Breast peripheral area correction in digital mammograms

Tortajada, Meritxell; Oliver, Arnau; Martí, Robert; Ganau, Sergi; Tortajada, Lidia; Sentís, Melcior; Freixenet, Jordi; Zwiggelaar, Reyer

doi:10.1016/j.compbiomed.2014.03.010

Cited by 15 publications

(26 citation statements)

References 43 publications

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“…The results are close to the results achieved (i.e. on average > 80%) using the state-of-the-art method [6]. It should be noted that different segmentation (e.g.…”

Section: Segmentationsupporting

confidence: 84%

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Breast image pre-processing for mammographic tissue segmentation

Hogg

Juette

et al. 2015

Computers in Biology and Medicine

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During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

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“…The results are close to the results achieved (i.e. on average > 80%) using the state-of-the-art method [6]. It should be noted that different segmentation (e.g.…”

Section: Segmentationsupporting

confidence: 84%

“…fatty/dense segmentation) and classification (e.g. high/low risk classification) principle and datasets were used in [6].…”

Section: Segmentationmentioning

confidence: 99%

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Breast image pre-processing for mammographic tissue segmentation

Hogg

Juette

et al. 2015

Computers in Biology and Medicine

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“…The database used in this study are mammogram images from MIAS (Mammographic Image Analysis Society) database: 57 normal and 120 abnormal images with a size of 1024 pixels x 1024 pixels and database obtained from UDIAT (Sabaddell Hospital): 52 abnormal images (19 benign and 33 malignant) (Tortajada et al, 2014).…”

Section: Datasetmentioning

confidence: 99%

“…We would like to acknowledge the Mammographic Image Analysis Society (MIAS) and UDIAT database was given by Hospital of Sabbadel (Tortajada et. al, 2014) for providing a digital mammogram dataset.…”

Section: Acknowledgmentmentioning

confidence: 99%

Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

Wisudawati¹,

Madenda²,

Wibowo³

et al. 2020

MAS

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Breast cancer is one of the leading causes of death worldwide among women. According to GLOBOCAN Data, the International Agency for Research on Cancer (IARC), in 2012 there were 14.067.894 new cases of cancer and 8.201.575 deaths from cancer worldwide (Kementerian Kesehatan Republik Indonesia [KemenkesRI], 2015). Mammography is the most common and effective technique for detecting breast tumors. However, mammograms have poor image quality with low contrast. A Computer-Aided Detection (CAD) system has been developed to help radiologists effectively detect lesions on mammograms that indicate the presence of breast tumor. The feature extraction method in the CAD system is an important part of getting high accuracy results in classifying normal and abnormal breast tumors. By using the combination of 2D-Discrete wavelet transform and Gray-Level Co-Occurrence Matrix (GLCM) obtained an accuracy value of 100% on MIAS and UDIAT Database in classifying the presence of masses in the mammogram image and obtained an accuracy value of 93.8% for classifying normal, benign, and malignant. The proposed method has the potential to identify the presence of masses in the mammogram image as a decision support system to the radiologist.

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Deep‐learning‐based projection‐domain breast thickness estimation for shape‐prior iterative image reconstruction in digital breast tomosynthesis

et al. 2022

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Background: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and inplane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation. Purpose: In this study, we propose a deep-learning-based methodology for projection-domain breast thickness estimation and demonstrate a shape-prior iterative DBT image reconstruction. Methods: We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin-line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the Xray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel-wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique. Results:The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root-mean-squared error (1.976%) and high structural similarity (99.997%) indicated a good agreement between the network-generated thickness corrected image and the ground truth image. Compared to the existing methods and simple single-input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape prior for the iterative DBT reconstruction. Conclusions:We presented a novel deep-learning-based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation.

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Breast peripheral area correction in digital mammograms

Cited by 15 publications

References 43 publications

Breast image pre-processing for mammographic tissue segmentation

Breast image pre-processing for mammographic tissue segmentation

Feature Extraction Optimization with Combination 2D-Discrete Wavelet Transform and Gray Level Co-Occurrence Matrix for Classifying Normal and Abnormal Breast Tumors

Deep‐learning‐based projection‐domain breast thickness estimation for shape‐prior iterative image reconstruction in digital breast tomosynthesis

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