Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Saifullah Harith Suradi ◽  
Kamarul Amin Abdullah
Keyword(s):  
Breast Cancer ◽  
Image Quality ◽  
Image Enhancement ◽  
Cancer Detection ◽  
Mean Squared Error ◽  
Survival Rates ◽  
Curvelet Transform ◽  
Breast Cancer Detection ◽  
Online Databases ◽  
Fast Discrete Curvelet Transform

Background: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection. Methods: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values. Results: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively. Conclusion: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

Potential Use of Biomarkers to Augment Clinical Decisions for the Early Detection of Breast Cancer

2014 ◽  
Vol 10 (02) ◽  
pp. 103 ◽  
Author(s):  
Alan B Hollingsworth ◽  
David E Reese ◽  
◽  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Survival Rates ◽  
High Sensitivity ◽  
Standard Of Care ◽  
Serum Biomarkers ◽  
Breast Cancer Detection ◽  
Breast Cancers

Breast cancer remains a significant worldwide health problem, despite the fact that early detection is associated with excellent survival rates. Currently, a substantial proportion of breast cancers are not detected using routine screening. Therefore, there is a need to identify a technology that can improve the precision and accuracy of early breast cancer detection. Biomarkers are attractive in that they can potentially detect early cancers with high sensitivity, while distinguishing between benign disease and invasive cancers. Many commonly used serum biomarkers have limited use in screening assays for breast cancer as single agents due to the heterogeneous nature of breast cancer. However, the use of protein panels that detect multiple serum biomarkers offer the potential for enhanced sensitivity and specificity in a clinical setting. Recently, a serum biomarker test comprising five serum biomarkers for breast cancer was clinically validated and showed high sensitivity and specificity. Additional panels have been developed that combine serum protein biomarkers (SPB) and tumor-associated autoantibodies (TAb) to further enhance the clinical utility of the assay. Serum biomarkers are currently not the standard of care and are not recommended in any detection guidelines. However, tumor biomarkers are used in the breast cancer setting to determine the course of care. The purpose of this article is to review recent advances in SPB, TAb, and biomarkers used in breast cancer detection to provide a perspective on how these technologies may offer benefit when combined with current imaging modalities.

scholarly journals Study of Microwave Tomography Measurement Setup Configurations for Breast Cancer Detection Based on Breast Compression

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Alvaro Diaz-Bolado ◽  
Paul-Andre Barriere ◽  
Jean-Jacques Laurin
Keyword(s):  
Breast Cancer ◽  
Image Quality ◽  
Cancer Detection ◽  
Synthetic Data ◽  
Singular Value ◽  
Breast Cancer Detection ◽  
Single Frequency ◽  
Microwave Tomography ◽  
Breast Compression ◽  
Value Decomposition

Microwave tomography (MT) measurement setups for different configurations based on breast compression are compared to classical circular measurement setups. Configurations based on compression allow measuring the evanescent component of the scattered field and lead to a compact measurement setup that allows direct image comparison with a standard mammography system. The different configurations are compared based on the singular value decomposition (SVD) of the radiation operator for a 2D TM case. This analysis allows determining under which conditions the image quality obtained from the reconstructions can be enhanced. These findings are confirmed by a series of reconstructions of breast phantoms based on synthetic data obtained at a single frequency of operation.

scholarly journals Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI

2020 ◽  
Vol 31 (1) ◽  
pp. 356-367
Author(s):  
Isaac Daimiel Naranjo ◽  
Roberto Lo Gullo ◽  
Carolina Saccarelli ◽  
Sunitha B. Thakur ◽  
Almir Bitencourt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Quality ◽  
Cancer Detection ◽  
Multiparametric Mri ◽  
Breast Cancer Detection ◽  
Dense Breast ◽  
B Values ◽  
Dce Mri ◽  
Adc Values ◽  
Lesion Conspicuity

Abstract Objectives To assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values. Methods Eighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2–5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm2 images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1–3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1–5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI. Results Sixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200–1500 s/mm2 were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm2 values allowed the visualization of 84–90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm2. DWI was more specific (86.3% vs 65.7%, p < 0.001) but less sensitive (62.8% vs 90%, p < 0.001) and accurate (71% vs 80.7%, p = 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases. Conclusion The addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite. Key Points • The addition of synthetic b-values (1200–1500 s/mm2) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts. • Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection. • A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

scholarly journals Thermogram Breast Cancer Detection: A Comparative Study of Two Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 551 ◽  
Author(s):  
Fayez AlFayez ◽  
Mohamed W. Abo El-Soud ◽  
Tarek Gaber
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Survival Rates ◽  
Histogram Equalization ◽  
Breast Cancer Detection ◽  
Machine Learning Techniques ◽  
World Health ◽  
The World ◽  
Health Organization

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%.

Comparison of various breast cancer detection methods with survival rates

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 17033-17033
Author(s):  
S. Misra ◽  
S. Tarr ◽  
D. Pratt
Keyword(s):  
Breast Cancer ◽  
Survival Time ◽  
Cancer Detection ◽  
Survival Rates ◽  
Staging System ◽  
Breast Cancer Detection ◽  
Detection Methods ◽  
Breast Cancer Patients ◽  
Estrogen Receptor Positivity

17033 Background: The role of mammography (MG) but not of breast self-exam (BSE) and clinical breast exam (CBE) in breast cancer detection and survival is well documented. This study compares the different methods of breast cancer detection and subsequent survival rates, analyzing the differences even within the same stage of the disease. Methods: Retrospective review of 1,259 patients was done using the hospital Tumor Registry data. Only patients with stage I, IIA and IIB are included and were diagnosed between April 1992 to December 2005 with follow up ranging from June 1993 to August 2006. The detection methods studied include BSE, CBE, MG and ultrasonography (USG). Parametric tests were conducted. Results: Mean age of the sample was 62 years (range 24–96). There were 293 BSE, 64 CBE, 885 MG, 17 USG detected breast cancer patients. Mean size of mass at presentation was 19 mm (range 1–110). Mean survival time for patients detected with breast cancer till August 2001 was 76 months (range 1–163). 67% patients survived 5 years or more and 12% survived more than 10 years. Mean survival for BSE/CBE and MG/USG group was 43 and 57 months respectively. This difference in survival is significant p< .05; The average survival time by stages I, IIA, IIB for BSE was 47, 45, 38 months, for CBE it was 43, 39, 51 months, for MG it was 57, 59, 50 months and for USG group it was 52, 47, 95 months respectively. Even within the same stage, the method of detection affected survival with the BSE and CBE group having less survival rates (Tukey Test mean difference 0.54, 95% C.I 42–66 and 0.38, 95% C.I 15–61) respectively than the MG group. Survival time also positively correlated with cancer recurrence (r =.7), family history (r = .06) and negatively correlated with age (r = -.09), size of tumor (r = -.09), estrogen receptor positivity status (r = -.06) all with (p < 0.05). We believe this study underestimates overall survival rate as the last follow up date was taken as an end point and also the survival rates are not disease specific survival. Conclusions: MG/USG group show higher survival rates compared to BSE/CBE across the early stages of breast cancer. Even within the same stage, the method of detection affects survival with MG/USG detected cases having more favorable outcomes. May be our current staging system for breast cancer is inadequate and needs revision. No significant financial relationships to disclose.

scholarly journals Breast cancer detection using image enhancement and segmentation algorithms

2018 ◽  
Vol 29 (20) ◽  
Author(s):  
Yousif MY Abdallah ◽  
Sami Elgak ◽  
Hosam Zain ◽  
Mohammed Rafiq ◽  
Elabbas A. Ebaid ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Enhancement ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Segmentation Algorithms
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