detection/diagnosis algorithms. The method is based on knowledge of the three-dimensional compressed breast shape to equalize thickness by adding virtual tissue, which results in intensity equalization for the mammographic image. Previously described methods implicitly estimate the contribution of thickness variations to image intensity, usually by nonparametric methods. The proposed method employs a global parametic breast shape model, which is advantageous for visualization and CAD.

Breast↗
PubMed2004

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.

Aged↗
PubMed2003

Computer-aided detection versus independent double reading of masses on mammograms.

PURPOSE: To evaluate the use of a computer-aided detection (CAD) system (designed for mammographic mass detection) to help improve mass interpretation and to compare CAD results with independent double-reading results. MATERIALS AND METHODS: Screening mammograms from 500 cases were collected; 125 of these cases were screening-detected cancers, and 125 were interval cancers. Previously obtained screening mammograms (ie, prior mammograms) were available in all cases. All mammograms were analyzed by a CAD system, which detected mass regions and assigned a level of (cancer) suspicion to each mass. Ten experienced screening radiologists read the prior mammograms. For independent interpretation with CAD, the suspicion rating assigned to each finding by the radiologist was weighted with the CAD output at the area of the finding. CAD markers on areas that were not reported by the radiologist were not used. Independent double reading was implemented by using a rule to combine the levels of suspicion assigned to findings by two radiologists. Results were evaluated by using localized-response receiver operating characteristic analysis. RESULTS: In a total of 141 cases, there was a visible abnormality at the location of the cancer on the prior mammogram, and 115 of these were classified as mass cases. For prior mammograms that depicted masses, the mean sensitivity of the radiologists, as averaged among the false-positive rates lower than 10%, was 39.4%; this increased by 7.0% with CAD and by 10.5% with double reading. Differences among single, double, and CAD readings were statistically significant (P <.001). CONCLUSION: Although independent double reading yields the best detection performance, the presence and probability of CAD mass markers can improve mammogram interpretation.

Aged↗
PubMed2003

Gray scale registration of mammograms using a model of image acquisition.

A parametric technique is proposed to match the pixel-value distributions of two mammograms of the same woman. It can be applied to mammograms of the left and the right breast, or, more effectively, to temporal mammograms, e.g., from two screening rounds. The main reason to match mammograms is to lessen irrelevant differences between images due to acquisition: by varying breast compression, different film types, et cetera. Firstly, a technique like this might reduce the radiologist's efforts to detect relevant differences like abnormal growth in breast tissue that signals breast cancer. Secondly, though not the aim of this study, applications might be found in subtraction radiology or in the computer aided detection of abnormalities in temporal mammograms. Instead of arbitrarily shifting and/or scaling the pixel-values of one image to match the other or directly mapping one histogram to the other, the proposed method is based on general aspects of acquisition. This encompasses (1) breast compression; (2) exposure time; (3) incident radiation intensity; and, (4a) film properties and digitization for screen-film mammograms, or (4b) detector response for unprocessed digital mammograms. The method does not require a priori knowledge about specific settings of acquisition to match histograms; the degrees of freedom are estimated from the pixel-value distributions of the two mammograms themselves. By the method it is possible to match digitized screen-film mammograms (in the next also referred to as analog mammograms) as well as unprocessed digital mammograms in any of the four possible combinations: analog to analog, analog to digital, digital to analog, and digital to digital.

Algorithms↗
PubMed2003

A comparison of methods for mammogram registration.

Mammogram registration is an important technique to optimize the display of cases on a digital viewing station, and to find corresponding regions in temporal pairs of mammograms for computer-aided diagnosis algorithms. Four methods for mammogram registration were tested and results were compared. The performance of all registration methods was measured by comparing the distance between annotations of abnormalities in the previous and current view before and after registration. Registration by mutual information outperformed alignment based on nipple location, alignment based on center of mass of breast tissue, and warping.

Algorithms↗
PubMed2003

The value of scatter removal by a grid in full field digital mammography.

Our objective in this study was to investigate the usefulness of an anti-scatter grid in digital mammography using a contrast detail phantom. The mammography system we investigated was a GE Senographe 2000D. We carried out phantom measurements under various conditions with and without using the anti-scatter grid. A new version of the CDMAM phantom (version 3.4) was used. This phantom consists of a matrix of square cells with disks of varying size and contrast. For given exposure conditions detectability of these disks can be determined and used for construction of contrast detail curves. Previously, a computer program was developed at our institute that performs a fully automatic analysis of the phantom recordings using the ideal observer model. Breast thickness was simulated by a homogeneous layer of PMMA in the range of 1 to 7 cm. Series of images were recorded for different KeV and target-filter combinations depending on the simulated thickness. The dose was kept constant for each thickness with and without using a grid. It appeared that image quality improved for simulated breast thickness below 5 cm when the grid was removed. In the range from 5 to 7 cm contrast detail curves obtained with or without a grid were similar. Results suggest that for compressed breast thickness in the range of 1 to 7 cm a grid might not be needed in the digital mammography system we investigated. Below 5 cm, omitting the grid may allow lower dose to the patient without losing image quality.

Equipment Failure Analysis↗