In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image enhancement techniques to each suspicious mass (region of interest (ROI)) to obtain several enhanced images, then applied segmentation to each image to obtain several contours of a given mass. Each segmentation in this ensemble is essentially a “weak segmentor” because no single segmentation can produce the optimal result for all images. Then after shape features are computed from the segmented contours, the final classification model was built using logistic regression. The work in this paper focuses on identifying the optimal segmentor(s) from an ensemble mix of weak segmentors. For our purpose, optimal segmentors are those in the ensemble mix which contribute the most to the overall classification rather than the ones that produced high precision segmentation. To measure the segmentors' contribution, we examined weights on the features in the derived logistic regression model and computed the average feature weight for each segmentor. The result showed that, while in general the segmentors with higher segmentation success rates had higher feature weights, some segmentors with lower segmentation rates had high classification feature weights as well.
This paper proposes to build multiple segmentations for identifying mass contours for a suspicious mass in a
mammogram. In this study, by using various parameter settings of the image enhancement functions, we
perform multiple segmentations for each suspicious mass (region of interest (ROI)), and multiple mass
contours are generated. Each of such segmentations is called a "weak segmentor", since there is no single
image enhancement which produces the optimal segmentation for all mass images. Then for each image, we
select the contour which has the highest overlapping ratio as the final segmentation (i.e., the "strong
segmentor"). The results show that the overall success rate (81.22%) of the strong segmentor was higher than
that of any single weak segmentor. This indicates that using multiple weak segmentors is an effective method
to generate a strong mass segmentation for mammograms.
This paper presents a novel, edge-based segmentation method for identifying the mass contour (boundary) for a
suspicious mass region (Region of Interest (ROI)) in a mammogram. The method first applies a contrast stretching
function to adjust the image contrast, then uses a filtering function to reduce image noise. Next, for each pixel in a ROI,
the energy descriptor (one of the Haralick descriptors) is computed from the co-occurrence matrix of the pixel; and the
energy texture image of a ROI is obtained. From the energy texture image, the edges in the image are detected; and the
mass region is identified from the closed-path edges. Finally, the boundary of the identified mass region is used as the
contour of the segmented mass. We applied our method to ROI-marked mammogram images from the Digital Database
for Screening Mammography (DDSM). Preliminary results show that the contours detected by our method outline the
shape and boundary of a mass much more closely than the ROI markings made by radiologists.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.