This paper presents a novel scheme for mass segmentation in digitized mammograms, which is based on Graph Cuts
algorithm and multi-scale analysis. The multi-scale method can segment mammographic images with a stepwise process
from global to local segmentation by iterating Graph Cuts. To improve the segmentation efficiency and robustness, the
watershed transform is used for pre-segmentation of the image to produce a region adjacency graph for the following
optimization steps. Besides, this paper proposes a strategy of increasing smoothness energy term step by step in the
Markov Random Field (MRF) image segmentation module, so as to improve the efficiency effectively in the mass
segmentation. The new segmentation strategy effectively improves segmentation performance with less influences of
image noise level. The experimental results demonstrate that the proposed method achieves a better performance in
accuracy and robustness than conventional ones.
KEYWORDS: Breast, Image segmentation, Mammography, Cancer, Visualization, Computer aided diagnosis and therapy, Breast cancer, Data acquisition, Eye models, Data modeling
In the paper, we propose a novel scheme for breast mass segmentation in mammography, which is based on visual
perception and consists of two steps. Firstly, radiologists' eye-gazing data is recorded by the eye-tracker during reading
and then clustered with a density-based spatial clustering of applications with noise (DBSCAN) algorithm to achieve
seeds locating radiologists' regions of interest (ROIs). The seeds-based region growing (SBRG) algorithm is applied to
buckle ROIs containing suspicious lesions. Secondly, in order to achieve fine lesion contour as final result, the ROIs are
segmented with a multi-scale mass segmentation approach using active contour models. The result of applying the
proposed method to the mammograms from both DDSM and Zhejiang Cancer Hospital shows that the achieved average
of overlap rate is 0.5915 and the achieved average of misclassification rate is 0.6342. The innovative point of the
proposed approach is to introduce visual perception into breast mass segmentation, which makes the result of mass
segmentation meet radiologists' subjective demand.
Breast cancer is one of the most common malignant tumors in women. In mammogram retrieval system, the query mass
is ambiguity and difficult to be described because in which the lesion and the normal tissue are physically adjacent. If the
query mass can be processed as an image bag, then the ambiguity can be tackled by multi-instance learning (MIL)
techniques. In this paper, we presented a preliminary study of MIL for mass retrieval in digitized mammograms, and
proposed three image bag generators named J-Bag, A-Bag and K-Bag, respectively. Diverse Density (DD), EM-DD and
BP-MIP were applied as MIL algorithms for mass retrieval. Experimental study was carried out on DDSM database and
another database in which images were collected from the Zhejiang Cancer Hospital in China. Preliminary experiments
showed that the MIL techniques can be applied to the problem of mass retrieval in digitized mammograms and the
proposed bag generators A-Bag and K-Bag can achieve more efficient results than the existing bag generator SBN.
Bilateral mammographic tissue density asymmetry could be an important factor in assessing risk of developing
breast cancer and improving the detection of the suspicious lesions. This study aims to assess whether fusion of the
bilateral mammographic density asymmetrical information into a computer-aided detection (CAD) scheme could
improve CAD performance in detecting mass-like breast cancers. A testing dataset involving 1352 full-field digital
mammograms (FFDM) acquired from 338 cases was used. In this dataset, half (169) cases are positive containing
malignant masses and half are negative. Two computerized schemes were first independently applied to process FFDM
images of each case. The first single-image based CAD scheme detected suspicious mass regions on each image. The
second scheme detected and computed the bilateral mammographic tissue density asymmetry for each case. A fusion
method was then applied to combine the output scores of the two schemes. The CAD performance levels using the
original CAD-generated detection scores and the new fusion scores were evaluated and compared using a free-response
receiver operating characteristic (FROC) type data analysis method. By fusion with the bilateral mammographic density
asymmetrical scores, the case-based CAD sensitivity was increased from 79.2% to 84.6% at a false-positive rate of 0.3
per image. CAD also cued more "difficult" masses with lower CAD-generated detection scores while discarded some
"easy" cases. The study indicated that fusion between the scores generated by a single-image based CAD scheme and the
computed bilateral mammographic density asymmetry scores enabled to increase mass detection sensitivity in particular
to detect more subtle masses.
As an important step of mass classification, mass segmentation plays an important role in computer-aided diagnosis
(CAD). In this paper, we propose a novel scheme for breast mass segmentation in mammograms, which is based on level
set method and multi-scale analysis. Mammogram is firstly decomposed by Gaussian pyramid into a sequence of images
from fine to coarse, the C-V model is then applied at the coarse scale, and the obtained rough contour is used as the
initial contour for segmentation at the fine scale. A local active contour (LAC) model based on image local information
is utilized to refine the rough contour locally at the fine scale. In addition, the feature of area and gray level extracted
from coarse segmentation is used to set the parameters of LAC model automatically to improve the adaptivity of our
method. The results show the higher accuracy and robustness of the proposed multi-scale segmentation method than the conventional ones.
In this paper a new approach to mass classification based on multi-agent (MA) method is proposed for CAD in
mammography. Multi-agent method is used here as a method that fuses the classification information from multiple
classifiers in order to obtain a better decision result. Each agent receives the measurement value of individual classifier
as initial value in classifying a sample and sends a message to a decision center. The decision center responds to this
message with analysis of the correlation among these classifiers and their own decisions information. If the analysis
result is conformable to a given standard, the center will provide a final result. Otherwise the message of agent had to be
modified iteratively. 128 ROIs, including 64 benign masses and 64 malignant masses, from the DDSM, were used in the
mass classification experiment. In comparison with the majority voting based fusion method, we evaluated the
performance of proposed multi-agent fusion approach in distinguishing malignant and benign masses. The results
demonstrated that the multi-agent method outperforms the majority voting method. Multi-agent fusion method yielded
an accuracy of 95.47%, while the majority voting method had an accuracy of 92.23%. In addition, a preliminary study of
MA method for mass classification under the bi-view model is reported. All of these experiments showed that the
multi-agent method can play a significant role in multiple classifier fusion to improve mass classification in
mammography.
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