Incorporation of an iterative, linear segmentation routine into a mammographic mass CAD system

Abstract

In previous research, we have developed a computer-aided detection (CAD) system designed to detect masses in mammograms. The previous version of our system employed a simple but imprecise method to localize the masses. In this research, we present a more robust segmentation routine for use with mammographic masses. Our hypothesis is that by more accurately describing the morphology of the masses, we can improve the CAD system's ability to distinguish masses from other mammographic structures. To test this hypothesis, we incorporated the new segmentation routine into our CAD system and examined the change in performance. The developed iterative, linear segmentation routine is a gray level-based procedure. Using the identified regions from the previous CAD system as the initial seeds, the new segmentation algorithm refines the suspicious mass borders by making estimates of the interior and exterior pixels. These estimates are then passed to a linear discriminant, which determines the optimal threshold between the interior and exterior pixels. After applying the threshold and identifying the object's outline, two constraints on the border are applied to reduce the influence of background noise. After the border is constrained, the process repeats until a stopping criterion is reached. The segmentation routine was tested on a study database of 183 mammographic images extracted from the Digital Database for Screening Mammography. Eighty-three of the images contained 50 malignant and 50 benign masses; 100 images contained no masses. The previously developed CAD system was used to locate a set of suspicious regions of interest (ROIs) within the images. To assess the performance of the segmentation algorithm, a set of 20 features was measured from the suspicious regions before and after the application of the developed segmentation routine. Receiver operating characteristic (ROC) analysis was employed on the ROIs to examine the discriminatory capabilities of each individual feature before and after the segmentation routine. A statistically significant performance increase was found in many of the individual features, particularly those describing the mass borders. To examine how the incorporation of the segmentation routine affected the performance of the overall CAD system, free-response ROC (FROC) analysis was employed. When considering only malignant masses, the FROC performance of the system with the segmentation routine appeared better than the previous system. When detecting 90% of the malignant masses, the previous system achieved 4.9 false positives per image (FPpI) compared to the post-segmentation system's 4.2 FPpI. At 80% sensitivity, the respective FPpI were 3.5 and 1.6.

A New Preprocessing Filter for Digital Mammograms

“…To investigate the effect of the FCLAHE over the accuracy of the subsequent segmentation algorithm, we performed the Catarious segmentation method (CSM) to delineate the masses [14]. We performed CSM segmentation method on both the original images and the enhanced images using either CLAHE or FCLAHE.…”

Tri-plane correlation imaging for the detection of breast cancer: Effects of angular separation and correlation rule

“…The filtered image was first thresholded at the 99th percentile. The threshold continued to be lowered to each lower percentile to grow regions until the entire region was segmented or a region attempted to merge with another suspicious region [16] . A minimal rule-based false-positive approach was used to eliminate suspicious regions based on area and eccentricity.…”

Reliable Evaluation of Performance Level for Computer-Aided Diagnostic Scheme

“…Improper use of these methods can lead to significant biases in the estimated performance levels. We reviewed all research articles on the development and evaluation of CAD schemes that were published in Medical Physics [15][16][17][18][19][20][21] and IEEE Transactions on Medical Imaging [22][23][24] in 2004. We found that, among the 10 papers we reviewed, nine employed incorrect evaluation methods leading to increased bias and/or variance in the estimated performance levels [9].…”