Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

Pérez‐Benito, Francisco Javier; Signol, François; Pérez-Cortés, Juan-Carlos; Pollán, Marina; Pérez‐Gómez, Beatriz; Salas-Trejo, Dolores; Casals, María Rosa; Failde, Inmaculada; Llobet, Rafael

doi:10.1016/j.cmpb.2019.05.022

Cited by 12 publications

(13 citation statements)

References 44 publications

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“…These were often contemporaneous with diagnosis (measured on contralateral breast). 33,40,47-49 Model building details and results were not routinely reported. Table 3 gives the AUC for a baseline model without texture and then the value for the model with texture when these were reported separately.…”

Section: Resultsmentioning

confidence: 99%

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Anandarajah¹,

Chen

Colditz³

et al. 2021

Preprint

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This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to prediction of future breast cancer including the time from mammogram to diagnosis of breast cancer, and methods for the identification of texture features and selection of features for inclusion in analysis. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. Of these, 7 assessed texture features from film mammograms images, 3 did not report details of the image used, and the others used full field mammograms from Hologic, GE and other manufacturers. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. Reduction in number of features chosen for analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

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Section: Resultsmentioning

confidence: 99%

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Anandarajah¹,

Chen

Colditz³

et al. 2021

Preprint

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show abstract

“…Researchers obtained an accuracy of 91.37% and 93.22% in the MIAS and DDSM datasets, respectively. Many studies have exploited the integration between different features of shape and texture to improve the CAD system for the classification of breast cancer [46][47][48].…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Zebari

Ibrahim²,

Zeebaree

et al. 2021

Applied Sciences

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Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.

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“…The SIFT features were used to realize the classification of neuroblastoma histological images [11]. The HOG features were utilized for risk estimation of breast cancer development [12]. Furthermore, the HOG features were combined with the SIFT features to realize the classification of brain disease in MR images [13].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

et al. 2020

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Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity. INDEX TERMS Medical image classification, hybrid neural network, PCANet, DenseNet.

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Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts

Cited by 12 publications

References 44 publications

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet

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