Effect of denoising on the localization of microcalcification clusters in digital mammography

Borges, Lucas R.; Caron, Renato F.; Azevedo-Marques, Paulo Mazzoncini de; Vieira, Marcelo A. C.

doi:10.1117/12.2564316

Cited by 6 publications

(5 citation statements)

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“…Hence, services can be replicated to meet workload demand and also provide significant flexibility in the integration and reuse. 12,13 The first step in this work was to transform the proposed methods for simulating radiation dose reduction in digital mammography 10 and the method for artificially inserting breast microcalcifications 11 into services to compose the application architecture. All technologies used in this project are open-source, thus facilitating system maintenance and cost elimination.…”

Section: Methodsmentioning

confidence: 99%

“…This is achievable through the application of the method for simulating dose reduction in digital mammography 10 and the method of artificial insertion of clustered microcalcifications into mammographic images without any pre-existing lesions. 11 To the best of our knowledge, there is no mammographic image database with these characteristics available for scientific use. This application will contribute to diverse research initiatives in the medical field, facilitating the validation of image processing methods and optimizing the relationship between radiation dose and image quality.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Customizable digital mammography database: on-demand generation with user-defined radiation dose and microcalcification cluster characteristics

Zanelato,

Exposto,

Brandão

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

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Several clinical image databases are currently available to support scientific research in the medical field. These images are generally used to validate studies based on measuring the sensitivity and specificity of a particular clinical task. In the case of digital mammography, the radiation dose directly influences the quality of the image and consequently the performance of radiologists. Therefore, it is important to conduct studies to find a balance between image quality and radiation dose. Image processing methods are typically employed to optimize this relationship. For the evaluation of these methods, it is crucial to have a mammographic image database with specific characteristics, currently unavailable for scientific use. For example, this image database should contain sets of images from the same patient acquired at different radiation doses with breast lesions in known locations. This is achievable using computational methods for noise and microcalcification insertion into pre-acquired clinical images. In this context, the present work aims to present a cloud-based application for on-demand generation of a clinical mammographic image database with different radiation doses and breast lesions. From a set of pre-acquired clinical digital mammograms, it is possible to create N databases with different characteristics. This technique can also be considered as data augmentation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Customizable digital mammography database: on-demand generation with user-defined radiation dose and microcalcification cluster characteristics

Zanelato,

Exposto,

Brandão

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

View full text Add to dashboard Cite

show abstract

“…Tissue density analysis was evaluated using the LIBRA software. 12 Among these ROIs, one was chosen for the artificial insertion of the microcalcification cluster using a method proposed by our research group in previous works, 13,14 while the other three ROIs remained lesion-free. The same procedure was applied for all microcalcification sizes.…”

Section: Image Datasetmentioning

confidence: 99%

Evaluating an image restoration pipeline for digital mammography across varied radiation exposures and microcalcification sizes using model observer analysis

Brandão,

Exposto Soares,

Rodrigues Borges

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

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In this study, we assess the impact of an image restoration pipeline, designed for digital mammography, on the detectability of microcalcifications of different sizes across varied radiation exposures. The restoration pipeline first removes the noise of the image considering a Poisson-Gaussian noise model that incorporates quantum and electronic noise. Then, it appropriately merges the noisy and denoised images to achieve a signal-to-noise ratio (SNR) comparable to an image obtained at a higher radiation dose. We created a database of mammographic images acquired at radiation doses between 50% and 200% of the automatic exposure control (AEC) using a physical anthropomorphic breast phantom. Clustered microcalcifications with diameters ranging from 190𝜇m to 390𝜇m were artificially inserted into the phantom images in regions with increased density. The Channelized Hotelling Observer (CHO) was employed as the model observer (MO) to evaluate the detectability of microcalcifications. A pilot study was conducted to adjust the percentage of correct detection to approximately 75% for microcalcifications with a diameter of 270𝜇m at the AEC dose. We applied the restoration pipeline to the image dataset and calculated the percentage of correctly detected signals (PC) using the MO in a four-alternative forced choice (4-AFC) study. The results indicated a PC enhancement of up to 10% when applying restoration to simulate acquisitions with twice the AEC dose. Additionally, for images acquired with radiation doses below the AEC, our results demonstrated a potential dose reduction of up to 22.4% without compromising microcalcification detectability. The detection of microcalcifications with a diameter of 390𝜇m remained unaffected by variations in radiation dose.

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“…In Yu et al, 111 CLAHE is used to enhance the image contrast while it was repurposed as the data augmentation method. In another work, 112 Borges et al explored the effect of denoising on the localization of breast microcalcification. In another research work, 113 a framework for mammogram image denoising was proposed.…”

Section: Pipelines Before Deep Learningmentioning

confidence: 99%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers.Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

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Effect of denoising on the localization of microcalcification clusters in digital mammography

Cited by 6 publications

References 0 publications

Customizable digital mammography database: on-demand generation with user-defined radiation dose and microcalcification cluster characteristics

Customizable digital mammography database: on-demand generation with user-defined radiation dose and microcalcification cluster characteristics

Evaluating an image restoration pipeline for digital mammography across varied radiation exposures and microcalcification sizes using model observer analysis

A systematic survey of deep learning in breast cancer

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