[show abstract][hide abstract] ABSTRACT: Airway diseases are frequently associated with morphological changes that may affect the physiology of the lungs. Accurate characterization of airways may be useful for quantitatively assessing prognosis and for monitoring therapeutic efficacy. The information gained may also provide insight into the underlying mechanisms of various lung diseases. We developed a computerized scheme to automatically segment the 3-D human airway tree depicted on computed tomography (CT) images. The method takes advantage of both principal curvatures and principal directions in differentiating airways from other tissues in geometric space. A "puzzle game" procedure is used to identify false negative regions and reduce false positive regions that do not meet the shape analysis criteria. The negative impact of partial volume effects on small airway detection is partially alleviated by repeating the developed differential geometric analysis on lung anatomical structures modeled at multiple iso-values (thresholds). In addition to having advantages, such as full automation, easy implementation and relative insensitivity to image noise and/or artifacts, this scheme has virtually no leakage issues and can be easily extended to the extraction or the segmentation of other tubular type structures (e.g., vascular tree). The performance of this scheme was assessed quantitatively using 75 chest CT examinations acquired on 45 subjects with different slice thicknesses and using 20 publicly available test cases that were originally designed for evaluating the performance of different airway tree segmentation algorithms.
IEEE transactions on medical imaging. 02/2011; 30(2):266-78.
[show abstract][hide abstract] ABSTRACT: This study investigated the relative efficiencies of a stereographic display and two monoscopic display schemes for detecting lung nodules in chest computed tomography (CT). The ultimate goal was to determine whether stereoscopic display provides advantages for visualization and interpretation of three-dimensional (3D) medical image datasets. A retrospective study that compared lung nodule detection performances achieved using three different schemes for displaying 3D CT data was conducted. The display modes included slice-by-slice, orthogonal maximum intensity projection (MIP), and stereoscopic display. One hundred lung-cancer screening CT examinations containing 647 nodules were interpreted by eight radiologists, in each of the display modes. Reading times and displayed slab thickness versus time were recorded, as well as the probability, location, and size for each detected nodule. Nodule detection performance was analyzed using the receiver operating characteristic method. The stereo display mode provided higher detection performance with a shorter interpretation time, as compared to the other display modes tested in the study, although the difference was not statistically significant. The analysis also showed that there was no difference in the patterns of displayed slab thickness versus time between the stereo and MIP display modes. Most radiologists preferred reading the 3D data at a slab thickness that corresponded to five CT slices. Our results indicate that stereo display has the potential to improve radiologists' performance for detecting lung nodules in CT datasets. The experience gained in conducting the study also strongly suggests that further benefits can be achieved through providing readers with additional functionality.
Journal of Digital Imaging 04/2010; 24(3):478-84. · 1.10 Impact Factor
[show abstract][hide abstract] ABSTRACT: The main purpose of this project is to investigate the feasibility and efficacy of using a stereo display workstation for lung cancer screening on CT images. The tasks included in this project are development and evaluation of stereo image projection and display for chest CT images, observer performance evaluation for the stereo display, and stereo feature analysis and comparison to the conventionally used display methods for lung cancer detection. During the funding period, we have made progress in following tasks. 1. We have built stereo display workstation for chest CT images and investigated effects of several commonly used compositing methods for nodule representation and detection in stereo CT images. Among these methods, conventional maximum intensity projection (MIP) produced the highest image contrast, but gave ambiguities in local geometric detail and texture, whereas averaging compositing resulted in the lowest contrast, but preserved geometric details. Distance-weighted MIP partially recovered geometric information, which was lost in images composited by conventional MIP, therefore is the best compositing method for stereo display. 2. Consensus truth of the cases collected for this project has been done by three experienced radiologists. 3. A pilot observer performance study was conducted. Six radiologists participated the pilot observer performance study. The study has three display modes, conventional slice-by-slice mode, conventional MIP display mode and stereo display mode. The performance of lung nodule detection are examined and compared for the three modes with Free-response Receiver Operating Characteristic (FROC) statistic method. The results indicate that the stereo display achieved the best performance followed by the slice by-slice display, and the conventional MIP display gave the worst performance, although there is no statistically significant difference between the three display modes.
[show abstract][hide abstract] ABSTRACT: The study was to explore the power and feasibility of using programmable graphics processing units (GPUs) for real-time rendering and displaying large 3D medical datasets for stereoscopic display workstation. Lung cancer screening CT images were used for developing GPU-based stereo rendering and displaying. The study was run on a personal computer with a 128 MB NVIDIA Quadro FX 1100 graphics card. The performance of rendering and displaying was measured and compared between GPU-based and central processing unit (CPU)-based programming. The results indicate that GPU-based programming was capable of rendering large 3D datasets at real-time interactive rates with stereographic displays.
Computerized Medical Imaging and Graphics 04/2008; 32(2):118-23. · 1.66 Impact Factor
[show abstract][hide abstract] ABSTRACT: The objective of our study was to assess ergonomic and diagnostic performance-related issues associated with the interpretation of digital breast tomosynthesis-generated examinations.
Thirty selected cases were read under three different display conditions by nine experienced radiologists in a fully crossed, mode-balanced observer performance study. The reading modes included full-field digital mammography (FFDM) alone, the 11 low-dose projections acquired for the reconstruction of tomosynthesis images, and the reconstructed digital breast tomosynthesis examination. Observers rated cases under the free-response receiver operating characteristic, as well as a screening paradigm, and provided subjective assessments of the relative diagnostic value of the two digital breast tomosynthesis-based image sets as compared with FFDM. The time to review and diagnose each case was also evaluated.
Observer performance measures were not statistically significant (p > 0.05) primarily because of the small sample size in this pilot study, suggesting that showing significant improvements in diagnosis, if any, will require a larger study. Several radiologists did perceive the digital breast tomosynthesis image set and the projection series to be better than FFDM (p < 0.05) for diagnosing this specific case set. The time to review, interpret, and rate the examinations was significantly different for the techniques in question (p < 0.05).
Tomosynthesis-based breast imaging may have great potential, but much work is needed before its optimal role in the clinical environment is known.
American Journal of Roentgenology 04/2008; 190(4):865-9. · 2.90 Impact Factor
[show abstract][hide abstract] ABSTRACT: Currently, breast cancer screening protocols are based on a woman's age, but not on other risk factors or on the physical characteristics of her breasts. One commonly cited risk factor is dense breast tissue. This study is part of an effort to provide basic information needed to develop automatically, individualized screening protocols, by clarifying the relationships between age, risk, breast composition, lesion conspicuity, and other factors. In this project, a database was established that includes 227 cancer negative cases and 116 cancer positive cases across a wide range of age groups. In the cancer positive cases, we included a subgroup in which the cancer had been missed in the previous exam. Using our physics based model of breast density, we quantified percentage of breast parenchyma as an index of density. Density distributions and changes over time were analyzed. The most significant finding within this data was a significantly slower density decrease over the time in the cancer positive group than in the cancer negative group, with no overall difference in the density distribution in those two groups. False negative cases were found to be significantly more dense than true positive cases. In addition, our results showed a trend of density decrease with increasing age, which is in agreement with others' widely reported results.
[show abstract][hide abstract] ABSTRACT: Using electrical impedance spectroscopy (EIS) technology to detect breast abnormalities in general and cancer in particular has been attracting research interests for decades. Large clinical tests suggest that current EIS systems can achieve high specificity (>= 90%) at a relatively low sensitivity ranging from 15% to 35%. In this study, we explore a new resonance frequency based electrical impedance spectroscopy (REIS) technology to measure breast tissue EIS signals in vivo, which aims to be more sensitive to small tissue changes. Through collaboration between our imaging research group and a commercial company, a unique prototype REIS system has been assembled and preliminary signal acquisition has commenced. This REIS system has two detection probes mounted in the two ends of a Y-shape support device with probe separation of 60 mm. During REIS measurement, one probe touches the nipple and the other touches to an outer point of the breast. The electronic system continuously generates sweeps of multi-frequency electrical pulses ranging from 100 to 4100 kHz. The maximum electric voltage and the current applied to the probes are 1.5V and 30mA, respectively. Once a "record" command is entered, multi-frequency sweeps are recorded every 12 seconds until the program receives a "stop recording" command. In our imaging center, we have collected REIS measurements from 150 women under an IRB approved protocol. The database includes 58 biopsy cases, 78 screening negative cases, and other "recalled" cases (for additional imaging procedures). We measured eight signal features from the effective REIS sweep of each breast. We applied a multi-feature based artificial neural network (ANN) to classify between "biopsy" and normal "non-biopsy" breasts. The ANN performance is evaluated using a leave-one-out validation method and ROC analysis. We conducted two experiments. The first experiment attempted to classify 58 "biopsy" breasts and 58 "non-biopsy" breasts acquired on 58 women each having one breast recommended for biopsy. The second experiment attempted to classify 58 "biopsy" breasts and 58 negative breasts from the set of screening negative cases. The areas under ROC curves are 0.679 +/- 0.033 and 0.606 +/- 0.035 for the first and the second experiment, respectively. The preliminary results demonstrate (1) even with this rudimentary system with only one paired probes there is a measurable signal of changes in breast tissue demonstrating the feasibility of applying REIS technology for identifying at least some women with highly suspicious breast abnormalities and (2) the electromagnetic asymmetry between two breasts may be more sensitive in detecting changes in the abnormal breast. To further improve the REIS system performance, we are currently designing a new REIS system with multiple electrical probes and a more sophisticated analysis scheme.
[show abstract][hide abstract] ABSTRACT: To improve radiologist's performance in lesion detection and diagnosis on 3D medical image dataset, we have conducted a pilot study to test viability and efficiency of the stereo display for lung nodule detection and classification. Using our previously developed stereo compositing methods, stereo image pairs were prestaged and precalculated from CT slices for real-time interactive display. Three display modes (i.e., stereoscopic 3D, orthogonal MIP and slice-by-slice) were compared for lung nodule detection and total of eight radiologists have participated this pilot study to interpret the images. The performance of lung nodule detection was analyzed and compared between the modes using FROC analysis. Subjective assessment indicates that stereo display was well accepted by the radiologists, despite some uncertainty of beneficial results due to the novelty of the display. The FROC analysis indicates a trend that, among the three display modes, stereo display resulted in the best performance of nodule detection followed by slice-based display, although no statistically significant difference was shown between the three modes. The stereo display of a stack of thin CT slices has the potential to clarify three-dimensional structures, while avoiding ambiguities due to tissue superposition. Few studies, however, have addressed actual utility of stereo display for medical diagnosis. Our preliminary results suggest a potential role of stereo display for improving radiologists' performance in medical detection and diagnosis, and also indicate some factors likely affect the performance with new display, such as novelty of the display, training effect from projected radiography interpretation and confidence with the new technology.
[show abstract][hide abstract] ABSTRACT: Many diagnostic problems involve the assessment of vascular structures or bronchial trees depicted in volumetric datasets, but previous algorithms for segmenting cylindrical structures are not sufficiently robust for them to be widely applied clinically. Local geometric information that is of importance in segmentation consists of voxel values and their first and second derivatives. First derivatives can be generalized to the gradient and more generally the structure tensor, while the second derivatives can be represented by Hessian matrices. It is desirable to exploit both kinds of information, at the same time, in any voxel classification process, but few segmentation algorithms have attempted to do this. This project compares segmentation based on the structure tensor to that based on the Hessian matrix, and attempts to determine whether some combination of the two can demonstrate better performance than either individually. To compare performance in a situation where a gold standard exists, the methods were tested on simulated tree structures. We generated 3D tree structures with varying amounts of added noise, and processed them with algorithms based on the structure tensor, the Hessian matrix, and a combination of the two. We applied an orientation-sensitive filter to smooth the tensor fields. The results suggest that the structure tensor by itself is more effective in detecting cylindrical structures than the Hessian tensor, and the combined tensor is better than either of the other tensors.
[show abstract][hide abstract] ABSTRACT: A workstation for testing the efficacy of stereographic displays for applications in radiology has been developed, and is currently being tested on lung CT exams acquired for lung cancer screening. The system exploits pre-staged rendering to achieve real-time dynamic display of slabs, where slab thickness, axial position, rendering method, brightness and contrast are interactively controlled by viewers. Stereo presentation is achieved by use of either frame-swapping images or cross-polarizing images. The system enables viewers to toggle between alternative renderings such as one using distance-weighted ray casting by maximum-intensity-projection, which is optimal for detection of small features in many cases, and ray casting by distance-weighted averaging, for characterizing features once detected. A reporting mechanism is provided which allows viewers to use a stereo cursor to measure and mark the 3D locations of specific features of interest, after which a pop-up dialog box appears for entering findings. The system's impact on performance is being tested on chest CT exams for lung cancer screening. Radiologists' subjective assessments have been solicited for other kinds of 3D exams (e.g., breast MRI) and their responses have been positive. Objective estimates of changes in performance and efficiency, however, must await the conclusion of our study.
[show abstract][hide abstract] ABSTRACT: Stereographic display has been proposed as a possible method of improving performance in reading computed tomographic (CT) examinations acquired for lung cancer screening. Optimizing such displays is important given the large volume of image data that must be evaluated for each of these examinations. This study is designed to explore certain tradeoffs between rendering methods designed for the stereo display of CT images.
Stereo CT image compositing methods, including distance-weighted averaging, distance-weighted maximum intensity projection (MIP), and conventional MIP, were applied to lung CT images and compared for lung nodule detection and characterization.
Using the Jonckheere test indicated a statistically significant (P < .01) increase in contrast among the three compositing methods. Wilcoxon-Mann-Whitney test showed significant differences in contrast between distance-weighted averaging and conventional MIP (P < .01) and between averaging and distance-weighted MIP (P < .05), but not between distance-weighted MIP and conventional MIP (P > .05). Conventional MIP compositing provided the highest image contrast, but produced ambiguities in local geometric detail and texture, whereas averaging resulted in the lowest contrast, but preserved geometric detail. Distance-weighted MIP partially recovered geometric information, which was lost in images composited by means of conventional MIP.
Our results indicate that distance-weighted MIP may be a better choice for nodule detection in stereo lung CT images for its high local contrast and partial preservation of geometric information, whereas compositing by means of distance-weighted averaging is preferable for nodule characterization. The relative clinical value of these compositing methods needs to be evaluated further.
[show abstract][hide abstract] ABSTRACT: The purpose of this study is to develop a new method for assessment of the reproducibility of computer-aided detection (CAD) schemes for digitized mammograms and to evaluate the possibility of using the implemented approach for improving CAD performance. Two thousand digitized mammograms (representing 500 cases) with 300 depicted verified masses were selected in the study. Series of images were generated for each digitized image by resampling after a series of slight image rotations. A CAD scheme developed in our laboratory was applied to all images to detect suspicious mass regions. We evaluated the reproducibility of the scheme using the detection sensitivity and false-positive rates for the original and resampled images. We also explored the possibility of improving CAD performance using three methods of combining results from the original and resampled images, including simple grouping, averaging output scores, and averaging output scores after grouping. The CAD scheme generated a detection score (from 0 to 1) for each identified suspicious region. A region with a detection score >0.5 was considered as positive. The CAD scheme detected 238 masses (79.3% case-based sensitivity) and identified 1093 false-positive regions (average 0.55 per image) in the original image dataset. In eleven repeated tests using original and ten sets of rotated and resampled images, the scheme detected a maximum of 271 masses and identified as many as 2359 false-positive regions. Two hundred and eighteen masses (80.4%) and 618 false-positive regions (26.2%) were detected in all 11 sets of images. Combining detection results improved reproducibility and the overall CAD performance. In the range of an average false-positive detection rate between 0.5 and 1 per image, the sensitivity of the scheme could be increased approximately 5% after averaging the scores of the regions detected in at least four images. At low false-positive rate (e.g., < or =average 0.3 per image), the grouping method alone could increase CAD sensitivity by 7%. The study demonstrated that reproducibility of a CAD scheme can be tested using a set of slightly rotated and resampled images. Because the reproducibility of true-positive detections is generally higher than that of false-positive detections, combining detection results generated from subsets of rotated and resampled images could improve both reproducibility and overall performance of CAD schemes.
Medical Physics 12/2004; 31(11):2964-72. · 2.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: Based on the need to increase the efficacy of chest CT for lung cancer screening, a stereoscopic display for viewing chest CT images has been developed. Stereo image pairs are generated from CT data by conventional stereo projection derived from a geometry that assumes the topmost slice being displayed is at the same distance as the screen of the physical display. Image grayscales are modified to make air transparent so that soft tissue structures of interest can be more easily seen. Because the process of combining multiple slices has a tendency to reduce the effective local contrast, we have included mechanisms to counteract this, such as linear and nonlinear local grayscale transforms. The physical display, which consists of a CRT viewed through shutter glasses, also provides for real-time adjustment of displayed thickness and axial position, as well as for changing brightness and contrast. While refinement of the stereo projection, contrast, and transparency models is ongoing, subjective evaluation of our current implementation indicates that the method has considerable potential for improving the efficiency of the detection of lung nodules. A more quantitative effort to assess its impact on performance, by ROC type methods, is underway.
[show abstract][hide abstract] ABSTRACT: We assessed performance changes of a mammographic computer-aided detection scheme when we restricted the maximum number of regions that could be identified (cued) as showing positive findings in each case.
A computer-aided detection scheme was applied to 500 cases (or 2,000 images), including 300 cases in which mammograms showed verified malignant masses. We evaluated the overall case-based performance of the scheme using a free-response receiver operating characteristic approach, and we measured detection sensitivity at a fixed false-positive detection rate of 0.4 per image after gradually reducing the maximum number of cued regions allowed for each case from seven to one.
The original computer-aided detection scheme achieved a maximum case-based sensitivity of 97% at 3.3 false-positive detected regions per image. For a detection decision score set at 0.565, the scheme had a 79% (237/300) case-based sensitivity, with 0.4 false-positive detected regions per image. After limiting the number of maximum allowed cued regions per case, the false-positive rates decreased faster than the true-positive rates. At a maximum of two cued regions per case, the false-positive rate decreased from 0.4 to 0.21 per image, whereas detection sensitivity decreased from 237 to 220 masses. To maintain sensitivity at 79%, we reduced the detection decision score to as low as 0.36, which resulted in a reduction of false-positive detected regions from 0.4 to 0.3 per image and a reduction in region-based sensitivity from 66.1% to 61.4%.
Limiting the maximum number of cued regions per case can improve the overall case-based performance of computer-aided detection schemes in mammography.
American Journal of Roentgenology 04/2004; 182(3):579-83. · 2.90 Impact Factor
[show abstract][hide abstract] ABSTRACT: The widespread adoption of chest CT for lung cancer screening will greatly increase the workload of chest radiologists. Contributing to this effort is the need for radiologists to differentiate between localized nodules and slices through linear structures such as blood vessels, in each of a large number of slices acquired for each subject. To increase efficiency and accuracy, thin slices can be combined to provide thicker slabs for presentation, but the resulting superposition of tissues can make it more difficult to detect and characterize smaller nodules. The stereo display of a stack of thin CT slices may be able to clarify three-dimensional structures, while avoiding the loss of resolution and ambiguities due to tissue superposition. The current work focuses on the development and evaluation of stereo projection models that are appropriate for chest CT. As slices are combined into a three dimensional structure, maximum image intensity, which is limited by the display, must be preserved. But, compositing methods that effectively average slices together typically reduce contrast of subtle nodules. For monoscopic viewing, orthographic maximum-intensity projection (MIP), of thick slabs, has been employed to overcome this effect, but this method provides no information of depth or of the geometrical relationships between structures. Our comparison of various rendering options indicates that a stereographic perspective transformation, used in conjunction with a compositing model that combines maximum-intensity projection with an appropriate brightness weighting function, shows promise for this application. The main drawback uncovered was that, for the images used in this study, the lung volume was undersampled in the z-direction, resulting in certain unavoidable image artifacts.
[show abstract][hide abstract] ABSTRACT: A method for quantitatively estimating lesion "size" from mammographic images was developed and evaluated. The main idea behind the measure, termed "integrated density" (ID), is that the total x-ray attenuation attributable to an object is theoretically invariant with respect to the projected view and object deformation. Because it is possible to estimate x-ray attenuation of a lesion from relative film densities, after appropriate corrections for background, the invariant property of the measure is expected to result in an objective method for evaluating the "sizes" of breast lesions. ID was calculated as the integral of the estimated image density attributable to a lesion, relative to surrounding background, over the area of the lesion and after corrections for the nonlinearity of the film characteristic curve. This effectively provides a measure proportional to lesion volume. We computed ID and more traditional measures of size (such as "mass diameter" and "effective size") for 100 pairs of ipsilateral mammographic views, each containing a lesion that was relatively visible in both views. The correlation between values calculated for each measure from corresponding pairs of ipsilateral views were computed and compared. All three size-related measures (mass diameter, effective size, and ID) exhibited reasonable linear relationship between paired views (r2>0.7, P<0.001). Specifically, the ID measures for the 100 masses were found to be highly correlated (r2=0.9, P<0.001) between ipsilateral views of the same mass. The correlation increased substantially (r2=0.95), when a measure with linear dimensions of length was defined as the cube root of ID. There is a high degree of correlation between ID-based measures obtained from different views of the same mass. ID-based measures showed a higher degree of invariance than mass diameter or effective size.
Medical Physics 07/2003; 30(7):1805-11. · 2.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: The authors evaluated performance changes in the detection of masses on "current" (latest) and "prior" images by computer-aided diagnosis (CAD) schemes that had been optimized with databases of current and prior mammograms.
The authors selected 260 pairs of matched consecutive mammograms. Each current image depicted one or two verified masses. All prior images had been interpreted originally as negative or probably benign. A CAD scheme initially detected 261 mass regions and 465 false-positive regions on the current images, and 252 corresponding mass regions (early signs) and 471 false-positive regions on prior images. These regions were divided into two training and two testing databases. The current and prior training databases were used to optimize two CAD schemes with a genetic algorithm. These schemes were evaluated with two independent testing databases.
The scheme optimized with current images produced areas under the receiver operating characteristic curve of (0.89 +/- 0.01 and 0.65 +/- 0.02 when tested with current images and prior images, respectively. The scheme optimized with prior images produced areas under the receiver operating characteristic curve of 0.81 +/- 0.02 and 0.71 +/- 0.02 when tested with current images and prior images, respectively. Performance changes for both current and prior testing databases were significant (P < .01) for the two schemes.
CAD schemes trained with current images do not perform optimally in detecting masses depicted on prior images. To optimize CAD schemes for early detection, it may be important to include in the training database a large fraction of prior images originally reported as negative and later proven to be positive.
[show abstract][hide abstract] ABSTRACT: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates.
We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard.
The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05).
Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.
American Journal of Roentgenology 02/2003; 180(1):257-62. · 2.90 Impact Factor
[show abstract][hide abstract] ABSTRACT: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms.
Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings.
Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well.
Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.
[show abstract][hide abstract] ABSTRACT: The purpose was to evaluate the effect of incorporating negative but suspicious regions into a knowledge-based computer-aided detection (CAD) scheme of masses depicted in mammograms. To determine if a suspicious region is "positive" for a mass, the region was compared not only with actually positive regions (masses), but also with known negative regions. A set of quantitative measures (i.e., a positive, a negative, and a combined likelihood measure) was computed. In addition, a process was developed to integrate two likelihood measures that were derived using two selected features. An initial evaluation with 300 positive and 300 negative regions was performed to determine the parameters associated with the likelihood measures. Then, an independent set of 500 positive and 500 negative regions was used to test the performance of the CAD scheme. During the training phase, the performance was improved from Az = 0.83 to 0.87 with the incorporation of negative regions and the integration process. During the independent test, the performance was improved from Az = 0.80 to 0.83. The incorporation of negative regions and the integration process was found to add information to the scheme. Hence, it may offer a relatively robust solution to differentiate masses from normal tissue in mammograms.