Murk J. Bottema

Two graph theory based methods for identifying the pectoral muscle in mammograms

Two image segmentation methods based on graph theory are used in conjunction with active contours to segment the pectoral muscle in screening mammograms. One method is based on adaptive pyramids (AP) and the other is based on minimum spanning trees (MST). The algorithms are tested on a public data set of mammograms and results are compared with previously reported methods. In 80% of the images, the boundary of the segmented regions has average error less than 2mm. In 82 of 84 images, the boundary of the pectoral muscle found by the AP algorithm has average error less than 5mm.

New electron-energy transfer rates for vibrational excitation of O2

We report on our computation of electron-energy transfer rates for vibrational excitation of O2. This work was necessitated by inadequacies in the electron-impact cross section databases employed in previous studies and, in one case, an inaccurate approximate formulation to the rate equation. Both these inadequacies led to incorrect energy transfer rates being published in the literature. We also demonstrate the importance of using cross sections that encompass an energy range that is extended enough to appropriately describe the environment under investigation.

Circularity of objects in images

The most commonly used measure of circularity of objects in images is shown to give incorrect results. An alternative measure of circularity based on the distance between a set and a discrete disk is described. The alternative measure gives circularity zero (distance zero) for discrete disks and values in the range (0,1) for discrete sets which are not disks.

Influence of Orthogonal Overload on Human Vertebral Trabecular Bone Mechanical Properties

The aim of this study was to investigate the effects of overload in orthogonal directions on longitudinal and transverse mechanical integrity in human vertebral trabecular bone. Results suggest that the trabecular structure has properties that act to minimize the decrease of apparent toughness transverse to the primary loading direction.

Minimum Spanning Trees and Active Contours for Identification of the Pectoral Muscle in Screening Mammograms

Image segmentation based on minimum spanning trees (MST) is used to identify the pectoral muscle in screening mammograms. The segmentation found using the MST is used to initialise an active contour for finding an anatomically reasonable estimate of the boundary of the pectoral muscle. The error is reported in terms of the number of in-correctly assigned pixels. Out of 83 images, 25 images have error rates less than 5 percent and 56 images have error rates less than 10 percent. The nature of the errors encountered indicates that the accuracy of computer algorithms for this task is approaching its practical limit.

Automatic Mass Segmentation Based on Adaptive Pyramid and Sublevel Set Analysis

A method based on sublevel sets is presented for refining segmentation of screening mammograms. Initial segmentation is provided by an adaptive pyramid (AP) scheme which is viewed as seeding of the final segmentation by sublevel sets. Performance is tested with and without prior anisotropic smoothing and is compared to refinement based on component merging. The combination of anisotropic smoothing, AP segmentation and sublevel refinement is found to outperform other combinations.

Background intensity independent texture features for assessing breast cancer risk in screening mammograms

Image intensity and texture in screening mammograms are thought to be associated with the risk of breast cancer. Studies on developing automatic breast cancer risk assessment schemes tend to employ texture measures which are correlated to local background intensity. Accordingly, the contribution of texture alone to risk assessment is not known. Here background intensity independent texture measures are used to assess cancer risk. Moreover risk assessment based on background intensity independent texture outperforms intensity dependent texture suggesting that local image background intensity may confound risk assessment. Performance seems to depend on the view of the breast and so suggests that optimizing schemes for different views may improve risk assessment.

Detection and classification of lobular and DCIS (small cell) microcalcifications in digital mammograms

Abstract Microcalcifications are detected by fitting a model to every location in the mammogram. Model parameters yielding the best fit are used as features for detection and classification. The fraction of true positive (tp) detection is 60% with 1.23 false detections per cm 2 . The rate of correct classification is 69%.

Texture and region dependent breast cancer risk assessment from screening mammograms

Breast density is a known risk factor for breast cancer. Here two classes of texture features, one based on textons derived from local pixel intensity variation and one based on oriented tissue structure characteristics are measured on different regions of the breast in an effort to clarify the potential contribution of texture independent of local tissue density to estimate breast cancer risk. The region just behind the nipple is found to be the most significant local region for estimating risk, but estimates based on the entire breast perform better. Texton features are found to perform better than features based on oriented tissue structure.

Mammographic Mass Detection with Statistical Region Merging

An automatic method for detection of mammographic masses is presented which utilizes statistical region merging for segmentation (SRM) and linear discriminant analysis (LDA) for classification. The performance of the scheme was evaluated on 36 images selected from the local database of mammograms and on 48 images taken from the Digital Database for Screening Mammography (DDSM). The Az value (area under the ROC curve) for classifying each region was 0.90 for the local dataset and 0.96 for the images from DDSM. Results indicate that SRM segmentation can form part of an robust and efficient basis for analysis of mammograms.