An important difference between projection images such as x-rays and natural images is that the intensity at a
single pixel in a projection image comprises information from all objects between the source and detector. In order
to exploit this information, a Dirichlet mixture of Gaussian distributions is used to model the intensity function
forming the projection image. The model requires initial seeding of Gaussians and uses the EM (estimation
maximisation) algorithm to arrive at a final model. The resulting models are shown to be robust with respect to
the number and positions of the Gaussians used to seed the algorithm. As an example, a screening mammogram
is modelled as the Dirichlet sum of Gaussians suggesting possible application to early detection of breast cancer.
This paper presents a method for associating regions of sequential
mammograms automatically using graph matching. The graph matching
utilises relative spatial relationships between the regions of a
mammogram to establish regional correspondences between two
mammograms. As a first step of the method, the mammogram is
segmented into separate regions using an adaptive pyramid
segmentation algorithm. This process produces both segmented
regions of the mammogram and a graph. The nodes of the graph
represent the segmented regions, and the lines represent the
relationships between the regions. The regions are then filtered
to remove undesired regions. To express the spatial relations
between the regions, we use a fuzzy logic expression, which takes
into account the characteristics of each region including the
shape, size and orientation. The spatial relations between regions
are utilised as weights of the graph. The backtrack algorithm is
then used to find the common subgraph between two graphs. The
proposed method is applied to 95 temporal pairs of mammograms. For
each temporal mammogram pair, an average of 13.2 regions are
matched. All region matches are classified as "good", "average",
"poor" and "unknown" by one of the authors (FM) based on visual
perception. 63.5% of region matches are identified as "good",
and 23.6% as "average". The percentages of "poor" and
"unknown" are 10.9% and 2% respectively. These results
indicate that our registration method may be useful for
establishing regional correspondence between sequential
mammograms.
A co-occurrence matrix is a joint probability distribution of the pixel values of two pixels in an image separated by a distance d in the direction θ. It is one of the texture analysis tools favored by the medical image processing community. The size of a co-occurrence matrix depends on gray levels re-quantization Q. Hence, when dealing with high depth
resolution images, gray levels re-quantization is routinely performed to reduce the size of the co-occurrence matrix. The gray levels re-quantization may play a role in the display of spatial relationships in co-occurrence matrix but is usually dealt with lightly. In this paper, we use an example to study the effect of gray-level re-quantization in high depth resolution medical images. Digitized film-screen mammograms have a typical depth resolution of 4096 gray levels. In a study classifying masses on mammograms as benign or malignant, 260 texture features are measured on 43 regions-of-interest (ROIs) containing malignant masses and 28 ROIs containing benign masses. Of the 260 texture features,
240 are texture features measured on co-occurrence matrices with parameters θ = 0, π/2; d = 11, 15, 21, 25, 31; and Q = 50, 100, 400. A genetic algorithm is used to select a subset of features (out of 260) that has discriminative power. Results show that top performing feature combinations selected by the genetic algorithm are not restricted to a single value of Q. This indicates that instead of searching for a correct Q, it may be more appropriate to explore a range of
Q values.
KEYWORDS: Mammography, Computer aided diagnosis and therapy, Cancer, Digital mammography, Digital imaging, Image filtering, Breast, Breast cancer, Wavelets, Image analysis
In the past ten years, there has been a push to improve early detection of breast cancer by providing radiologists with computer assistance in assessing screening mammograms. A large variety of modern image analysis techniques have been proposed for automatically detecting and classifying anomalies in mammograms. Although much of the work has not been focused on the critical issues and there have been problems in comparing the performance of the various proposed techniques, substantial progress has been made. The field is now at the critical point of emerging from a state where the goal was to prove feasibility to a stage where the full potential of computer assistance can be realized. The three ingredients driving this transition are (1) recent studies which firmly establish a positive effect of computer assistance on assessing mammograms, (2) winning US FDA approval of the first commercial product for providing such assistance, and (3) the advent of direct digital image acquisition for screening mammography.
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