1. Introduction Computer-Aided Detection and Diagnosis in Mammography

The American Cancer Society estimates that 215,990 women will be diagnosed with breast cancer in the U.S. in 2004 [1]. Another 40,110 women will die of the disease. In the US, breast cancer is the most commo

Bovik, Facial range image matching using the complex-wavelet structural similarity metric

We propose a novel 3D face recognition algorithm based on facial range image matching using the complex wavelet structural similarity metric (CW-SSIM) metric. Compared with many existing 3D surface matching methods, CW-SSIM is computationally efficient and is robust to small geometrical distortions. Using a data set that contains 360 3D face models of 12 subjects, we tested the performance of the proposed method and compared it with existing 3D surface matching based face recognition algorithms. Verification and identification performance of each algorithm was evaluated by means of the receiver operating characteristic curve and the cumulative match characteristic curve. Among the algorithms tested, the proposed algorithm based on the CW-SSIM resulted in the best overall performance with an equal error rate of 9.13 % and a rank 1 recognition rate of 98.6%, significantly better than all the other algorithms. Besides the introduction of a novel approach for 3D face recognition, this is also the first attempt to expand the application scope of complex wavelet domain similarity measure to range image matching in general. 1

TOROIDAL GAUSSIAN FILTERS FOR DETECTION AND EXTRACTION OF PROPERTIES OF SPICULATED MASSES

We have invented a new class of linear filters for the detection of spiculated masses and architectural distortions in mammography. We call these Spiculation Filters. These filters are narrow band filters and form a new class of wavelettype filter banks. In this paper, we show that unmodulated versions of these filters can be used to detect the central mass region of spiculated masses. We refer to these as toroidal gaussian filters. We also show that the physical properties of spiculated masses can be extracted from the responses of the toroidal gaussian filters without segmentation. 1

MEASURING INTRA- AND INTER-OBSERVER AGREEMENT IN IDENTIFYING AND LOCALIZING STRUCTURES IN MEDICAL IMAGES

Inter- and intra-observer variability exists in any measurements made on medical images. There are two sources of variability. The first occurs when the observers identify and localize the object of interest, and the second happens when the observers make appropriate measurement on the object of interest. A number of statistical methods are available to quantify the degree of agreement between measurements made by different observers. However, little has been done to develop metrics for quantifying the variability in identifying and localizing the objects of interest prior to measurement. In this paper, we propose to use the complex wavelet structural similarity index (CW-SSIM) method to measure the variability in identifying and localizing structures on images. Performance comparisons using simulated images as well as real mammography images demonstrate the effectiveness and robustness of the CW-SSIM method. 1