Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

Abdel-Nasser, Mohamed; Rashwan, Hatem A.; Puig, Domènec; Moreno, Antonio

doi:10.1016/j.eswa.2015.07.072

Cited by 61 publications

(35 citation statements)

References 45 publications

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“…Contrasting with several other recently developed methods for quantifying breast density from mammography further highlights the uniqueness of our proposed method. It appears new works published still primarily take a 2D approach to either density- [51,52] or parenchymal pattern-based segmentations [33,37], though our method takes advantage of the third dimension. Other 3D approaches using mammography exist and also become more common, but are often proprietary [19,53] or may require in-image phantoms [18].…”

Section: Discussionmentioning

confidence: 99%

“…More recently, with the emergence and growth of digital mammography technology in parallel with spread of computerized algorithms for image interpretation and diagnosis [29], interest in measuring breast density in a fully automatic, quantitative, as well as volumetric manner has grown [18,[30][31][32][33]. Noticeably, due to the rise in information processing power and growing amounts of data being created, machine learning algorithms have also seen an increased presence in such computer-aided analyses in breast imaging and radiology as a whole [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

et al. 2016

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Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and interpatient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

et al. 2016

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“…Abdel-Nasser et al [44] used a local directional pattern for breast masses classification. Authors have used a mini-MIAS database for experiment, and comparing different size ( 32×32,64×64,75×75 and 150×150 pixels) ROI and finally classify mammogram masses using support vector machine.…”

Section: Related Workmentioning

confidence: 99%

Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

Tripathy¹,

Swarnkar²

2019

IJRTE

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 Abstract: Breast cancer has become a major concern of women health throughout the world and has an important cause of death among women. The important radiographic signs of cancer are the masses visible in the breast. In the initial stage, the masses in the women breast are very strenuous to detect. In many cases, it has been proven that a manual attempt of treatment methods are time consuming and inefficient. Hence there is a basic demand for well-planned methods for diagnosis of the cancerous cells with minimal human participation resulting high in precision. Mammography has been proven as an efficient technique for the identification of cancer in women breast. Automated detection of masses in breast mammogram is the major goal in the identification of cancer in women breast. Machine learning techniques can be used as an effective mechanism by the physician for the early detection of cancer in the breast. By early recognition of malignancy in the breast, patients will get treatment right from the initial stage of cancer which can save their lives. Keywords : breast cancer, computer aided detection (CAD), mammogram, machine learning (ML), region of interest (ROI) .

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“…Histogram of Gradients (HOG) (Ergin & Kilinc, 2015), Local Configure Pattern (LCP) (Ergin & Kilinc, 2015), Uniform Directional Pattern (UDP) (Abdel-Nasser et al, 2015), Local Ternary Pattern (LTP) (Muramatsu et al, 2016), Local Phase Quantization (LPQ) (Ojansivu & Heikkilä, 2008) and Local Binary Pattern (LBP) (Oliver et al, 2007) are some such examples. There are three different variants of LBP, which are usually used for exploiting local textural properties; Uniform Local Binary Pattern (LBP-u), Rotation Invariant Local Binary Pattern (LBP-ri) and Rotation Invariant Uniform Local Binary Pattern (LBP-riu) (Nanni et al, 2012;Ojala et al, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

Density-wise two stage mammogram classification using texture exploiting descriptors

Shastri

Tamrakar

Ahuja

2018

Expert Systems with Applications

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Breast cancer is becoming pervasive with each passing day. Hence, its early detection is a big step in saving the life of any patient. Mammography is a common tool in breast cancer diagnosis. The most important step here is classification of mammogram patches as normal-abnormal and benign-malignant.Texture of a breast in a mammogram patch plays a significant role in these classifications. We propose a variation of Histogram of Gradients (HOG) and Gabor filter combination called Histogram of Oriented Texture (HOT) that exploits this fact. We also revisit the Pass Band -Discrete Cosine Transform (PB-DCT) descriptor that captures texture information well. All features of a mammogram patch may not be useful. Hence, we apply a feature selection technique called Discrimination Potentiality (DP). Our resulting descriptors, DP-HOT and DP-PB-DCT, are compared with the standard descriptors.Density of a mammogram patch is important for classification, and has not been studied exhaustively. The Image Retrieval in Medical Application (IRMA) database from RWTH Aachen, Germany is a standard database that provides mammogram patches, and most researchers have tested their frameworks only on a subset of patches from this database. We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors. We achieve higher accuracy than all of the existing standard descriptors (more than 92%).

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Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

Cited by 61 publications

References 45 publications

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer

Imaging & Machine Learning Techniques Used for Early Identification of Cancer in Breast Mammogram

Density-wise two stage mammogram classification using texture exploiting descriptors

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