Breast image pre-processing for mammographic tissue segmentation

Abstract

During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

Fibroglandular Tissue Quantification in Mammography by Optimized Fuzzy C-Means with Variable Compactness

“…A Mo/Mo anode/filter combination was used for all X-ray exposures. Processed images were used, since raw data is often not achieved in clinical practice [16].…”

“…However, most medical centers achieve only the post-processed images for clinical purposes. Therefore, development of methodology to estimate BD in postprocessed mammogram would be beneficial both in terms of direct clinical application and retrospective research-related studies [15,16].…”

Content-Based Image Retrieval Techniques for Mammographic Images Using Soft Computing Techniques

“…An image selection process should be incorporated in to better target problematic images [12]. In some circumstances, various enhancement algorithms can be combined together to achieve better results for the segmentation of the breast region and visual interpretation, analysis, and classification of mammogram masses to assist the radiologist in making more accurate decisions [13].…”

Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods

“…Mammography systems for the computer-aided detection (CAD) of cancer masses perform the following steps: preprocessing [1][2][3], segmentation [4][5][6][7], feature extraction [8][9][10] and classification [11][12][13]. However, whether a CAD system will be successfully adopted in clinical practice depends mainly on the segmentation algorithm or algorithms used.…”