Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms

Muštra, Mario; Grgić, Mislav; Rangayyan, Rangaraj M.

doi:10.1007/s11517-015-1411-7

Cited by 77 publications

(45 citation statements)

References 48 publications

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“…By identifying the threshold value from histogram, background of mammography removed. Using identified threshold value, image binarized and ordered with connected components, the largest component indicated the breast profile [21]. Computer Aided Diagnosis -Medical Image Analysis Techniques http://dx.doi.org/10.5772/intechopen.69792…”

Section: Removal Of Backgroundmentioning

confidence: 99%

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Computer Aided Diagnosis - Medical Image Analysis Techniques

Halalli¹,

Makandar²

2018

Breast Imaging

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Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties.

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Section: Removal Of Backgroundmentioning

confidence: 99%

“…Another challenging task in preprocessing of mammography is removal of pectoral muscle [21]. The modified region growing method used to remove the pectoral muscle by identifying the origin of the image either left oriented or right oriented.…”

Section: Removal Of Pectoral Musclementioning

confidence: 99%

Computer Aided Diagnosis - Medical Image Analysis Techniques

Halalli¹,

Makandar²

2018

Breast Imaging

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“…In accord with recent studies of MRI data segmentation between the breast and the pectoral muscle, the anterior extent of Ella’s chest wall remained flat and aligned on the coronal plane. [34, 35] To position the breast phantoms on the Ella model before the fusing operation detailed below, two voxels were manually selected for centering the posterior coronal layer of all left and right breast phantoms. These locations were determined by orienting the base of each breast phantom on the inmost posterior coronal plane in Ella’s anterior thorax that solely overlapped breast tissue, fat, and skin, i.e., without encroaching on the pectoral fascia or adjacent muscle tissue.…”

Section: Methodsmentioning

confidence: 99%

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations

Rispoli

Wright

Malloy

et al. 2017

JBGC

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Background Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Methods Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. Results The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Conclusions Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue.

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“…Breast cancer is one of the high-incidence cancer types among women worldwide [1, 2]. Early detection of breast cancer is crucial for successful treatment and reducing the mortality rate [2].…”

Section: Introductionmentioning

confidence: 99%

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

Liu

Qin

et al. 2018

Med Biol Eng Comput

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Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: preprocessing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice.

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Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms

Cited by 77 publications

References 48 publications

Computer Aided Diagnosis - Medical Image Analysis Techniques

Computer Aided Diagnosis - Medical Image Analysis Techniques

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

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