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1.  Representation of Lesion Similarity by Use of Multidimensional Scaling for Breast Masses on Mammograms 
Journal of Digital Imaging  2013;26(4):740-747.
Presentation of similar reference images can be useful for diagnosis of new lesions. A similarity map which can visually present the overview of the relationship between the lesions with different types may provide the supplemental information to the reference images. A new method for constructing the similarity map by multidimensional scaling (MDS) for breast masses on mammograms was investigated. Nine pathologic types were included; three regions of interests each from the nine groups were employed in this study. Subjective similarity ratings by expert readers were obtained for all possible 351 pairs of masses. Using the average ratings, MDS similarity map was created. Each axis of the MDS configuration was fitted by the linear model with 13 image features to reconstruct the similarity map. Dissimilarity based on the distance in the reconstructed space was determined and compared with the subjective rating. The MDS map consistently represented the similarity between cysts and fibroadenomas, invasive lobular carcinomas and scirrhous carcinomas, and ductal carcinomas in situ, solid–tubular carcinomas, and papillotubular carcinomas with the experts’ data. The correlation between the average subjective ratings and the dissimilarities based on the distance in the reconstructed feature space was much greater (−0.87) than that of the dissimilarities based on the distance in the conventional feature space (−0.65). The new similarity map by MDS can be useful for visualizing the relationship between breast masses with different pathologic types. It has potential usefulness in selecting the similarity measures and providing the supplemental information.
PMCID: PMC3705001  PMID: 23306711
Similarity map; Multidimensional scaling; Breast masses; Digital mammography
2.  Clinical Utility of Temporal Subtraction Images in Successive Whole-Body Bone Scans: Evaluation in a Prospective Clinical Study 
Journal of Digital Imaging  2010;24(4):680-687.
In order to aid radiologists’ routine work for interpreting bone scan images, we developed a computerized method for temporal subtraction (TS) images which can highlight interval changes between successive whole-body bone scans, and we performed a prospective clinical study for evaluating the clinical utility of the TS images. We developed a TS image server which includes an automated image-retrieval system, an automated image-conversion system, an automated TS image-producing system, a computer interface for displaying and evaluating TS images with five subjective scales, and an automated data-archiving system. In this study, the radiologist could revise his/her report after reviewing the TS images if the findings on the TS image were confirmed retrospectively on our clinical picture archiving and communication system. We had 256 consenting patients of whom 143 had two or more whole-body bone scans available for TS images. In total, we obtained TS images successfully in 292 (96.1%) pairs and failed to produce TS images in 12 pairs. Among the 292 TS studies used for diagnosis, TS images were considered as “extremely beneficial” or “somewhat beneficial” in 247 (84.6%) pairs, as “no utility” in 44 pairs, and as “somewhat detrimental” in only one pair. There was no TS image for any pairs that was considered “extremely detrimental.” In addition, the radiologists changed their initial reported impression in 18 pairs (6.2%). The benefit to the radiologist of using TS images in the routine interpretation of successive whole-body bone scans was significant, with negligible detrimental effects.
PMCID: PMC3138932  PMID: 20730471
Bone scintigram; whole-body scan; interval change; temporal subtraction image; prospective clinical study
3.  Evaluation of Objective Similarity Measures for Selecting Similar Images of Mammographic Lesions 
Journal of Digital Imaging  2010;24(1):75-85.
The purpose of this study was to investigate four objective similarity measures as an image retrieval tool for selecting lesions similar to unknown lesions on mammograms. Measures A and B were based on the Euclidean distance in feature space and the psychophysical similarity measure, respectively. Measure C was the sequential combination of B and A, whereas measure D was the sequential combination of A and B. In this study, we selected 100 lesions each for masses and clustered microcalcifications randomly from our database, and we selected five pairs of lesions from 4,950 pairs based on all combinations of the 100 lesions by use of each measure. In two observer studies for 20 mass pairs and 20 calcification pairs, six radiologists compared all combinations of 20 pairs by using a two-alternative forced-choice method to determine the subjective similarity ranking score which was obtained from the frequency with which a pair was considered as more similar than the other 19 pairs. In both mass and calcification pairs, pairs selected by use of measure D had the highest mean value of the average subjective similarity ranking scores. The difference between measures D and A (P = 0.008 and 0.024), as well as that between measures D and B (P = 0.018 and 0.028) were statistically significant for masses and microcalcifications, respectively. The sequential combination of the objective similarity measure based on the Euclidean distance and the psychophysical similarity measure would be useful in the selection of images similar to those of unknown lesions.
PMCID: PMC3046795  PMID: 20352281
Similarity measure; similar image; mass; clustered microcalcifications; mammogram
4.  Presentation of Similar Images as a Reference for Distinction Between Benign and Malignant Masses on Mammograms: Analysis of Initial Observer Study 
Journal of Digital Imaging  2010;23(5):592-602.
The effect of the presentation of similar images for distinction between benign and malignant masses on mammograms was evaluated in the observer performance study. Images of masses were obtained from the Digital Database for Screening Mammography. We selected 50 benign and 50 malignant masses by a stratified randomization method. For each case, similar images were selected based on the size of masses and the similarity measures. Radiologists were shown images with unknown masses and asked to provide their confidence level that the lesions were malignant before and after the presentation of the similar images. Eleven observers, including three attending breast radiologists, three breast imaging fellows, and five residents, participated. The average areas under the receiver operating characteristic curves without and with the presentation of the similar images were almost equivalent. However, there were many cases in which the similar images caused beneficial effects to the observers, whereas there were a small number of cases in which the similar images had detrimental effects. From a detailed analysis of the reasons for these detrimental effects, we found that the similar images would not be useful for diagnosis of rare and very difficult cases, i.e., benign-looking malignant and malignant-looking benign cases. In addition, these cases should not be included in the reference database, because radiologists would be confused by these unusual cases. The results of this study could be very important and useful for the future development and improvement of a computer-aided diagnosis system.
PMCID: PMC3046675  PMID: 20054606
Similar images; computer-aided diagnosis; breast masses; mammograms
5.  Observer Study for Evaluating Potential Utility of a Super-High-Resolution LCD in the Detection of Clustered Microcalcifications on Digital Mammograms 
We evaluated the potential utility of a newly developed liquid-crystal display (LCD), which used an independent sub-pixel drive (ISD) technique for increasing the spatial resolution of a standard LCD three times in one direction, by use of receiver operating characteristic (ROC) analysis and a two-alternative-forced-choice (2AFC) method to determine improvement in radiologists’ accuracy in the detection of clustered microcalcifications (MCLs) on digital mammograms. We used a standard LCD without and with the ISD technique, which can increase the spatial resolution of the LCD three times in one direction from three mega- to nine megapixels without changes in the size of the display. We used 60 single views of digital mammograms (30 with and 30 without clustered MCLs) for ROC studies and 60 regions of interest (ROIs) with clustered MCLs for 2AFC studies. In the ROC study, seven radiologists attempted to detect clustered MCLs without and with the ISD on the same LCD. In the 2AFC study, the same observer group compared the visibility of MCLs by use of the LCD without and with the ISD. Our institutional review board approved the use of this database and the participation of radiologists in this study. The accuracy in detecting clustered MCLs in the ROC study was improved by use of the LCD with the ISD, but the improvement was not statistically significant (p = 0.08). However, the superiority of the LCD with the ISD was demonstrated as significant (p < 0.001) in the 2AFC study. An LCD with ISD can improve the visibility of clustered MCLs when high-resolution digital mammograms are available.
PMCID: PMC3043779  PMID: 19277785
Digital mammography; observer performance; display device; receiver operating characteristic curve; digital display
6.  Development of a Voxel-Matching Technique for Substantial Reduction of Subtraction Artifacts in Temporal Subtraction Images Obtained from Thoracic MDCT 
A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as formation of new lesions and changes in existing abnormalities) on medical images by removing most of the normal structures. However, subtraction artifacts are commonly included in temporal subtraction images obtained from thoracic computed tomography and thus tend to reduce its effectiveness in the detection of pulmonary nodules. In this study, we developed a new method for substantially removing the artifacts on temporal subtraction images of lungs obtained from multiple-detector computed tomography (MDCT) by using a voxel-matching technique. Our new method was examined on 20 clinical cases with MDCT images. With this technique, the voxel value in a warped (or nonwarped) previous image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. With the voxel-matching technique, the correspondence not only between the structures but also between the voxel values in the current and the previous images is determined. To evaluate the usefulness of the voxel-matching technique for removal of subtraction artifacts, the magnitude of artifacts remaining in the temporal subtraction images was examined by use of the full width at half maximum and the sum of a histogram of voxel values, which may indicate the average contrast and the total amount, respectively, of subtraction artifacts. With our new method, subtraction artifacts due to normal structures such as blood vessels were substantially removed on temporal subtraction images. This computerized method can enhance lung nodules on chest MDCT images without disturbing misregistration artifacts.
PMCID: PMC3043742  PMID: 19020936
Temporal subtraction; nonlinear warping; computer-aided diagnosis; chest CT; image registration
7.  True Detection Versus “Accidental” Detection of Small Lung Cancer by a Computer-Aided Detection (CAD) Program on Chest Radiographs 
To evaluate the number of actual detections versus “accidental” detections by a computer-aided detection (CAD) system for small nodular lung cancers (≤30 mm) on chest radiographs, using two different criteria for measuring performance. A Food-and-Drug-Administration-approved CAD program (version 1.0; Riverain Medical) was applied to 34 chest radiographs with a “radiologist-missed” nodular cancer and 36 radiographs with a radiologist-mentioned nodule (a newer version 3.0 was also applied to the 36-case database). The marks applied by this CAD system consisted of 5-cm-diameter circles. A strict “nodule-in-center” criterion and a generous “nodule-in-circle” criterion were compared as methods for the calculation of CAD sensitivity. The increased sensitivities by the nodule-in-circle criterion were considered as nodules detected by chance. The number of false-positive (FP) marks was also analyzed. For the 34 radiologist-missed cancers, the nodule-in-circle criterion caused eight more cancers (24%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results. For the 36 radiologist-mentioned nodules, the nodule-in-circle criterion caused seven more lesions (19%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results, and three more lesions (8%) to be detected by chance when using the version 3.0 results. Version 1.0 yielded a mean of six FP marks per image, while version 3.0 yielded only three FP marks per image. The specific criteria used to define true- and false-positive CAD detections can substantially influence the apparent accuracy of a CAD system.
PMCID: PMC3043747  PMID: 19421813
Lung; neoplasms; computer-aided detection; chest radiography
8.  Integration of Temporal Subtraction and Nodule Detection System for Digital Chest Radiographs into Picture Archiving and Communication System (PACS): Four-year Experience 
Journal of Digital Imaging  2007;21(1):91-98.
Since May 2002, temporal subtraction and nodule detection systems for digital chest radiographs have been integrated into our hospital’s picture archiving and communication systems (PACS). Image data of digital chest radiographs were stored in PACS with the digital image and communication in medicine (DICOM) protocol. Temporal subtraction and nodule detection images were produced automatically in an exclusive server and delivered with current and previous images to the work stations. The problems that we faced and the solutions that we arrived at were analyzed. We encountered four major problems. The first problem, as a result of the storage of the original images’ data with the upside-down, reverse, or lying-down positioning on portable chest radiographs, was solved by postponing the original data storage for 30 min. The second problem, the variable matrix sizes of chest radiographs obtained with flat-panel detectors (FPDs), was solved by improving the computer algorithm to produce consistent temporal subtraction images. The third problem, the production of temporal subtraction images of low quality, could not be solved fundamentally when the original images were obtained with different modalities. The fourth problem, an excessive false-positive rate on the nodule detection system, was solved by adjusting this system to chest radiographs obtained in our hospital. Integration of the temporal subtraction and nodule detection system into our hospital’s PACS was customized successfully; this experience may be helpful to other hospitals.
PMCID: PMC3043823  PMID: 17333415
Nodule detection system; temporal subtraction; picture archiving and communication systems (PACS); computed radiography (CR) flat-panel detectors (FPDs)
9.  Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms 
Journal of Digital Imaging  2006;20(3):248-255.
This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.
PMCID: PMC3043897  PMID: 17122993
Mammography; computer-aided diagnosis; texture analysis
10.  Evaluation of the Image Quality of Temporal Subtraction Images Produced Automatically in a PACS Environment 
Journal of Digital Imaging  2006;19(4):383-390.
The aim of the study is to evaluate the reliable production of temporal subtraction images in a picture archiving and communication system environment and to establish objective criteria for the evaluation of image quality. A total of 117 temporal subtraction chest images (55 in the upright position, 62 in the supine position) were obtained in five consecutive days. In all of these, we confirmed that there were no interval changes on the original images, and cases with diffuse lung disease were excluded. The temporal subtraction images were classified by three chest radiologists into five levels: 5, excellent; 4, good; 3, acceptable; 2, poor; and 1, very poor. The following were examined: (1) the yield of adequate quality of the temporal subtraction images; (2) whether the temporal subtraction images were obtained in the warping or nonwarping mode; and (3) the correlation of the overall subjective image quality with the relative shift angles, relative shift distances, and the standard deviation of gray levels in the temporal subtraction images. The percentages of acceptable temporal subtraction images were 100% and 66% in the upright and supine positions, respectively. Sixteen (26%) of the 62 supine-position images were made in nonwarping mode, whereas all upright images were made in warping mode. Significant correlations were obtained in the relative shift angle (P < 0.05), relative horizontal shift distance (P < 0.05), and standard deviation of gray levels (P < 0.0001). Temporal subtraction images with acceptable image quality were obtained in the upright position. The objective criteria may be useful for the evaluation of image quality.
PMCID: PMC3045158  PMID: 16741663
Chest radiography; temporal subtraction; computer-assisted diagnosis; computer-assisted image interpretation; PACS
11.  Computer-Aided Nodule Detection on Digital Chest Radiography: Validation Test on Consecutive T1 Cases of Resectable Lung Cancer 
Journal of Digital Imaging  2006;19(4):376-382.
To evaluate the usefulness of a commercially available computer-assisted diagnosis (CAD) system on operable T1 cases of lung cancer by use of digital chest radiography equipment.
Materials and Methods
Fifty consecutive patients underwent surgery for primary lung cancer, and 50 normal cases were selected. All cancer cases were histopathologically confirmed T1 cases. All normal individuals were selected on the basis of chest computed tomography (CT) confirmation and were matched with cancer cases in terms of age and gender distributions. All chest radiographs were obtained with one computed radiography or two flat-panel detector systems. Eight radiologists (four chest radiologists and four residents) participated in observer tests and interpreted soft copy images by using an exclusive display system without and with CAD output. When radiologists diagnosed cases as positives, the locations of lesions were recorded on hard copies. The observers’ performance was evaluated by receiver operating characteristic analysis.
The overall detectability of lung cancer cases with CAD system was 74% (37/50), and the false-positive rate was 2.28 (114/50) false positives per case for normal cases. The mean Az value increased significantly from 0.896 without CAD output to 0.923 with CAD output (P = 0.018). The main cause of the improvement in performance is attributable to changes from false negatives without CAD to true positives with CAD (19/31, 61%). Moreover, improvement in the location of the tumor was observed in 1.5 cases, on average, for radiology residents.
This CAD system for digital chest radiographs is useful in assisting radiologists in the detection of early resectable lung cancer.
PMCID: PMC3045164  PMID: 16763934
Chest radiography; lung cancer; computer-aided nodule detection; screening; computer-assisted diagnosis; computer-assisted image interpretation; PACS
12.  Comparison of the quality of temporal subtraction images obtained with manual and automated methods of digital chest radiography 
Journal of Digital Imaging  1999;12(4):166-172.
The authors have been developing a fully automated temporal subtraction scheme to assist radiologists in the detection of interval changes in digital chest radiographs. The temporal subtraction image is obtained by subtraction of a previous image from a current image. The authors' automated method includes not only image shift and rotation techniques but also a nonlinear geometric warping technique for reduction of misregistration artifacts in the subtraction image. However, a manual subtraction method that can be carried out only with image shift and rotation has been employed as a common clinical technique in angiography, and it might be clinically acceptable for detection of interval changes on chest radiographs as well. Therefore, the authors applied both the manual and automated temporal subtraction techniques to 181 digital chest radiographs, and compared the quality of the subtraction images obtained with the two methods. The numbers of clinically acceptable subtraction images were 147 (81.2%) and 176 (97.2%) for the manual and automated subtraction methods, respectively. The image quality of 148 (81.8%) subtraction images was improved by use of the automated method in comparison with the subtraction images obtained with the manual method. These results indicate that the automated method with the nonlinear warping technique can significantly reduce misregistration artifacts in comparison with the manual method. Therefore, the authors believe that the automated subtraction method is more useful for the detection of interval changes in digital chest radiographs.
PMCID: PMC3452422  PMID: 10587911
computer-aided diagnosis; digital image subtraction; image matching; interval change; chest radiograph
13.  Application of temporal subtraction for detection of interval changes on chest radiographs: Improvement of subtraction images using automated initial image matching 
Journal of Digital Imaging  1999;12(2):77-86.
The authors developed a temporal subtraction scheme based on a nonlinear geometric warping technique to assist radiologists in the detection of interval changes in chest radiographs obtained on different occasions. The performance of the current temporal subtraction scheme is reasonably good; however, severe misregistration can occur in some cases. The authors evaluated the quality of 100 chest temporal subtraction images selected from their clinical image database. Severe misregistration was mainly attributable to initial incorrect global matching. Therefore, they attempted to improve the quality of the subtraction images by applying a new initial image matching technique to determine the global shift value between the current and the previous chest images. A cross-correlation method was employed for the initial image matching by use of blurred low-resolution chest images. Nineteen cases (40.4%) among 47 poor registered subtraction images were improved. These results show that the new initial image matching technique is very effective for improving the quality of chest temporal subtraction images, which can greatly enhance subtle changes in chest radiographs.
PMCID: PMC3452488  PMID: 10342250
computer-aided diagnosis; digital image subtraction; image matching; interval change; chest radiograph
14.  Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease 
Journal of Digital Imaging  1998;11(4):182-192.
The authors have developed an automated computeraided diagnostic (CAD) scheme by using artificial neural networks (ANNs) on quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs for distinguishing between normal and abnormal patterns. For training and testing of the second ANN, the vertical output patterns obtained from the 1st ANN were used for each ROI. The output value of the second ANN was used to distinguish between normal and abnormal ROIs with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a specified threshold level, the image was classified as abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images. The combination of the rule-based method and the third ANN also was applied to the classification between normal and abnormal chest images. The performance of the ANNs was evaluated by means of receiver operating characteristic (ROC) analysis. The average Az value (area under the ROC curve) for distinguishing between normal and abnormal cases was 0.976±0.012 for 100 chest radiographs that were not used in training of ANNs. The results indicate that the ANN trained with image data can learn some statistical properties associated with interstitial infiltrates in chest radiographs.
PMCID: PMC3453158  PMID: 9848051
interstitial infiltrate; computer-aided diagnosis; artificial neural network; chest radiograph
15.  Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks 
Journal of Digital Imaging  1997;10(3):108-114.
We devised an automated classification scheme by using the rule-based method plus artificial neural networks (ANN) for distinction between normal and abnormal lungs with interstitial disease in digital chest radiographs. Four measures used in the classification scheme are determined from the texture and geometric-pattern feature analyses. The rms variation and the first moment of the power spectrum of lung patterns aredetermined as measures for the texture analysis. In addition, the total area of nodular opacities and the total length of linear opacities are determined as measures for the geometric-pattern feature analysis. In our classification scheme with these measures, we identify obviously normal and abnormal cases first by the rule-based method and then ANN is applied for the remaining difficult cases. The rulebased plus ANN method provided a sensitivity of 0.926 at the specificity of 0.900, which was considerably improved compared to performance of either the rule-based method alone or ANNs alone.
PMCID: PMC3452953  PMID: 9268905
computer-aided diagnosis; interstitial lung disease; automated classification; chest radiography

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