KEYWORDS: Photovoltaics, Particles, Digital breast tomosynthesis, Breast, Signal to noise ratio, Feature extraction, Gaussian filters, Computer aided diagnosis and therapy, Tissues, Mammography
This paper presents a novel method for enhancing the contrast of microcalcifications in digital breast tomosynthesis projection views for detection purposes. The proposed method relies on the correlation between the projection views in order to reduce the effect of noise, due to the low-dose exposure, and increase the contrast of the microcalcification particles for microcalcification cluster detection purposes. The method performs a series of multi-shift operations to capture the microcalcification particle movement information and compensate it in order to enhance microcalcification particles contrast. Furthermore, the proposed approach utilizes the projection view correlation in order to reduce the falsely detected regions of interest, and improve the classification of the detected regions into false positives or actual microcalcification clusters. Comparative experiments have been performed to quantitatively measure the contrast enhancement of microcalcification particles and its effect on the MC cluster detection. To that end, the contrast to noise ratio have been calculated and compared with some with previous methods. Furthermore, the free response receiver operating characteristic (FROC) curve have been used to measure the effect of the proposed enhancement on the microcalcification cluster detectability.
In this paper, new mass features based on inter-view similarity in DBT projection views are proposed for classifying masses, aiming to effectively reducing false-positives (FPs). The proposed features are focused on utilizing inter-view information in projection views. The FPs induced by overlapping tissues of different depth could be observed differently between projection views, while masses could appear similar. To utilize the observation, the inter-view similarity measure is developed by utilizing the normalized cross-correlation between ROIs in projection views. In the analysis of inter-view similarities of masses and FPs, it is showed that inter-view similarities of FPs are lower than those of masses. To that end, new features are proposed to encode aforementioned difference of inter-view similarities between FPs and masses. Experimental results show that the proposed features can improve the mass classification performance in projection views in terms of the area under the ROC curve.
KEYWORDS: Digital breast tomosynthesis, Mammography, 3D image processing, Computer aided design, Information fusion, CAD systems, Digital mammography, Computer aided diagnosis and therapy, 3D image reconstruction
In this study, a novel mass detection framework that utilizes the information from synthetic mammograms has been developed for detecting masses in digital breast tomosynthesis (DBT). In clinical study, it is demonstrated that the combination of DBT and full field digital mammography (FFDM) increases the reader performance. To reduce the radiation dose in this approach, synthetic mammogram has been developed in previous researches and it is demonstrated that synthetic mammogram can alternate the FFDM when it is used with DBT. In this study, we investigate the feasibility of the combined approach of DBT and synthetic mammogram in point of computer-aided detection (CAD). As a synthetic mammogram, two-dimensional image was generated by adopting conspicuous voxels of three-dimensional DBT volume in our study. The mass likelihood scores estimated for each mass candidates in synthetic mammogram and DBT are merged to differentiate masses and false positives (FPs) in combined approach. We compared the performance of detecting masses in the proposed combined approach and DBT alone. A clinical data set of 196 DBT volumes was used to evaluate the different detection schemes. The combined approach achieved sensitivity of 80% and 89% with 1.16 and 2.37 FPs per DBT volume. The DBT alone approach achieved same sensitivities with 1.61 and 3.46 FPs per DBT volume. Experimental results show that statistically significant improvement (p = 0.002) is achieved in combined approach compared to DBT alone. These results imply that the information fusion of synthetic mammogram and DBT is a promising approach to detect masses in DBT.
KEYWORDS: Image segmentation, Ultrasonography, 3D image processing, Neodymium, Medical imaging, Image analysis, Distance measurement, Time metrology, MATLAB, Medicine
In this study, new free fluid segmentation method is proposed, aiming to increase segmentation accuracy on free fluids, at the same time, decrease processing time, regardless of the accuracy of initial seeds. In order to segment free fluid regions fast and accurate, we propose a new free fluid segmentation based on Chan-vese level-set with an improved initialization using minimum variance region growing. The proposed method is devised to take complementary effects on both methods. In experiments, the effectiveness of the proposed method is demonstrated with 3D US volumes in terms of Dice’s coefficient, volume difference, Hausdorff distance and processing time. Results show that the proposed method outperforms CVLS and MVRG in terms of processing time as well as segmentation accuracy.
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.
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.
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.
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