A Novel Application of Multifractal Features for Detection of Microcalcifications in Digital Mammograms

Li, Haipeng; Mukundan, Ramakrishnan; Boyd, Shelley

doi:10.1007/978-3-030-39343-4_3

Cited by 4 publications

(1 citation statement)

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“…Breast segmentation is first applied to remove nonbreast areas such as background region, pectoral muscle, and label artefacts. A multifractal-method-based feature-enhanced image [ 30 ] is used to highlight the contrast between image background and the breast tissue region. The intensity thresholding method and morphological operations [ 31 ] are used to separate breast region and artefacts from the background.…”

Section: Methodsmentioning

confidence: 99%

Spatial Distribution Analysis of Novel Texture Feature Descriptors for Accurate Breast Density Classification

Mukundan

Boyd³

2022

Sensors

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Breast density has been recognised as an important biomarker that indicates the risk of developing breast cancer. Accurate classification of breast density plays a crucial role in developing a computer-aided detection (CADe) system for mammogram interpretation. This paper proposes a novel texture descriptor, namely, rotation invariant uniform local quinary patterns (RIU4-LQP), to describe texture patterns in mammograms and to improve the robustness of image features. In conventional processing schemes, image features are obtained by computing histograms from texture patterns. However, such processes ignore very important spatial information related to the texture features. This study designs a new feature vector, namely, K-spectrum, by using Baddeley’s K-inhom function to characterise the spatial distribution information of feature point sets. Texture features extracted by RIU4-LQP and K-spectrum are utilised to classify mammograms into BI-RADS density categories. Three feature selection methods are employed to optimise the feature set. In our experiment, two mammogram datasets, INbreast and MIAS, are used to test the proposed methods, and comparative analyses and statistical tests between different schemes are conducted. Experimental results show that our proposed method outperforms other approaches described in the literature, with the best classification accuracy of 92.76% (INbreast) and 86.96% (MIAS).

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Section: Methodsmentioning

confidence: 99%