Microwave mammography is a promising alternative to X-ray based imaging modalities, because of its small size, low cost, and cell-friendly exposure. More importantly, this modality enables the suppression of surface reflection clutter, which can be enhanced by introducing accurate surface shape estimations. However, near-field measurements can reduce the shape estimation accuracy, due to a mismatch between the reference and observed waveforms. To mitigate this problem, this study incorporates envelope-based shape estimation and finite-difference time-domain (FDTD)-based waveform correction with a fractional derivative adjustment. Numerical simulations based on realistic breast phantoms derived from magnetic resonance imaging (MRI) show that the proposed method significantly enhances the accuracy of breast surface imaging and the performance of surface clutter rejection.

Automatic detection of normal mammograms, as a "first look" for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable "crossed-distributions" in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity.

In this paper, an automated computer-aided-detection scheme is proposed to identify and locate the suspicious masses in the abnormal breasts from the full mammograms. Mammograms are examined using a four-stage detection method including pre-processing, identification of local maxima, seeded region-growing, and false positive (FP) reduction. This method has been applied to the entire Mammographic Image Analysis Society (MIAS) database of 322 digitized mammograms containing 59 biopsy-proven masses in 56 images. Results of detection show 95% true positive (TP) fraction at 1.9 FPs per image for the 56 images and 1.3 FPs per image for the entire database.

Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection algorithm using adaptive contrast enhancement in a mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection algorithm includes two parts. One is adaptive contrast enhancement in which the enhancement filtering parameters are determined based on noise characteristics of the mammogram. The other is a multi-stage microcalcification detection. The results show that the proposed microcalcification detection algorithm is much more robust against fluctuating noisy environments.

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.

The massively multiplayer online game (MMOG) industry is suffering from huge outgoing traffic from centralized servers. To accommodate this traffic, game companies claim large bandwidth to Internet Data Centers (IDCs), and several months' payment for that bandwidth is likely to even exceed the cost for MMOG servers. In this paper, we propose a MMOG server architecture to reduce outgoing bandwidth consumption from MMOG servers. The proposed architecture distributes some functions of servers to selected clients, and those clients are in charge of event notification to other clients in order to reduce the outgoing traffic from servers. The clients with server functions communicate with each other in peer-to-peer manner. We analyze traffic reduction as a function of cell-daemonable ratio of clients, and the results show that up to 80% of outgoing traffic from servers can be reduced using the proposed architecture when 10% of clients are cell-daemonable.

The use of the homomorphic filter technique is described in order to enhance the contrast in the mammographic images, which is adopted to the dyadic wavelet transform. The proposed method has employed the nonlinear enhancement in homomorphic filtering as well as denoising method in the wavelet domains. Experimental results show that the homomorphic filtering method improves the contrast in breast tumor images such that the contrast improvement index is increased by two fold compared to the conventional wavelet-based enhancement technique.

This paper presents a method for detection of calcification, which is an important early sign of breast cancer in mammograms. Since information of calcifications is located in inhomogeneous background and noises, it is hard to be detected. This method uses wavelet packet transform (WPT) for elimination of the background image related to low frequency components. However, very high frequency signals of noises exist with the calcifications and make it hard to suppress them. Since calcification location can be represented as vertical, horizontal, and diagonal edges in time-frequency domain, the edges in spatial domain can be utilized as a filter for noise suppression. Then the image from inverse transform will contain only required information. A free-response operating characteristic (FROC) curve is used to evaluate a performance of proposed method by applying it to thirty images of calcifications. The results show 82.19 percent true positive detection rate at the cost of 6.73 false positive per image.

An Off-line mammography screening system is used in pre-screening mammograms to separate high-risk mammograms from most normal cases. Off-line system can run before radiologist's review and is particularly useful in the national breast cancer screening program which usually consists of high percentage of normal cases. Until now, the shortcomings of on-line detection of clustered microcalcifications from a mammogram remain in the necessity of manual selection of regions of interest. The developed technique focuses on detection of microcalcifications within a region of interest indicated by the radiologist. Therefore, this kind of system is not efficient enough to process hundreds of mammograms in a short time without a large number of radiologists. In this paper, based on a "hierarchically-coarse-to-fine" approach, an off-line mammography screening system for the detection and segmentation of clustered microcalcifications is presented. A serial off-line procedures without any human intervention should consider the complexity of organization of mammograms. In practice, it is impossible to use one technique to obtain clustered microcalcifications without consideration of background text and noises from image acquisition, the position of breast area and regions of interest. "Hierarchically-coarse-to-fine" approach is a serial procedures without any manual operations to reduce the potential areas of clustered microcalcifications from a mammogram until clustered microcalcifications are found. The reduction of potential areas starts with a mammogram, through identification of the breast area, identification of the suspicious areas of clustered microcalcifications, and finally segmentation of clustered microcalcifications. It is achieved hierarchically from coarse level to fine level. In detail, the proposed system includes breast area separation, enhancement, detection and localization of suspicious areas, segmentation of microcalcifications, and target selection of microcalcifications. The system separates its functions into hierarchical steps and follows the rule of thumb "coarse detection followed by fine segmentation" in performing each step of processing. The decomposed hierarchical steps are as follows: The system first extracts the breast region from which suspicious areas are detected. Then precise clustered microcalcification regions are segmented from the suspicious areas. For each step of operation, techniques for rough detection are first applied followed by a fine segmentation to accurately detect the boundaries of the target regions. With this "hierarchically-coarse-to-fine" approach, a complicated work such as the detection of clustered microcalcifications can be divided and conquered. The effectiveness of the system is evaluated by three experienced radiologists using two mammogram databases from the Nijmegen University Hospital and the Taichung Veterans General Hospital. Results indicate that the system can precisely extract the clustered microcalcifications without human intervention, and its performance is competitive with that of experienced radiologists, showing the system as a promising asset to radiologists.

We applied an artificial neural network approach identify possible tumors into benign and malignant ones in mammograms. A sequential-dependence technique, which calculates the degree of redundancy or patterning in a sequence, was employed to extract image features from mammographic images. The extracted vectors were then used as input to the network. Our preliminary results show that the neural network can correctly classify benign and malignant tumors at an average rate of 85%. This accuracy rate indicates that the neural network approach with the proposed feature-extraction technique has potential utility in the computer-aided diagnosis of breast cancer.