Proc. SPIE. 9034, Medical Imaging 2014: Image Processing
KEYWORDS: Breast, Computer aided diagnosis and therapy, Breast cancer, Detection and tracking algorithms, Digital filtering, 3D modeling, Mammography, Reconstruction algorithms, 3D image processing, Digital breast tomosynthesis
Digital breast tomosynthesis (DBT) is a new modality that has strong potential in improving the sensitivity and specificity of breast mass detection. However, the detection of microcalcifications (MCs) in DBT is challenging because radiologists have to search for the often subtle signals in many slices. We are developing a computer-aided detection (CAD) system to assist radiologists in reading DBT. The system consists of four major steps, namely: image enhancement; pre-screening of MC candidates; false-positive (FP) reduction, and detection of MC cluster candidates of clinical interest. We propose an algorithm for reducing FPs by using 3D characteristics of MC clusters in DBT. The proposed method takes the MC candidates from the pre-screening step described in  as input, which are then iteratively clustered to provide training samples to a random-forest classifier and a rule-based classifier. The random forest classifier is used to learn a discriminative model of MC clusters using 3D texture features, whereas the rule-based classifier revisits the initial training samples and enhances them by combining median filtering and graph-cut-based segmentation followed by thresholding on the final number of MCs belonging to the candidate cluster. The outputs of these two classifiers are combined according to the prediction confidence of the random-forest classifier. We evaluate the proposed FP-reduction algorithm on a data set of two-view DBT from 40 breasts with biopsy-proven MC clusters. The experimental results demonstrate a significant reduction in FP detections, with a final sensitivity of 92.2% for an FP rate of 50%.
One of the main challenges of high level analysis of human behavior is the high dimension of the feature space.
To overcome the curse of dimensionality, we propose in this paper, a space curve representation of the high
dimensional behavior features. The features of interest here, are restricted to sequences of shapes of the human
body such as those extracted from a video sequence. This evolution is a one dimensional sub-manifold in shape
space. The central idea of the proposed representation takes root in the Whitney embedding theorem which
guarantees an embedding of a one dimensional manifold in as a space curve. The resulting of such dimension
reduction, is a simplification of comparing two behaviors to that of comparing two curves in R3. This comparison
is additionally theoretically and numerically easier to implement for statistical analysis. By exploiting sampling
theory, we are moreover able to achieve a computationally efficient embedding that is invertible. Specifically,
we first construct a global coordinates expression for the one dimension manifold and sampled along a generating
curve.As experiment result, we provide substantiating modeling examples and illustrations of behavior
In order to improve the quality of image with super-resolution reconstruction, a method based on motion estimation error and edge constraint was proposed. Under the condition of data consistency and amplitude restriction, the motion estimation error was analyzed, with its variance being calculated; meanwhile, in order to suppress the ringing artifacts, edge constraint was adopted and a method based clustering for judging the edge's direction was proposed. The experimental results show that the performance of the this algorithm is better than the traditional linear interpolation and method without considering motion estimation error both in vision effect and peak signal to noise ratio.
The existing methods for texture modeling include co-occurrence statistics, filter banks and random fields. However most of these methods lack of capability to characterize the different scale of texture effectively. In this paper, we propose a texture representation which combines local scale feature, amplitude and phase of wavelet modules in multi-scales. The self-similarity of texture is not globally uniform and could be measured in both correlations across the multi-scale and statistical feature within a single-scale. In our approach, the local scale feature is represented by optimal scale obtained through the evolution of wavelet modulus across multi-scales. Then, for all the blocks of the same optimal scale, the statistical measurement of amplitude is extracted to represent the energy within the corresponding frequency
band; the statistical measurement of the phase of modulus is extracted to represent the texture's orientation. Our experiment indicates that, in the proposed texture representation the separability of different texture patterns is larger than the one of the traditional features.
Many advance image processing, like segmentation and recognition, are based on contour extraction which usually lack of ability to allocate edge precisely in the image of heavy noise with low computation burden. For such problem, in this paper, we proposed a new approach of edge detection based on pyramid-structure wavelet transform. In order to suppress noise and keep good continuity of edge, the proposed edge representation considered both inter-correlations across the multi-scales and intra-correlations within the single-scale. The former one is described by point-wise singularity. The later one is described by the magnitude and ratio of wavelet coefficients in different sub-bands. Based on such edge modeling, the edge point allocation is then complemented in wavelet domain by synthesizing the edge information in multi-scales. The experimental results shows that our approaches achieve the pixel-level edge detection with strong resistant against noise due to scattering in water.
Self-similarity features of natural surface play a key role in region segmentation and recognition. Due to long period of
natural evolution, real terrain surface is composed of many
self-similar structures. Consequently, the Self-similarity is
not always so perfect that remains invariable in whole scale space and the traditional single self-similarity parameter can
not represent such abundant self-similarity. In this view, the
self-similarity is not a constant parameter over all scales, but
multi-scale parameters. In order to describe such multi-scale
self-similarities of real surface, firstly we adopt the
Fractional Brownian Motion (FBM) model to estimate the
self-similarity curve of terrain surface. Then the curve is
divided into several linear regions to represent relevant
self-similarities. Based on such regions, we introduce a parameter
called Self-similar Degree (SSD) in the similitude of information entropy. Moreover, the small value of SSD indicates the
more consistent self-similarity. We adopt fifty samples of terrain images and evaluate SSD that represents the multi-scale
self-similarity features for each sample. The samples are clustered by unsupervised fuzzy c mean clustering into various
classes according to SSD and traditional monotone Hurst feature respectively. The measurement for separability of
features shows that the new parameter SSD is an effective feature for terrain classification. Therefore the similarity
feature set that is made up of the monotone Hurst parameter and SSD provides more information than traditional
monotone feature. Consequently, the performance of terrain classification is improved.
Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. But in the traditional methods of watermarking images, the information of original image will be distorted more or less. Facing this problem, a new watermarking approach, zero-watermarking technique, is proposed. The zero-watermarking approach changes the traditional doings that watermarking is embedded into images, and makes the watermarked image distortion-free. Zero-watermarking can successfully solve the conflict between invisibility and robustness. In this paper, a digital image zero-watermarking method based on discrete wavelet transform and chaotic modulation is proposed.
The zero-watermarking algorithm based on DWT and chaos modulation consists of watermark embedding and detecting processes.
The watermark embedding process is as follow:
First, the original image is decomposed to three-level in wavelet domain. Second, some low frequency wavelet coefficients of original image are selected. The selection of the wavelet coefficients is random by chaotic modulation. Third, the character of coefficients selected is used to construct the character watermark. For each coefficient, in comparison with the adjacent coefficient, we can get the character watermark.
The watermark extracting process is invert process. The location of the coefficients being extracted is also determined by chaotic sequence.
The experimental results show that the watermarking method is invisible and robust against some image processing such as median filtering, JPEG compression, additive Gaussian noise, cropping and rotation attacks and so on. If the initial value of chaos is unknown, the character watermarking can't be extracted correctly.