Breast region segmentation and pectoral muscle removal in mammograms

Slavkovic-Ilic, Marijeta; Gavrovska, Ana; Milivojević, Milan S.; Reljin, Irini; Reljin, Branimir

doi:10.5937/telfor1601050s

Cited by 15 publications

(5 citation statements)

References 9 publications

(12 reference statements)

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“…The label artefacts can be removed by keeping only the largest connected area (breast region). K-means algorithm and polynomial fitting approach [ 32 ] are employed to eliminate pectoral muscle from the breast region in MLO view mammograms. A median filter of 3 × 3 size is used to reduce noise.…”

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%

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

Mukundan

Boyd³

2022

Sensors

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“…Artifacts outside the breast region, such as labels, are eliminated by applying an active contour process 52 . The pectoral muscle is also removed from the breast region in MLO view mammograms by using K‐means and polynomial fitting approaches 53 . As related studies 22,25,54 employed different cropping frames of the tumor ROI, thus we denote the enhanced non‐cropping images as the maximal frame, then further apply two cropping strategies to obtain a tight frame and loose frame.…”

Section: Experimental Designmentioning

confidence: 99%

TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

Liu

et al. 2023

Medical Physics

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Background: Breast cancer is one of the most prevalent malignancies diagnosed in women. Mammogram inspection in the search and delineation of breast tumors is an essential prerequisite for a reliable diagnosis. However, analyzing mammograms by radiologists is time-consuming and prone to errors. Therefore, the development of computer-aided diagnostic (CAD) systems to automate the mass segmentation procedure is greatly expected. Purpose: Accurate breast mass segmentation in mammograms remains challenging in CAD systems due to the low contrast, various shapes, and fuzzy boundaries of masses. In this paper, we propose a fully automatic and effective mass segmentation model based on deep learning for improving segmentation performance. Methods:We propose an effective transformer-based encoder-decoder model (TrEnD). Firstly, we introduce a lightweight method for adaptive patch embedding (APE) of the transformer, which utilizes superpixels to adaptively adjust the size and position of each patch. Secondly, we introduce a hierarchical transformer-encoder and attention-gated-decoder structure, which is beneficial for progressively suppressing interference feature activations in irrelevant background areas. Thirdly, a dual-branch design is employed to extract and fuse globally coarse and locally fine features in parallel, which could capture the global contextual information and ensure the relevance and integrity of local information. The model is evaluated on two public datasets CBIS-DDSM and INbreast. To further demonstrate the robustness of TrEnD, different cropping strategies are applied to these datasets, termed tight, loose, maximal, and mixframe.Finally,ablation analysis is performed to assess the individual contribution of each module to the model performance. Results:The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 92.20% and 85.81% on the mix-frame CBIS-DDSM, while 91.83% and 85.29% for the mix-frame INbreast, respectively. The segmentation performance outperforms the current state-of -the-art approaches. By adding the APE and attention-gated module, the Dice and IoU have improved by 6.54% and 10.07%. Conclusion: According to extensive qualitative and quantitative assessments, the proposed network is effective for automatic breast mass segmentation, and has adequate potential to offer technical assistance for subsequent clinical diagnoses.

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“…An intensity thresholding method and morphological operations [35] are used to separate the breast region from the background. A K-means algorithm and polynomial fitting approach [36] are employed to distinguish the pectoral muscle region from the breast region in MLO view mammograms. A median filter of 3 × 3 size is used to reduce noise.…”

Section: Mammogram Pre-processingmentioning

confidence: 99%

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

Mukundan

Boyd³

2021

J. Imaging

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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.

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Breast region segmentation and pectoral muscle removal in mammograms

Cited by 15 publications

References 9 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

TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

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

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