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Heang-Ping Chan

Publications and source records attributed to Heang-Ping Chan.

34 records · Page 2Linked to original sources
PubMed2004

Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial.

PURPOSE: To evaluate a noncommercial computer-aided detection (CAD) program for breast cancer detection with screening mammography. MATERIALS AND METHODS: A CAD program was developed for mammographic breast cancer detection. The program was applied to 2,389 patients' screening mammograms at two geographically remote academic institutions (institutions A and B). Thirteen radiologists who specialized in breast imaging participated in this pilot study. For each case, the individual radiologist performed a prospective Breast Imaging Reporting and Data System (BI-RADS) assessment after viewing of the screening mammogram. Subsequently, the radiologist was shown CAD results and rendered a second BI-RADS assessment by using knowledge of both mammographic appearance and CAD results. Outcome analysis of results of examination in patients recalled for a repeat examination, of biopsy, and of 1-year follow-up examination was recorded. Correct detection with CAD included a computer-generated mark indicating a possible malignancy on craniocaudal or mediolateral oblique views or both. RESULTS: Eleven (0.46%) of 2,389 patients had mammographically detected nonpalpable breast cancers. Ten (91%) of 11 (95% CI: 74%, 100%) cancers were correctly identified with CAD. Radiologist sensitivity without CAD was 91% (10 of 11; 95% CI: 74%, 100%). In 1,077 patients, follow-up findings were documented at 1 year. Five (0.46%) patients developed cancers, which were found on subsequent screening mammograms. The area where the cancers developed in two (40%) of these five patients was marked (true-positive finding) by the computer in the preceding year. Because of CAD results, a 9.7% increase in recall rate from 14.4% (344 of 2,389) to 15.8% (378 of 2,389) occurred. Radiologists' recall rate of study patients prior to use of CAD was 31% higher than the average rate for nonstudy cases (10.3%) during the same time period at institution A. CONCLUSION: Performance of the CAD program had a very high sensitivity of 91% (95% CI: 74%, 100%).

Adult↗
PubMed2004

Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium.

Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database.

Algorithms↗
PubMed2004

Computerized characterization of breast masses on three-dimensional ultrasound volumes.

We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

Algorithms↗
PubMed2004

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images.

Previous studies have found that mammographic breast density is highly correlated with breast cancer risk. Therefore, mammographic breast density may be considered as an important risk factor in studies of breast cancer treatments. In this paper, we evaluated the accuracy of using mammograms for estimating breast density by analyzing the correlation between the percent mammographic dense area and the percent glandular tissue volume as estimated from MR images. A dataset of 67 cases having MR images (coronal 3-D SPGR T1-weighted pre-contrast) and corresponding 4-view mammograms was used in this study. Mammographic breast density was estimated by an experienced radiologist and an automated image analysis tool, Mammography Density ESTimator (MDEST) developed previously in our laboratory. For the estimation of the percent volume of fibroglandular tissue in breast MR images, a semiautomatic method was developed to segment the fibroglandular tissue from each slice. The tissue volume was calculated by integration over all slices containing the breast. Interobserver variation was measured for 3 different readers. It was found that the correlation between every two of the three readers for segmentation of MR volumetric fibroglandular tissue was 0.99. The correlations between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by an experienced radiologist were both 0.91. The correlation between the percent volumetric fibroglandular tissue on MR images and the percent dense area of the CC and MLO views segmented by MDEST was 0.91 and 0.89, respectively. The root-mean-square (rms) residual ranged from 5.4% to 6.3%. The mean bias ranged from 3% to 6%. The high correlation indicates that changes in mammographic density may be a useful indicator of changes in fibroglandular tissue volume in the breast.

Absorptiometry, Photon↗
PubMed2004

An observer study comparing spot imaging regions selected by radiologists and a computer for an automated stereo spot mammography technique.

We are developing an automated stereo spot mammography technique for improved imaging of suspicious dense regions within digital mammograms. The technique entails the acquisition of a full-field digital mammogram, automated detection of a suspicious dense region within that mammogram by a computer aided detection (CAD) program, and acquisition of a stereo pair of images with automated collimation to the suspicious region. The latter stereo spot image is obtained within seconds of the original full-field mammogram, without releasing the compression paddle. The spot image is viewed on a stereo video display. A critical element of this technique is the automated detection of suspicious regions for spot imaging. We performed an observer study to compare the suspicious regions selected by radiologists with those selected by a CAD program developed at the University of Michigan. True regions of interest (TROIs) were separately determined by one of the radiologists who reviewed the original mammograms, biopsy images, and histology results. We compared the radiologist and computer-selected regions of interest (ROIs) to the TROIs. Both the radiologists and the computer were allowed to select up to 3 regions in each of 200 images (mixture of 100 CC and 100 MLO views). We computed overlap indices (the overlap index is defined as the ratio of the area of intersection to the area of interest) to quantify the agreement between the selected regions in each image. The averages of the largest overlap indices per image for the 5 radiologist-to-computer comparisons were directly related to the average number of regions per image traced by the radiologists (about 50% for 1 region/image, 84% for 2 regions/image and 96% for 3 regions/image). The average of the overlap indices with all of the TROIs was 73% for CAD and 76.8% +/- 10.0% for the radiologists. This study indicates that the CAD determined ROIs could potentially be useful for a screening technique that includes stereo spot mammography imaging.

Biophysical Phenomena↗
PubMed2004

Computerized nipple identification for multiple image analysis in computer-aided diagnosis.

Correlation of information from multiple-view mammograms (e.g., MLO and CC views, bilateral views, or current and prior mammograms) can improve the performance of breast cancer diagnosis by radiologists or by computer. The nipple is a reliable and stable landmark on mammograms for the registration of multiple mammograms. However, accurate identification of nipple location on mammograms is challenging because of the variations in image quality and in the nipple projections, resulting in some nipples being nearly invisible on the mammograms. In this study, we developed a computerized method to automatically identify the nipple location on digitized mammograms. First, the breast boundary was obtained using a gradient-based boundary tracking algorithm, and then the gray level profiles along the inside and outside of the boundary were identified. A geometric convergence analysis was used to limit the nipple search to a region of the breast boundary. A two-stage nipple detection method was developed to identify the nipple location using the gray level information around the nipple, the geometric characteristics of nipple shapes, and the texture features of glandular tissue or ducts which converge toward the nipple. At the first stage, a rule-based method was designed to identify the nipple location by detecting significant changes of intensity along the gray level profiles inside and outside the breast boundary and the changes in the boundary direction. At the second stage, a texture orientation-field analysis was developed to estimate the nipple location based on the convergence of the texture pattern of glandular tissue or ducts towards the nipple. The nipple location was finally determined from the detected nipple candidates by a rule-based confidence analysis. In this study, 377 and 367 randomly selected digitized mammograms were used for training and testing the nipple detection algorithm, respectively. Two experienced radiologists identified the nipple locations which were used as the gold standard. In the training data set, 301 nipples were positively identified and were referred to as visible nipples. Seventy six nipples could not be positively identified and were referred to as invisible nipples. The radiologists provided their estimation of the nipple locations in the latter group for comparison with the computer estimates. The computerized method could detect 89.37% (269/301) of the visible nipples and 69.74% (53/76) of the invisible nipples within 1 cm of the gold standard. In the test data set, 298 and 69 of the nipples were classified as visible and invisible, respectively. 92.28% (275/298) of the visible nipples and 53.62% (37/69) of the invisible nipples were identified within 1 cm of the gold standard. The results demonstrate that the nipple locations on digitized mammograms can be accurately detected if they are visible and can be reasonably estimated if they are invisible. Automated nipple detection will be an important step towards multiple image analysis for CAD.

Algorithms↗
PubMed2004

Combination of digital mammography with semi-automated 3D breast ultrasound.

This paper describes work aimed at combining 3D ultrasound with full-field digital mammography via a semi-automatic prototype ultrasound scanning mechanism attached to the digital mammography system gantry. Initial efforts to obtain high x-ray and ultrasound image quality through a compression paddle are proving successful. Registration between the x-ray mammogram and ultrasound image volumes is quite promising when the breast is stably compressed. This prototype system takes advantage of many synergies between the co-registered digital mammography and pulse-echo ultrasound image data used for breast cancer detection and diagnosis. In addition, innovative combinations of advanced US and X-ray applications are being implemented and tested along with the basic modes. The basic and advanced applications are those that should provide relatively independent information about the breast tissues. Advanced applications include x-ray tomosynthesis, for 3D delineation of mammographic structures, and non-linear elasticity and 3D color flow imaging by ultrasound, for mechanical and physiological information unavailable from conventional, non-contrast x-ray and ultrasound imaging.

Adult↗
PubMed2003

Effects of magnification and zooming on depth perception in digital stereomammography: an observer performance study.

We are evaluating the application of stereoscopic imaging to digital mammography. In the current study, we investigated the effects of magnification and zooming on depth perception. A modular phantom was designed which contained six layers of 1-mm-thick Lexan plates, each spaced 1 mm apart. Eight to nine small, thin nylon fibrils were pasted on each plate in horizontal or vertical orientations such that they formed 25 crossing fibril pairs in a projected image. The depth separation between each fibril pair ranged from 2 to 10 mm. A change in the order of the Lexan plates changed the depth separation of the two fibrils in a pair. Stereoscopic image pairs of the phantom were acquired with a GE full-field digital mammography system. Three different phantom configurations were imaged. All images were obtained using a Rh target/Rh filter spectrum at 30 kVp tube potential and a +/- 3 stereo shift angle. Images were acquired in both contact and 1.8X magnification geometry and an exposure range of 4 to 63 mAs was employed. The images were displayed on a Barco monitor driven by a Metheus stereo graphics board and viewed with LCD stereo glasses. Five observers participated in the study. Each observer visually judged whether the vertical fibril was in front of or behind the horizontal fibril in each fibril pair. It was found that the accuracy of depth discrimination increased with increasing fibril depth separation and x-ray exposure. The accuracy was not improved by electronic display zooming of the contact stereo images by 2X. Under conditions of high noise (low mAs) and small depth separation between the fibrils, the observers' depth discrimination ability was significantly better in stereo images acquired with geometric magnification than in images acquired with a contact technique and displayed with or without zooming. Under our experimental conditions, a 2 mm depth discrimination was achieved with over 60% accuracy on contact images with and without zooming, and with over 90% accuracy on magnification images. This study indicates that stereoscopic imaging, especially with magnification, may be useful for visualizing the spatial distribution of microcalcifications in a cluster and for differentiating overlapping tissues from masses on mammograms.

Breast↗
PubMed2003

Evaluation of the transmitted exposure through lead equivalent aprons used in a radiology department, including the contribution from backscatter.

A study was conducted to evaluate the radiation transmission through lead equivalent aprons that are used in a radiology department. A large area beam (poor geometry) was employed for the transmission measurements, and backscatter was simulated by placing 7" of Lucite behind each apron. Separate ionization chambers were used to measure the incident and transmitted x-ray beams. Transmission measurements were made at 70 kVp and 100 kVp through aprons and protective shields from eight different vendors that were marked 0.25 mm and 0.5 mm lead equivalent. Transmissions through 0.254 mm and 0.508 mm of pure lead were also measured and were compared with the transmissions through the lead equivalent materials. In addition, the area densities of the aprons were measured to compare radiation transmission with respect to the weights of the aprons. At 70 kVp, the transmission through 0.254 mm of pure lead was 5.4% and the transmissions through the 0.25 mm lead equivalent materials were 4.3% to 10.2% with a mean value of 7.1% and a standard deviation (s.d.) of 1.4%. At 100 kVp, the values were 15% for 0.254 mm pure lead and 12.3% to 20.7% (mean 16.8%, s.d. 2.1%) for the 0.25 mm lead equivalent materials. The transmission through the 0.508 mm pure lead sample was 0.9% at 70 kVp, and the corresponding transmissions through the 0.5 mm lead equivalent materials were 0.6% to 1.6% (mean 1.0%, s.d. 0.2%). At 100 kVp, the transmission through the 0.508 mm lead sample was 5% and those through the 0.5 mm lead equivalent materials were 3.5% to 6.7% (mean 4.9%, s.d. 0.7%). The radiation transmissions at 70 kVp, through two "lead-free" 0.5 mm lead equivalent aprons, were 1.7% and 1.9% and at 100 kVp the transmissions were 6.1% and 6.8%, respectively. This study indicates that there is a need to establish methods for acceptance testing of aprons and a need to establish acceptance limits for the x-ray transmission of aprons at specific kVp values. There is also a need for the establishment of appropriate methods and frequencies of routine quality assurance testing of radiation protection aprons.

Equipment Failure Analysis↗
PubMed2002

Optimal neural network architecture selection: improvement in computerized detection of microcalcifications.

RATIONALE AND OBJECTIVES: The authors evaluated the effect of optimal neural network architecture selection on the performance of a computer-aided diagnostic system designed to detect microcalcification clusters on digitized mammograms. MATERIALS AND METHODS: The authors developed a computer program to detect microcalcification clusters automatically on digitized mammograms. Previously, they found that a properly selected and trained convolution neural network (CNN) could reduce false-positive (FP) findings and therefore improve the accuracy of microcalcification detection. In the current study, they evaluated the effectiveness of the CNN optimized with an automated optimization technique in improving the accuracy of the microcalcification detection program, comparing it with the manually selected CNN. An independent test data set was used, which included 472 mammograms selected from the University of South Florida public database and contained 253 biopsy-proved malignant clusters. RESULTS: At an FP rate of 0.7 cluster per image, the film-based sensitivity was 84.6% for the optimized CNN, compared with 77.2% for the manually selected CNN. For clusters imaged on both craniocaudal and mediolateral oblique views, a cluster could be considered detected when it was detected on one or both views. For this case-based approach, at an FP rate of 0.7 per image, the sensitivity was 93.3% for the optimized and 87.0% for the manually selected CNN. CONCLUSION: The classification of true and false signals is an important step in the microcalcification detection program. An optimized CNN can effectively reduce FP findings and improve the accuracy of the computer-aided detection system.

Breast Neoplasms↗
PubMed2002

Improvement of computerized mass detection on mammograms: fusion of two-view information.

Recent clinical studies have proved that computer-aided diagnosis (CAD) systems are helpful for improving lesion detection by radiologists in mammography. However, these systems would be more useful if the false-positive rate is reduced. Current CAD systems generally detect and characterize suspicious abnormal structures in individual mammographic images. Clinical experiences by radiologists indicate that screening with two mammographic views improves the detection accuracy of abnormalities in the breast. It is expected that the fusion of information from different mammographic views will improve the performance of CAD systems. We are developing a two-view matching method that utilizes the geometric locations, and morphological and textural features to correlate objects detected in two different views using a prescreening program. First, a geometrical model is used to predict the search region for an object in a second view from its location in the first view. The distance between the object and the nipple is used to define the search area. After pairing the objects in two views, textural and morphological characteristics of the paired objects are merged and similarity measures are defined. Linear discriminant analysis is then employed to classify each object pair as a true or false mass pair. The resulting object correspondence score is combined with its one-view detection score using a fusion scheme. The fusion information was found to improve the lesion detectability and reduce the number of FPs. In a preliminary study, we used a data set of 169 pairs of cranio-caudal (CC) and mediolateral oblique (MLO) view mammograms. For the detection of malignant masses on current mammograms, the film-based detection sensitivity was found to improve from 62% with a one-view detection scheme to 73% with the new two-view scheme, at a false-positive rate of 1 FP/image. The corresponding cased-based detection sensitivity improved from 77% to 91%.

Algorithms↗
PubMed2002

Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.

We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified by a k-means clustering technique. Each lung slice is classified as belonging to the upper, middle, or the lower part of the lung volume. Within each lung region, structures are segmented again using weighted k-means clustering. These structures may include true lung nodules and normal structures consisting mainly of blood vessels. Rule-based classifiers are designed to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We performed a preliminary study using 1454 CT slices from 34 patients with 63 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (53/63) with 5.48 (7961/1454) FP objects per slice. When rule-based classification was used before LDA, the free response receiver operating characteristic (FROC) curve improved over the entire sensitivity and specificity ranges of interest. In particular, the FP rate decreased to 1.74 (2530/1454) objects per slice at the same sensitivity. Thus, compared to FP reduction with LDA alone, the inclusion of rule-based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images.

Adolescent↗
PubMed2002

The effects of stereo shift angle, geometric magnification and display zoom on depth measurements in digital stereomammography.

We are developing virtual three-dimensional (3-D) cursors for measuring depths in digital stereomammograms. We performed a study to investigate the effects of stereo shift angle, geometric magnification, and display zoom on the accuracy of depth measurements made with a virtual 3-D cursor. A phantom containing 50 low contrast fibrils at depths ranging from 1 to 11 mm was imaged with a full-field digital mammography system. Left- and right-eye images were generated at stereo shift angles of +/-3 degrees and +/-6 degrees, using either contact or 1.8x geometric magnification geometry. The images were viewed on a high-resolution stereoscopic display system in normal and 2x zoom mode. Observers viewed the images with stereo glasses and adjusted the depth of a cross-shaped virtual cursor to best match the perceived depth of each fibril. The results for two trained observers with excellent stereo acuity were nearly identical when viewing the same images. The average root mean square errors for the two observers were 1.2 mm (+/-3 degrees contact, no zoom), 1.3 mm (+/-3 degrees contact zoom), 0.8 mm (+/-6 degrees contact, no zoom), 0.6 mm (+/-6 degrees contact, zoom), 0.8 mm (+/-3 magnification, no zoom), 0.7 mm (+/-3 degrees magnification, zoom), and 0.2 mm (+/-6 degrees magnification, no zoom). One observer repeated the entire study for two additional fibril phantom configurations. Combining all the results, we found that for the contact geometry increasing the stereo shift angle from +/-3 degrees to +/-6 degrees improved the depth measurement accuracy by factors of about 1.2-4.0. Zooming did not provide observable improvement in the depth measurement accuracy; sometimes having no effect, sometimes improving the accuracy, and other times reducing the accuracy, with no general trends. Its effect is likely within experimental errors. However, the stereo effect was more readily visualized in the zoom mode. Geometric magnification improved the depth measurement accuracy. The best accuracy among all cases was about 0.2 mm, obtained with geometric magnification using a stereo angle of +/-6 degrees. This is the mode we recommend for obtaining accurate depth measurements with virtual cursors in stereomammograms.

Computer Graphics↗
PubMed2002

Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis -- experience in 263 patients.

PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per mammogram, respectively. The difference between malignant mass-detection performance in subsets of cases collected at each institution was found to be less than 1%. The detection accuracy for benign masses was lower than that for malignant masses. CONCLUSION: This mass-detection algorithm had a high sensitivity for detection of malignant masses. It may be useful as a second opinion in mammographic interpretation.

Algorithms↗