Screening for breast cancer

Automatic extraction of pectoral muscle in the MLO view of mammograms

A mammogram is the standard modality used for breast cancer screening. Computer-aided detection (CAD) approaches are helpful for improving breast cancer detection rates when applied to mammograms. However, automated analysis of a mammogram often leads to inaccurate results in the presence of the pectoral muscle. Therefore, it is necessary to first handle pectoral muscle segmentation separately before any further analysis of a mammogram. One difficulty to overcome when segmenting out pectoral muscle is its strong overlapping with dense glandular tissue which tampers with its extraction. This paper introduces an automated two-step approach for pectoral muscle extraction. The pectoral region is firstly estimated through segmentation by mean of a modified Fuzzy C-Means clustering algorithm. After contour validation, the final boundary is delineated through iterative refinement of edge point using average gradient. The proposed method is quite simple in implementation and yields accurate results. It was tested on a set of images from the MIAS database and yielded results which, compared to those of some state-of-the-art approaches, were better.

Marker-controlled watershed for lesion segmentation in mammograms

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.

Clinical, demographic, and situational factors linked to distress associated with benign breast biopsy

Benign breast biopsy (BBB) can be distressing for many women. Few studies have examined specific aspects of the BBB more or less distressing or risk factors for distress. Women (N = 51) who had a recent BBB reported the magnitude of distress associated with specific aspects of their experience. Clinical and demographic variables were also examined as risk factors for distress. All women reported some distress associated with the BBB with one third reporting their experience was "very stressful." Generally, biopsy-specific events were more distressing than follow-up mammography. Distress risk factors included younger age, less education, nonsurgical biopsy, and no family history of breast cancer. Clinical efforts to better manage biopsy-related distress are warranted. The authors identified clinical and demographic risk factors that furnish a simple, efficient, and potentially cost-effective means of stratifying risk for distress in the breast biopsy setting.

Computer-aided diagnostic models in breast cancer screening

Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.