Variations on breast density and subtlety of the findings require different computational intelligence pipelines for the diagnosis of clustered microcalcifications

Andreadis, Ioannis; Spyrou, George M.; Ligomenides, Panos A.; Nikita, Konstantina S.

doi:10.1109/bibe.2013.6701693

Cited by 4 publications

(14 citation statements)

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“…Many studies report high values of A z (A z >0.85), but for a small number of cases (cases<150), without discussing important factors such as the subtlety of the cases [4]. In a previous study [14], we have studied various subsets of mammograms and we have located a subset consisted of 256 cases where we reported A z equal to 0.909. When implementing the proposed methodologies on the whole database (1715 cases) we reported a value equal to 0.75 [5].…”

Section: Discussionmentioning

confidence: 92%

“…The framework investigated in the current study has been presented in a previous work [5], whose efficacy has been investigated using subsets of varying findings' subtlety [14]. The main steps of this CADx framework are presented in fig.1 in blue color.…”

Section: A Cad X Frameworkmentioning

confidence: 99%

“…Therefore, each ROI is projected on the corresponding location into the atlas shape. More specific information on the generation of breast atlases is given in our previous work [14].…”

Section: B Clustered Microcalcifications' Atlasesmentioning

confidence: 99%

“…Previously, we have indicated the crucial role that the composition of the dataset plays on the classification performance [14]. The grade of difficulty for cluster's analysis is characterized by a value of subtlety (ranging from 1 to 5), provided in the database, where smaller values imply > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 5…”

Section: Data Collectionmentioning

confidence: 99%

“…Occurrence maps for (a) the CC view and (b) the MLO view and the corresponding probabilistic maps for (a) the CC view and (b) the MLO view[14].…”

mentioning

confidence: 99%

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A CAD$_{\bf x}$ Scheme for Mammography Empowered With Topological Information From Clustered Microcalcifications’ Atlases

Andreadis

Spyrou

Nikita

2015

IEEE J. Biomed. Health Inform.

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A computer-aided diagnosis (CADx ) framework for the diagnosis of clustered microcalcifications (MCs) has already been developed, which is based on the analysis of MCs' morphologies,the shape of the cluster they form and the texture of the surrounding tissue. In this study, we investigate the diagnostic information that the relative location of the cluster inside the breast may provide. Breast probabilistic maps are generated and adopted in the CADx pipeline, expecting to empower its diagnostic procedure. We propose a flowchart combining alternative classification algorithms and the aforementioned probabilistic maps in order to provide a final risk for malignancy for new considered mammograms. For the evaluation performance, a large dataset of mammograms provided from the Digital Database of Screening Mammography (DDSM) has been used. The obtained results indicate that the proposed modifications lead to the enhancement of the diagnostic process, as the classification results are further improved. Additionally, a straightforward comparison between the CADx pipeline and the radiologists who assessed the same mammograms, reveal that the CADx pipeline performs toward the right direction, as the sensitivity remains at high levels, while improving both the accuracy, from 51.4% to 69%, and the specificity, from 16.6% to 54.7%.

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Section: Discussionmentioning

confidence: 92%

Section: A Cad X Frameworkmentioning

confidence: 99%

“…Therefore, each ROI is projected on the corresponding location into the atlas shape. More specific information on the generation of breast atlases is given in our previous work [14].…”

Section: B Clustered Microcalcifications' Atlasesmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

“…Occurrence maps for (a) the CC view and (b) the MLO view and the corresponding probabilistic maps for (a) the CC view and (b) the MLO view[14].…”

mentioning

confidence: 99%

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A CAD$_{\bf x}$ Scheme for Mammography Empowered With Topological Information From Clustered Microcalcifications’ Atlases

Andreadis

Spyrou

Nikita

2015

IEEE J. Biomed. Health Inform.

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Fused man-machine classification schemes to enhance diagnosis of breast microcalcifications

Andreadis¹,

Sevastianos²,

George³

et al. 2017

Meas. Sci. Technol.

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Computer aided diagnosis (CADx) approaches are developed towards the effective discrimination between benign and malignant clusters of microcalcifications. Different sources of information are exploited, such as features extracted from the image analysis of the region of interest, features related to the location of the cluster inside the breast, age of the patient and descriptors provided by the radiologists while performing their diagnostic task. A series of different CADx schemes are implemented, each of which uses a different category of features and adopts a variety of machine learning algorithms and alternative image processing techniques. A novel framework is introduced where these independent diagnostic components are properly combined according to features critical to a radiologist in an attempt to identify the most appropriate CADx schemes for the case under consideration. An open access database (Digital Database of Screening Mammography (DDSM)) has been elaborated to construct a large dataset with cases of varying subtlety, in order to ensure the development of schemes with high generalization ability, as well as extensive evaluation of their performance. The obtained results indicate that the proposed framework succeeds in improving the diagnostic procedure, as the achieved overall classification performance outperforms all the independent single diagnostic components, as well as the radiologists that assessed the same cases, in terms of accuracy, sensitivity, specificity and area under the curve following receiver operating characteristic analysis.

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Investigating the performance of a CAD<inf>x</inf> scheme for mammography in specific BIRADS categories

Andreadis

Nikita

Spyrou

2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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A Computer Aided Diagnosis (CAD x ) pipeline has already been introduced to discriminate between benign and malignant clusters of microcalcifications (MCs). In this study, we evaluate the specific methodologies using cases from publicly available databases of mammograms, the MIAS database and the Digital Database of Screening Mammography (DDSM). Specifically, we investigate various subsets of regions of interest (ROIs) containing cluster of MCs, following the BIRADS assessment performed by radiologists who have participated in the preparation of the databases. The obtained results indicate that there are specific BIRADS categories where the proposed system provides high classification results. Furthermore, it seems that the best results are achieved in obscure cases, where the radiologists have doubts about their diagnosis and recommend extra medical examinations and short follow-up. The reported results indicate the potential of the proposed CAD x system to assist the diagnostic task of radiologists by providing a reliable second opinion concerning the diagnosis of a cluster of MCs.

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Variations on breast density and subtlety of the findings require different computational intelligence pipelines for the diagnosis of clustered microcalcifications

Cited by 4 publications

References 10 publications

A CAD$_{\bf x}$ Scheme for Mammography Empowered With Topological Information From Clustered Microcalcifications’ Atlases

A CAD$_{\bf x}$ Scheme for Mammography Empowered With Topological Information From Clustered Microcalcifications’ Atlases

Fused man-machine classification schemes to enhance diagnosis of breast microcalcifications

Investigating the performance of a CAD<inf>x</inf> scheme for mammography in specific BIRADS categories

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