Can texture of tissue surrounding microcalcifications in mammography be used for breast cancer diagnosis?

Karahaliou, Anna; Boniatis, Ioannis; Sakellaropoulos, P.; Skiadopoulos, Spyros; Panayiotakis, George; Costaridou, Lena

doi:10.1016/j.nima.2007.06.064

Cited by 16 publications

(16 citation statements)

References 12 publications

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“…Finally a SVM classifier was trained to classify MC clusters as either benign or malignant. Karahaliou et al [44] tested the performance of texture features extracted from the tissue surrounding MCs. The author reported the best classification accuracy is 89%.…”

Section: Mcs Classificationmentioning

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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Section: Mcs Classificationmentioning

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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“…Recent studies, Karahaliou et al [28] investigate whether texture properties of the tissue surrounding micro-calcifications using a wavelet transform. Thirteen textural features were calculated from four GLCMs.…”

Section: Feature Extractionmentioning

confidence: 99%

Mammogram Image Segmentation Using Rough Clustering

Boss¹

2013

IJRET

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“…The hypothesis was tested on a dataset of 54 cases (18 malignant/36 benign) exploiting GLCMs and fractal geometry based features and achieved a classification performance 85% (sensitivity 89%, specificity 83%) employing Linear and Logistic Discriminant analysis. Since its introduction on digital scout views, the feasibility of the MCs surrounding tissue analysis hypothesis has been further investigated on screening mammograms (Karahaliou et al, 2007a(Karahaliou et al, , 2007b). …”

Section: Texture Analysis Of the Tissue Surrounding Mcsmentioning

confidence: 99%

“…In the following sections (4.1, 4.2 and 4.3) the potential contribution of this approach in CADx of MC clusters is investigated and compared to current state-of-the-art approaches, by means of a meta-analysis application paradigm of the sample analysed in (Karahaliou et al, 2007aArikidis et al, 2008).…”

Section: Texture Analysis Of the Tissue Surrounding Mcsmentioning

confidence: 99%