Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments

He, Wenda; Denton, Erika R. E.; Stafford, Kirsten; Zwiggelaar, Reyer

doi:10.1016/j.bspc.2011.03.008

Cited by 17 publications

(24 citation statements)

References 24 publications

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“…[5] (79.3%), Chen et al [7,9] (59%, 70%, 72%, 75% and 76%), Bovis and Singh [3] (71.4%), and He et al [11] (70%). However, the methods of Parthaláin et al [6] and Oliver et al [2] achieved 91.4% and 86%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 3 summarises the feature extraction process in this study. Note that, in comparison to the other methods [2][3][4][6][7][8][9][10][11][12] our proposed method applied the LTP operators only within the F GD roi and features were extracted at eight different orientations. Fig.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Although dense breasts can be inherited genetically, many studies have indicated that breast density is a strong risk factor for developing breast cancer [2][3][4][6][7][8][9][10][11][12]. Based on the Breast Imaging Reporting and Data System (BI-RADS), there are four major categories used for classifying breast density: (a) predominantly fat, (b) fat with some fibroglandular tissue, (c) heterogeneously dense and (d) extremely dense.…”

Section: Introductionmentioning

confidence: 99%

“…Petroudi et al [12] implemented breast density segmentation using textons based on the Maximum Response 8 (MR8) filter bank and reported 75.5% accuracy. He et al [11] achieved an accuracy of 70% using the relative proportions of the four Tabár's building blocks. Muštra et al [5] captured the characteristics of the breast region using multi-resolution of first and second-order statistical features and reported 79.3% accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the proposed methods [2][3][4][6][7][8][9][10][11][12] in the literature extract texture information from the whole breast region whereas most dense tissues are located within the breast fibroglandular disk (F GD roi ). In this paper, we propose a method that extracts texture features from the F GD roi only (see Figure 3 later) to obtain more descriptive information.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Breast Density Classification Using Local Ternary Patterns in Mammograms

Rampun

Morrow

Scotney

et al. 2017

Lecture Notes in Computer Science

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Abstract. This paper presents a method for breast density classification. Local ternary pattern operators are employed to model the appearance of the fibroglandular disk region instead of the whole breast region as the majority of current studies have done. The Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method achieved 82.33% accuracy which is comparable with some of the best methods in the literature based on the same dataset and evaluation scheme.

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Section: Resultsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Breast Density Classification Using Local Ternary Patterns in Mammograms

Rampun

Morrow

Scotney

et al. 2017

Lecture Notes in Computer Science

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Image Analysis and Recognition

Rampun¹,

Morrow²,

Scotney³

et al. 2017

Lecture Notes in Computer Science

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material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Breast Density Classification Using Multiresolution Local Quinary Patterns in Mammograms

Rampun

Morrow

Scotney

et al. 2017

Communications in Computer and Information Science

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Abstract. This paper presents a method for breast density classification using local quinary patterns (LQP) in mammograms. LQP operators are used to capture the texture characteristics of the fibroglandular disk region (F GDroi) instead of the whole breast region as the majority of current studies have done. To maximise the local information, a multiresolution approach is employed followed by dimensionality reduction by selecting dominant patterns only. Subsequently, the Support Vector Machine classifier is used to perform the classification and a stratified ten-fold cross-validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to 85.6% accuracy which is comparable with the state-of-the-art in the literature. Our contributions are two fold: firstly, we show the role of the fibroglandular disk area in representing the whole breast region as an important region for more accurate density classification and secondly we show that the LQP operators can extract discriminative features comparable with the other popular techniques such as local binary patterns, textons and local ternary patterns (LTP).

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Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments

Cited by 17 publications

References 24 publications

Breast Density Classification Using Local Ternary Patterns in Mammograms

Breast Density Classification Using Local Ternary Patterns in Mammograms

Image Analysis and Recognition

Breast Density Classification Using Multiresolution Local Quinary Patterns in Mammograms

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