Hideya Takeo

Computerized detection of malignant tumors on digital mammograms

This paper presents a tumor detection system for fully digital mammography. The processing scheme adopted in the proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which characterize malignant tumors. For the first problem, a unique adaptive filter called the iris filter is proposed. It is very effective in enhancing approximately rounded opacities no matter what their contrasts might be. Clues for differentiation between malignant tumors and other tumors are believed to be mostly in their border areas. This paper proposes typical parameters which reflect boundary characteristics. To confirm the system performance for unknown samples, large scale experiments using 1212 CR images were performed. The results showed that the sensitivity of the proposed system was 90.5% and the average number of false positives per image was found to be only 1.3. These results show the effectiveness of the proposed system.

Computer-aided diagnosis in full digital mammography

RATIONALE AND OBJECTIVES The authors clarify the detection rates for breast cancerous tumors and clustered microcalcifications with computer-aided diagnosis (CAD) based on Fuji Computed Radiography. The authors also determine whether mammographic reading with CAD contributes to the discovery of breast cancer. METHODS Data acquired by Fuji Computed Radiography 9000, which consisted of 4148 digital mammograms including 267 cases of breast cancer, was transferred directly to an analysis workstation where an original software program determined extraction rates for breast tumors and clustered microcalcifications. Furthermore, using another 344 mammograms from 86 women, observer performance studies were conducted on five doctors for receiver operating characteristic (ROC) analysis. RESULTS Sensitivity to breast cancerous tumors and clustered microcalcifications were 89.9% and 92.8%, respectively false-positive rates were 1.35 and 0.40 per image, respectively. The observer performance studies indicate that an average Az value for the five doctors was greater with the CAD system than with a film-only reading without CAD, and that a reading with CAD was significantly superior at P < 0.022. CONCLUSIONS It has been shown that CAD based on Fuji Computed Radiography offers good detection rates for both breast cancerous tumors and clustered microcalcifications, and that the reading of mammograms with this CAD system would provide potential improvement in diagnostic accuracy for breast cancer.

Stationary grid pattern removal using 2D technique for moire-free radiographic image display

The striped patterns are superimposed in the radiographic images exposed with the stationary grid. When those images are displayed on a monitor, the scaling process causes the low frequency moire patterns overlapped over the object shadow. To prevent these moire patterns, it is necessary to remove the grid patterns before scaling process. The 1-dimenstional filtering can remove the grid pattern, on the other hand it removes some diagnostic information too. We developed two different grid pattern removal processes using 2-dimensional technique. The 2-dimensional technique can localize the information 2-dimensionally in frequency domain, so that the localized information includes the grid information. So the 2-dimensional method can remove the grid pattern with minimum loss of diagnostic information. Quality of images processed by the two 2-dimensional methods and the conventional 1-dimensional filtering method were evaluated. No grid patterns were observed in the images processed by three methods. However, as compared with the 1-dimensional filtered image, the images processed by the 2-dimensional methods were much sharper and have more detail information.

Detection System of Clustered Microcalcifications on CR Mammogram

CR (Computed Radiography) is characterized by high sensitivity and wide dynamic range. Moreover, it has the advantage of being able to transfer exposed images directly to a computer-aided detection (CAD) system which is not possible using conventional film digitizer systems. This paper proposes a high-performance clustered microcalcification detection system for CR mammography. Before detecting and classifying candidate regions, the system preprocesses images with a normalization step to take into account various imaging conditions and to enhance microcalcifications with weak contrast. Large-scale experiments using images taken under various imaging conditions at seven hospitals were performed. According to analysis of the experimental results, the proposed system displays high performance. In particular, at a true positive detection rate of 97.1%, the false positive clusters average is only 0.4 per image. The introduction of geometrical features of each microcalcification for identifying true microcalcifications contributed to the performance improvement. One of the aims of this study was to develop a system for practical use. The results indicate that the proposed system is promising.

Tumor Detection System for Full-Digital Mammography

Mammogram is considered to be the most reliable modality for the screening of breast cancer, and screening programs using mammography has become popular for the detection of early breast cancer. Computer aided diagnosis (CAD) system for mammography has the possibility to be used as a second reader to increase the reliability of the mass screening.

Diagnosis of breast cancer with multidetector computed tomography: analysis of optimal delay time after contrast media injection.

PURPOSE The aim of this study was to investigate the optimal delay time after a contrast media injection for multidetector computed tomography (MD-CT) images in the diagnosis of breast cancer patients. MATERIALS AND METHODS Thirty-one patients who underwent MD-CT for their preoperative examination and who had postoperatively confirmed pathology were enrolled. Four-phase images of dynamic contrast enhanced study were acquired using four-detector MDCT. All cases were mammographically classified into two groups according to BI-RADS: nondense and dense groups. The CT value of the background mammary gland, background breast enhancement (BBE), and tumor-background mammary gland contrast (TBC) were compared between the two groups. RESULTS The CT value of the dense group was significantly higher than that of the nondense group in all phases. BBE in both nondense and dense groups showed no significant differences in any of the phases. In the nondense group, TBC was significantly higher in both the second and the third phases than in the first phase, while in the dense group, TBC was significantly higher in the second phase than in the first and third phases. CONCLUSION The optimal delay time to depict breast cancer is 80 s after a contrast media injection, regardless of the density level of the background mammary gland.

CAD system for full-digital mammography and its evaluation

ABSTRACT The purpose of this study is to develop a clinical intelligent workstation for computer-aided diagnosis (CAD) of breastcancer using full digital mammography. It consists of a clinical workstation and Fuji Computed Radiography 9000 System. Newimage processing methods to extract tumor masses and clustered microcalcifications have been developed and implemented inthe CAD system. A new filter called Iris Filter has been developed to detect tumor candidates. It realizes reliable detection oftumor candidates regardless of their sizes and their contrast against their background on mammograms. And a new methodbased on mathematical morphology has been developed to detect microcalcifications. It is adaptive to the imaging conditions ofmammograms. One thousand, two hundred and twelve CR images, which include 240 malignant tumors, were used to test theperformance of the system. The sensitivity for malignant tumors was 90. 5% and the average number of false positives per imagewere only 1.3. The true positive detection rate for clustered microcalcifications was 89.2% and the average number of falsepositives per image were 0.36. The high true positive rates and the low false positive detection characterize the proposed full-digital CAD system, which shows the possibility of practical application of computer aided diagnosis of breast cancer.Keywords: breast cancer, mammography, CAD, malignant tumors, microcalcijIcations, irisfilter. morphological processing

Microcalcification Detection System for Full-Digital Mammography

The development of computer aided diagnosis (CAD) system of breast cancer is urgently required. Several investigators have shown the possibility to use CAD system as a second reader. This paper proposes a new CAD system for microcalcification detection.

Improved automatic adjustment of density and contrast in FCR system using neural network

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.

Computer Aided Detection (CAD) for Digital Mammography: A Retrospective Reading Study for Consideration on Utilizing CAD Most Effectively

A reading test was performed for a digital mammography (MMG)-CAD system and analyzed the type of readers that used CAD most effectively. The database for the reading test consisted of 40 breast cancer cases and 60 cases without breast cancer. 12 readers interpreted mammograms both with and without CAD. We divided the readers into 2 groups. Group A included readers who had either an extremely high or a relatively low ability of interpretation and insufficient understanding of CAD. Group B included the rest of the readers. As the results, in Group A, there was no statistical difference between the results with and without CAD. In Group B, there was a statistical difference between results with and without CAD in sensitivity, but specificity. This result implies that both sufficient reading training of MMG and the sufficient explanation and training of MMG-CAD is important.