Parenchymal breast density estimation with the use of statistical characteristics and textons

Chatzistergos, Sevastianos; Stoitsis, John; Papaevangelou, A.; Zografos, Georgios; Nikita, Konstantina S.

doi:10.1109/itab.2010.5687686

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Cited by 2 publications

(2 citation statements)

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“…They have been used in a host of applications, including biomedical ones. [ 18 19 20 ] We use an implementation consisting of isotropic Gaussians and Laplacians of Gaussians in addition to oriented Gabor filters as before, for a total of 50 filters. Following the filter phase, we apply a K-means clustering using a Euclidean distance metric to learn a fixed number (we chose 100) of textons.…”

Section: Methodsmentioning

confidence: 99%

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

Bhagavatula

McCann²,

Fickus

et al. 2014

Journal of Pathology Informatics

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Section: Methodsmentioning

confidence: 99%

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

Bhagavatula

McCann²,

Fickus

et al. 2014

Journal of Pathology Informatics

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“…We based our approach on the aforementioned principal treating the mammographic images as "documents" full of "visual words". The basic elements used in our method were "textons" based on statistical and textural characteristics [20,21]. We aimed to evaluate this computerized system and assess whether it can provide an accurate and objective estimation of the breast density and accurately classify the mammograms according to the ACR-BIRADS system.…”

Section: Introductionmentioning

confidence: 99%

Breast density: Computerized analysis on digitized mammograms

Papaevangelou¹,

Chatzistergos

Nikita

et al. 2011

Hellenic J Surg

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Background-Aim: Mammographically dense breast tissue is related to a higher risk of breast cancer. We aim to evaluate a computerized system, assess whether it can provide an accurate and objective estimation of the breast density and if it can accurately classify the mammograms according to the ACR/ BIRADS system. Methods: We retrospectively reviewed the mediolateral oblique (MLO) and cranial-caudal (CC) views of 83 normal mammograms and classified them, both manually and with the use of computerized texture analysis (CTA), according to their density. We grouped the mammograms either into two (ACR 1-2, ACR 3-4) or four categories (ACR 1 to 4). An inter-rater reliability analysis was performed using the kappa statistic to determine consistency among the radiologist and the CTA. Results: The best matching was observed for the MLO view when the classification involved 2 groups (94%).

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Parenchymal breast density estimation with the use of statistical characteristics and textons

Cited by 2 publications

References 15 publications

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples

Breast density: Computerized analysis on digitized mammograms

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