Contrast Enhancement in Dense Breast Images to Aid Clustered Microcalcifications Detection

Nunes, Fátima L. S.; Schiabel, Homero; Góes, Cláudio Eduardo

doi:10.1007/s10278-005-6976-5

Cited by 29 publications

(30 citation statements)

References 15 publications

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“…The results obtained from these evaluation were used as input data to the WFR procedure in order to restore the set of digital mammographic images. Finally, WFR scheme performance was evaluated as a preprocessing step of a previously developed scheme [34] in order to investigate the effect on its performance in clustered microcalcifications detection. Also the characteristic curve of the digitizer used in this work was determined according to procedures previously described [32,33] , from the relation between the optical densities (OD) of each standard radiographic image region and their average pixel values.…”

Section: Resultsmentioning

confidence: 99%

Preprocessing for improving CAD scheme performance for microcalcification detection based on mammography imaging quality parameters

Schiabel¹,

Vieira²,

Ventura³

2009

Medical Imaging 2009: Computer-Aided Diagnosis

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Database characteristics can affect significantly the performance of a mammography CAD scheme. Hence adequate performance comparison among different CAD schemes is not suitable since a single scheme could present different results depending on the set of chosen cases. Images in database should follow a set of quality criteria, since the imaging process up to digital file. CAD schemes can not be developed without a database used to test their efficacy, but each database with particular characteristics may influence on the processing scheme performance. A possible solution could be using information on the imaging equipment characteristics. This work describes a preprocessing in order to "compensate" the image degradation during the acquisition steps, assuring a better "uniformity" relative to the images quality. Thus, poor quality images would be restored, providing therefore some independence on the images source to CAD schemes and allowing to reach the better possible performance. Tests performed with mammography images sets reported a 14% increase in sensitivity for microcalcifications detection. Although this result was followed by a little increase in false positive rates, simple changes in techniques parameters can provide the same improvement but with a reduction of the false positive detections.

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Section: Resultsmentioning

confidence: 99%

Preprocessing for improving CAD scheme performance for microcalcification detection based on mammography imaging quality parameters

Schiabel¹,

Vieira²,

Ventura³

2009

Medical Imaging 2009: Computer-Aided Diagnosis

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“…In [42], Nunes et al obtained A z = 0.93 for MC detection on a database of 121 mammograms by combining three contrast enhancement techniques. Papadopoulos et al [43] investigated five image enhancement techniques and obtained A z = 0.92 for MC detection on a database consisting of 60 mammograms from the MIAS and Nijmegen databases.…”

Section: Discussionmentioning

confidence: 99%

Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

Liu

Mei

et al. 2015

EURASIP J. Adv. Signal Process.

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Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/ image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

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“…We proposed, applied, and evaluated a methodology based on graphic oracles to test a segmentation scheme used in [17]. We used the O-FIm framework as base technology to conduct all the experiments.…”

Section: Research Design and Methodologymentioning

confidence: 99%

“…To reach this goal, we use a previously developed framework named OFIm (Oracle For Images) 1 as a support tool. The validation is conducted with segmentation outputs of a breast region in mammographic images, which is part of a CAD system [17].…”

Section: Introductionmentioning

confidence: 99%

Applying graphical oracles to evaluate image segmentation results

2017

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Segmentation plays an important role in the pattern recognition and image processing areas. Several techniques have been proposed aiming at solving generic issues or particular applications. Traditionally, these techniques have been evaluated by using the Overlap measure, which verifies the coincident and non-coincident areas between the image resulting from a segmentation process and an image considered correct. Albeit widely, this type of measure does not allow flexibility in the assessment process. We here propose an approach to evaluate segmentation techniques using concepts from content-based image retrieval and considering a methodology for testing generic programs with graphical outputs, named graphic oracle. Our approach was applied to evaluate the segmentation of mammographic images, and the results indicate a performance compatible with the traditional measure with more flexibility and precision. Thus, our approach provides a contribution to allow a more flexible segmentation assessment, according to image characteristics and application objectives.

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Contrast Enhancement in Dense Breast Images to Aid Clustered Microcalcifications Detection

Cited by 29 publications

References 15 publications

Preprocessing for improving CAD scheme performance for microcalcification detection based on mammography imaging quality parameters

Preprocessing for improving CAD scheme performance for microcalcification detection based on mammography imaging quality parameters

Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method

Applying graphical oracles to evaluate image segmentation results

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