Lesion segmentation, which is a critical step in computer-aided diagnosis system, is a challenging task as lesion boundaries are usually obscured, irregular, and low contrast. In this paper, an accurate and robust algorithm for the automatic segmentation of breast lesions in mammograms is proposed. The traditional watershed transformation is applied to the smoothed (by the morphological reconstruction) morphological gradient image to obtain the lesion boundary in the belt between the internal and external markers. To automatically determine the internal and external markers, the rough region of the lesion is identified by a template matching and a thresholding method. Then, the internal marker is determined by performing a distance transform and the external marker by morphological dilation. The proposed algorithm is quantitatively compared to the dynamic programming boundary tracing method and the plane fitting and dynamic programming method on a set of 363 lesions (size range, 5–42 mm in diameter; mean, 15 mm), using the area overlap metric (AOM), Hausdorff distance (HD), and average minimum Euclidean distance (AMED). The mean ± SD of the values of AOM, HD, and AMED for our method were respectively 0.72 ± 0.13, 5.69 ± 2.85 mm, and 1.76 ± 1.04 mm, which is a better performance than two other proposed segmentation methods. The results also confirm the potential of the proposed algorithm to allow reliable segmentation and quantification of breast lesion in mammograms.

Rationale and Objectives

Although spiculation level of breast mass boundary is a primary sign of malignancy for the mass detected on mammograms, developing an automated computer scheme to detect mass spiculation level and quantitatively evaluating the performance of the scheme is a difficult task. The objective of this study is to (1) develop and test a new scheme to improve mass segmentation and detect mass boundary spiculation level, and (2) assess the scheme performance using a relatively large image dataset.

Materials and Methods

This fully-automated scheme includes three image processing steps. The first step applies the maximum entropy principle in the selected region of interest (ROI) after correcting the background-trend to enhance the initial outlines of the masses. The second step uses an active contour model to refine the initial outlines. The third step detects and identifies spiculated lines connected to the mass boundary using a special line detector. A quantitative spiculation index is computed to assess the degree of spiculation levels. To develop and evaluate this automated scheme, we selected 211 ROIs depicting masses that were extracted from a publicly available image database. Among these ROIs, 106 depict “circumscribed” mass regions and 105 involve “spiculated” mass regions. The scheme performance was evaluated using the receiver operating characteristic (ROC) analysis method.

Results

The computed area under ROC curve when applying the scheme to the dataset is 0.701 ± 0.027. By setting up a threshold at spiculation index = 5.0, the scheme achieves the overall classification accuracy of 66.4% with 54.3% sensitivity and 78.3% specificity, respectively.

Conclusions

We developed a new computer scheme with a number of unique characteristics to detect spiculated mass regions and applied a simple spiculation index to quantify mass spiculation levels. Although this quantitative index can be used to classify between the spiculated and circumscribed masses, the results also suggest that automated detection of mass spiculation levels remains a technical challenge.

The authors studied current levels of computerization in dental clinics and the attitudes of dentists towards dental computerization in metropolises in China. A survey consisting of 22 questions was e-mailed or mailed to a random sample of 354 dentists. Of all respondents, 80.5% reported using a computer in their practice. The authors found that administrative tasks were the first to be computerized. A majority of respondents supported the statement that computerization is a benefit to patient care. The authors found that the computerization of dental clinics in Chinese metropolises is a few years behind that of western nations.

Despite the clinical significance of event-related potential (ERP) latency abnormalities, little attention has focused on the anatomic substrate of latency variability. Volume conduction models do not identify the anatomy responsible for delayed neural transmission between neural sources. To explore the anatomic substrate of ERP latency variability in normal adults using automated measures derived from magnetic resonance imaging (MRI), ERPs were recorded in the visual three-stimulus oddball task in 59 healthy participants. Latencies of the P3a and P3b components were measured at the vertex. Measures of local anatomic size in the brain were estimated from structural MRI, using tissue segmentation and deformation morphometry. A general linear model was fitted relating latency to measures of local anatomic size, covarying for intracranial vault volume. Longer P3b latencies were related to contractions in thalamus extending superiorly into the corpus callosum, white matter (WM) anterior to the central sulcus on the left and right, left temporal WM, the right anterior limb of the internal capsule extending into the lenticular nucleus, and larger cerebrospinal fluid volumes. There was no evidence for a relationship between gray matter (GM) volumes and P3b latency. Longer P3a latencies were related to contractions in left temporal WM, and left parietal GM and WM near the interhemispheric fissure. P3b latency variability is related chiefly to WM, thalamus, and lenticular nucleus, whereas P3a latency variability is not related as strongly to anatomy. These results imply that the WM connectivity between generators influences P3b latency more than the generators themselves do.

The Committee of Human Research of the University of California San Francisco approved this study, and all volunteers provided written informed consent. The goal of this study was to prospectively determine the global and regional reliability and reproducibility of noninvasive brain perfusion measurements obtained with different pulsed arterial spin-labeling (ASL) magnetic resonance (MR) imaging methods and to determine the extent to which within-subject variability and random noise limit reliability and reproducibility. Thirteen healthy volunteers were examined twice within 2 hours. The pulsed ASL methods compared in this study differ mainly with regard to magnetization transfer and eddy current effects. There were two main results: (a) Pulsed ASL MR imaging consistently had high measurement reliability (intraclass correlation coefficients greater than 0.75) and reproducibility (coefficients of variation less than 8.5%), and (b) random noise rather than within-subject variability limited reliability and reproducibility. It was concluded that low signal-to-noise ratios substantially limit the reliability and reproducibility of perfusion measurements.