Automated Methods in Clustered Microcalcifications Detection Module of a Cad System

Diyana, W. Mimi; Besar, Rosli

doi:10.1142/s0219519403000752

Cited by 3 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Breast microcalcification (MC) is an indicator of potential carcinoma; it has been the only detectable sign of nonpalpable breast cancer during screening (25–43% clinically; 62–98% ductal carcinoma in situ (DCIS)) [ 6 ]. MCs are small glandular bright calcium deposits that typically range between 0.01 and 0.1 mm in size, and appear as a collection of regionalized high-intensity areas on radiology film [ 7 ]. Clinical studies indicate that there are 5 or more MC points per centimeter [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Mahmood

Pei

et al. 2021

Cancers

View full text Add to dashboard Cite

Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

show abstract

Section: Introductionmentioning

confidence: 99%

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Mahmood

Pei

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

“…Microcalcifications are minute granular bright spots of calcium deposits (from 0.01 mm 2 to 0.1 mm 2 ) (Diyana and Besar, 2003). They appear in radiology film as a group of localised high-intensity regions (spots) (Panda et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Microcalcifications segmentation from mammograms for breast cancer detection

Hadjidj

Amel

Belgherbi

et al. 2019

IJBET

View full text Add to dashboard Cite

The presence of microcalcifications (MCs) in X-ray mammograms provides an important early sign of women breast cancer. However, their detection still remains very complex due to the diversity in shape, size, their distributions and to the low contrast between the cancerous areas and surrounding bright structures in mammograms. This paper presents an effective approach based on mathematical morphology for detection of MCs in digitised mammograms. The developed approach performs an initial step in order to extract the breast area and removing unwanted artefacts out of the mammogram. Subsequently, an enhancement process is applied to improve appearance and increase the contrast of images and to eliminate noise. Once the breast region has been found, a segmentation phase through morphological watershed is performed in order to detect MCs. The performance of our approach is evaluated using a total of 22 mammograms extracted from the MIAS mammographic database, showing the presence of MCs. The obtained results were compared with manual detection, marked by an expert mammographic radiologist. These results show that the system is very effective, especially in terms of sensitivity.

show abstract

Breast calcification detection based on multichannel radiofrequency signals via a unified deep learning framework

Qiao

Fang

Guo

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

Automated Methods in Clustered Microcalcifications Detection Module of a Cad System

Cited by 3 publications

References 3 publications

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Microcalcifications segmentation from mammograms for breast cancer detection

Breast calcification detection based on multichannel radiofrequency signals via a unified deep learning framework

Contact Info

Product

Resources

About