Classification Using Fractal Features of Well-Defined Mammographic Masses Using Power Spectral Analysis and Differential Box Counting Approaches

Dhanalekshmi, P. S. Menon; Phadke, Anuradha C.

doi:10.1007/978-81-322-2135-7_53

Cited by 3 publications

(4 citation statements)

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“…As shown, the results obtained by our method are better than those obtained by Rangayyan et al [24], Surendiran and Vadivel [13], Yanfeng et al [12] and Menon and Phadke [15]. This ranking is good taking into account the generality of our method compared with the other techniques, and its applicability to process various mass shapes, margin and densities.…”

Section: Resultsmentioning

confidence: 51%

“…In [15] two methods were proposed by Menon and Phadke: power spectral analysis (PSA) and differential box counting method (DBCM); both methods were applied for the fractal dimension computation of mass boundary to differentiate between benign and malignant lesions. Their paper particularly focuses only on the well-defined masses; the classification step shows that PSA method yields better accuracy than DBCM.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of CADx of breast masses using shape, margin or textural features is addressed; several works explore the field of automatic mammographic masses classification, but unfortunately, most classification schemes mentioned in the literature classify masses into benign and malignant categories [9][10][11][12][13][14][15][16]; whereas, the use of BI-RADS lexicon is limited on the differentiation between density levels [17], shape categories [18,19] or margin types [20,21], instead of classifying abnormalities according to BI-RADS classes.…”

Section: Related Workmentioning

confidence: 99%

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Mammographic mass classification according to Bi‐RADS lexicon

Ferkous¹,

Farida

2016

IET Computer Vision

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The goal of this study is to propose a computer-aided diagnosis system to differentiate between four breast imaging reporting and data system (Bi-RADS) classes in digitised mammograms. This system is inspired by the approach of the doctor during the radiologic examination as it was agreed in BI-RADS, where masses are described by their form, their boundary and their density. The segmentation of masses in the authors' approach is manual because it is supposed that the detection is already made. When the segmented region is available, the features extraction process can be carried out. 22 visual characteristics are automatically computed from shape, edge and textural properties; only one human feature is used in this study, which is the patient's age. Classification is finally done using a multi-layer perceptron according to two separate schemes; the first one consists of classify masses to distinguish between the four BI-RADS classes (2, 3, 4 and 5). In the second one the authors classify abnormalities on two classes (benign and malign). The proposed approach has been evaluated on 480 mammographic masses extracted from the digital database for screening mammography, and the obtained results are encouraging. This paper is organised as the following: Section 2 presents the practical background related to the BI-RADS assessment; in Section 3, we review related researches; in Section 4, we explain the features extraction methods investigated and classification schemes used in the present work; in Section 5, we show the

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Section: Resultsmentioning

confidence: 51%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Mammographic mass classification according to Bi‐RADS lexicon

Ferkous¹,

Farida

2016

IET Computer Vision

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“…So in literature [ 16 ] the breast mass is classified with the fractal analysis and the classification accuracy is greater than 80%. However, the function of radial versus degree could lead to a multi-value function in the case of an irregular or speculated margin [ 17 ]; the signature computed in this manner would also have ranges of undefined values in the case of a contour for which the centroid falls outside the region enclosed by the contour. Rangaraj et al [ 16 ] improved the method.…”

Section: Introductionmentioning

confidence: 99%

Breast masses in mammography classification with local contour features

Meng

Wang

et al. 2017

BioMed Eng OnLine

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BackgroundMammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.MethodsIn this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.ResultsThe proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.ConclusionThe performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0332-0) contains supplementary material, which is available to authorized users.

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Algorithms for Detection and Classification of Abnormality in Mammograms

Phadke¹,

Rege

2018

Handbook of Research on Information Security in Biomedical Signal Processing

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Mammography is a popular imaging modality currently in use for routine screening of breast. Radiologists look for some of the significant signs of breast cancer while examining the mammogram visually. These signs are bounded masses, clusters of micro-calcifications, spiculations, and architectural distortions. Developing computer-aided algorithms for the detection and classification of abnormalities in mammograms is an extremely challenging task because of significant variableness in the type, size, shape, texture variation of abnormal region, and variability in the structure of surrounding tissues of the breast. The main objective of this chapter is to introduce dominant features of various signs of abnormalities and to discuss techniques to detect various abnormalities in mammograms. This knowledge will help to develop a system that is useful for the early detection and classification of breast cancer.

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Classification Using Fractal Features of Well-Defined Mammographic Masses Using Power Spectral Analysis and Differential Box Counting Approaches

Cited by 3 publications

References 4 publications

Mammographic mass classification according to Bi‐RADS lexicon

Mammographic mass classification according to Bi‐RADS lexicon

Breast masses in mammography classification with local contour features

Algorithms for Detection and Classification of Abnormality in Mammograms

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