Automatic microcalcification and cluster detection for digital and digitised mammograms

Oliver, Arnau; Torrent, Albert; Lladó, Xavier; Tortajada, Meritxell; Tortajada, Lidia; Sentís, Melcior; Freixenet, Jordi; Zwiggelaar, Reyer

doi:10.1016/j.knosys.2011.11.021

Cited by 104 publications

(107 citation statements)

References 24 publications

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“…They also performed benign/ malignant classification. Oliver et al [16] extracted image features with a bank of filters, and a boosting method is used to separate MC from non-MC. The dataset they used included the MIAS dataset (322 mammograms) and another 280 FFDM mammograms.…”

Section: Discussionmentioning

confidence: 99%

“…b Enlarged view of the microcalcification part of a outlined with a red rectangle lower specificity (87.77 %). In the work of Oliver et al [16], the individual microcalcification detection is based on local image features of the microcalcifications from a bank of filters. A pixel-based boosting classifier is then trained, and salient features were selected.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Finally, diverse Adaboost support vector machine (SVM) was used as second level classifier. Oliver et al [16] detected the MCs based on extracting local features for characterizing the morphology of the MCs. The developed approach automatically learns and selects the most salient features.…”

Section: Mcs Detectionmentioning

confidence: 99%

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Yanfeng¹,

Chen²,

Chen³

et al. 2016

J Health Med Inform

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Computer-aided detection (CAD) systems can be served as a second view for radiologist. An overview of recent development in CAD methods are presented in this paper. Abnormalities detection, abnormalities classification and content-based image retrieval (CBIR) are briefly reviewed. For the abnormalities detection, micro-calcification detection, mass detection and multi-view based detection are introduced. For the abnormalities classification, micro-calcification classification and mass classification are given.

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“…As a result, most of the techniques focus on two types of breast cancer: microcalcifications and masses [20]. Oliver et al [35] presented a knowledge-based approach for automatic detection of microcalcifications and clusters in mammographic images by using local features extracted from a bank of filters to obtain a local description of the microcalcifications morphology. Kabbadj et al [25] also presented a novel approach to detect microcalcifications on digitized mammograms using shape features, fuzzy logic, and SVM.…”

Section: Literature Surveymentioning

confidence: 99%

“…Shanthi and Bhaskaran [41] proposed an integrated methodology of intuitionistic fuzzy C-means clustering, discrete wavelet feature extraction technique, and a self-adaptive resource allocation network classifier for automatic detection and classification of breast cancer in mammogram images. Oliveira et al [35] developed content-based image retrieval (CBIR) system in support with the classification of breast tissue density which can be used in the processing chain for lesion segmentation and classification.…”

Section: Literature Surveymentioning

confidence: 99%

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

2014

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This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128 × 128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99 % sensitivity and 99 % specificity, and on DDSM mammogram database, it obtained 97 % sensitivity and 96 % specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the S. Sharma · P. Khanna ( ) Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Dumna Airport Road, P.O.: Khamaria, Jabalpur, Madhya Pradesh 482 005, India e-mail: pkhanna@iiitdmj.ac.in S. Sharma e-mail: shubhi.sharma@iiitdmj.ac.in other well-known texture descriptors namely gray-level cooccurrence matrix (GLCM) and discrete cosine transform (DCT).

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Automatic microcalcification and cluster detection for digital and digitised mammograms

Cited by 104 publications

References 24 publications

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

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

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM

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