Robust Texture Features for Breast Density Classification in Mammograms

Li, Haipeng; Mukundan, Ramakrishnan; Boyd, Shelley

doi:10.1109/icarcv50220.2020.9305431

Cited by 7 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, mask images are obtained, which are used to extract image features only from the region of interest (breast area) in the following steps. As relative work [ 16 , 17 ] reported promising classification results using resized mammogram images, this study applies the bicubic interpolation method to resize processed images with a scale factor s [ 33 ], resulting in a resized image that is s times the size of original image. Figure 3 and Figure 4 show some examples of segmenting breast region from image background using INbreast and MIAS mammograms, respectively.…”

Section: Methodsmentioning

confidence: 99%

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

Mukundan

Boyd³

2022

Sensors

Self Cite

View full text Add to dashboard Cite

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).

show abstract

Section: Methodsmentioning

confidence: 99%

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

Mukundan

Boyd³

2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…An association between lattice-based texture features and breast cancer was evaluated using logistic regression. Li et al [60] presented a texture feature descriptor for mammographic image classification into different breast density categories. More precisely, they adopted the commonly used local binary patterns (LBP) and considered more feature details by including its variant, local quinary patterns (LQP).…”

Section: Texture Descriptors For Classification Systemsmentioning

confidence: 99%

“…Tai et al [56] obtained 99% sensitivity on DDSM but no data about false positives' rates are given. With the only exception of local patterns [60], when texture features feed into classification systems [58,59,61], they return noticeably high rates of accuracy (93.6% on DDSM), sensitivity and specificity (greater than 99% on MIAS). However, as in the method of Biswas et al [63], the classification systems' performance may slightly drop when other texture features and descriptors are used (mixture of Gaussians).…”

Section: Pros and Consmentioning

confidence: 99%

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

View full text Add to dashboard Cite

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

show abstract

“…Challenging cases with inaccurate mask images generated and their adjusted mask images based on manual operations. References [42][43][44] are cited in the Supplementary Materials.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms

Mukundan

Boyd³

2021

J. Imaging

Self Cite

View full text Add to dashboard Cite

This paper investigates the usefulness of multi-fractal analysis and local binary patterns (LBP) as texture descriptors for classifying mammogram images into different breast density categories. Multi-fractal analysis is also used in the pre-processing step to segment the region of interest (ROI). We use four multi-fractal measures and the LBP method to extract texture features, and to compare their classification performance in experiments. In addition, a feature descriptor combining multi-fractal features and multi-resolution LBP (MLBP) features is proposed and evaluated in this study to improve classification accuracy. An autoencoder network and principal component analysis (PCA) are used for reducing feature redundancy in the classification model. A full field digital mammogram (FFDM) dataset, INBreast, which contains 409 mammogram images, is used in our experiment. BI-RADS density labels given by radiologists are used as the ground truth to evaluate the classification results using the proposed methods. Experimental results show that the proposed feature descriptor based on multi-fractal features and LBP result in higher classification accuracy than using individual texture feature sets.

show abstract

Robust Texture Features for Breast Density Classification in Mammograms

Cited by 7 publications

References 20 publications

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

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

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Novel Texture Feature Descriptors Based on Multi-Fractal Analysis and LBP for Classifying Breast Density in Mammograms

Contact Info

Product

Resources

About