In this study, a novel fusion framework has been developed to combine the detection of both breast masses and
microcalcifications (MCs), aiming to improve positive predictive value (PPV) in Computer-aided Diagnosis (CADx).
Clinically, it has been widely accepted that a mass associated with MC is a useful indicator of predicting the malignancy of the mass. In light of this fact, given that a mass and MCs are co-located each other (i.e., they are at the same location), the proposed fusion framework combines confidence scores of the mass and MCs for the purpose of improving the probability that the mass is malignant. To this end, the popular Bayesian network model is applied to effectively combine the detection confidence scores and to achieve higher accuracy for malignant mass classification. To demonstrate the effectiveness of the proposed fusion framework, 31 mammograms were collected from the public DDSM database. The proposed fusion framework can increase the area under the receiver operating characteristic curve (AUC) from 0.7939 to 0.8806, and the partial area index (PAUC) above the sensitivity of 0.9 from 0.1270 to 0.2280, compared to the CADx system without exploiting co-location information with MCs. Based on these results, it can be expected that the proposed fusion framework can be readily applied for realizing CADx systems with the higher PPV.
In breast cancer screening practice, radiologists compare multiple views during the interpretation of mammograms to detect breast cancers. Hence, it is natural that information derived from multiple mammograms can be used for computer-aided detection (CAD) system to obtain better sensitivity and/or specificity. However, similarity features derived from the combination of cranio-caudal (CC) and mediolateral oblique (MLO) views are weak for classifying masses, because a breast is elastic and deformable. In this study, therefore, a new mass classification with boosting algorithm is proposed, aiming to reduce FPs by combining the information of CC and MLO view mammograms. The proposed method has been developed under the following facts: (1) classifiers trained using similarity features are rather weak classifier; (2) boosting technique generates a single strong classifier by combining multiple weak classifiers. By combining the classifier ensemble framework with similarity features, we are able to improve mass classification performance in two-view analysis. In this study, 192 mammogram cases were collected from the public DDSM database (DB) to demonstrate the effectiveness of the proposed method in terms of improving mass classification. Results show that our proposed classifier ensemble method can improve an area under the ROC curve (AUC) of 0.7479, compared to the best single support vector machine (SVM) classifier using feature-level fusion (AUC of 0.7123). In addition, the weakness of similarity features is experimentally found to prove the feasibility of the proposed method.
We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive
(FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP
features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize
the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the
masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels
of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called
multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core
region and its margin has clearly proven to be significant and is not sensitive to the precise location of the
boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To
perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total
of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine
(SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating
Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using
multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the
ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts
features designed for false positive reduction.
In this study, a novel mammogram enhancement solution is proposed, aiming to improve the quality of subsequent
mass segmentation in mammograms. It has been widely accepted that characteristics of masses are usually hyper-dense
or uniform density with respect to its background. Also, their core parts are likely to have high-intensity values while the
values of intensity tend to be decreased as the distance to core parts increases. Based on the aforementioned
observations, we develop a new and effective mammogram enhancement method by combining local statistical
measurements and Sliding Band Filtering (SBF). By effectively combining local statistical measurements and SBF, we
are able to improve the contrast of the bright and smooth regions (which represent potential mass regions), as well as, at
the same time, the regions where their surrounding gradients are converging to the centers of regions of interest. In this
study, 89 mammograms were collected from the public MAIS database (DB) to demonstrate the effectiveness of the
proposed enhancement solution in terms of improving mass segmentation. As for a segmentation method, widely used
contour-based segmentation approach was employed. The contour-based method in conjunction with the proposed
enhancement solution achieved overall detection accuracy of 92.4% with a total of 85 correct cases. On the other hand,
without using our enhancement solution, overall detection accuracy of the contour-based method was only 78.3%. In
addition, experimental results demonstrated the feasibility of our enhancement solution for the purpose of improving
detection accuracy on mammograms containing dense parenchymal patterns.
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