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.

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 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.

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.