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Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter

Langarizadeh M, Mahmud R, Ramli AR, Napis S, Beikzadeh MR, Rahman WE

J Med Eng Technol, 2011 Feb;35(2):103-8.
PMID: 21204610 DOI: 10.3109/03091902.2010.542271

Breast cancer is one of the most important diseases in females worldwide. According to the Malaysian Oncological Society, about 4% of women who are 40 years old and above are involved have breast cancer. Masses and microcalcifications are two important signs of breast cancer diagnosis on mammography. Enhancement techniques, i.e. histogram equalization, histogram stretching and median filters, were used to provide better visualization for radiologists in order to help early detection of breast abnormalities. In this research 60 digital mammogram images which includes 20 normal and 40 confirmed diagnosed cancerous cases were selected and manipulated using the mentioned techniques. The original and manipulated images were scored by three expert radiologists. Results showed that the selected methods have a positive significant effect on image quality.

Matched MeSH terms: Mammography/methods*
Impact of age and breast thickness on mean glandular dose of standard digital mammography and digital breast tomosynthesis

Norsuddin NM, Mei Sin JG, Ravintaran R, Arasaratnam S, Abdul Karim MK

Appl Radiat Isot, 2023 Feb;192:110525.
PMID: 36436228 DOI: 10.1016/j.apradiso.2022.110525

This study compares the mean glandular dose (MGD) across 2D, 3D projection and Contrast-Enhanced Digital Mammography (CEDM) mammographic techniques. The important metadata were extracted from the digital mammography console. 650 subjects were clustered based on projections, age and CBT. The MGD of 2D, 3D, and CEDM was positively correlated with CBT but inversely correlated with the age factor. This study indicate MGD of CEDM was 16% and 22% lower compared to 2D and 3D techniques, respectively.

Matched MeSH terms: Mammography/methods
Assessment of male patients' average glandular dose during mammography procedure

Sulieman A, Salah H, Rabbaa M, Abuljoud M, Alkhorayef M, Tahir D, et al.

Appl Radiat Isot, 2023 Mar;193:110626.
PMID: 36640699 DOI: 10.1016/j.apradiso.2022.110626

Breast cancer is a common malignancy for females (25% of female cancers) and also has low incidence in males. It was estimated that 1% of all breast malignancies occur in males with mortality rate about 20%, with annual increase in incidence. Risk factors include age, family history, exposure to ionizing radiation and high estrogen and low of androgens hormones level. Diagnosis and screening are challenging due to limiting effectiveness of breast cancer screening. Therefore, patients may expose to ionizing radiation that may contribute in breast cancer incidence in males. In literature, limited studies were published regarding radiation exposure for males during mammography. The objective of this research is to quantify patient doses during male mammogram and to estimate the projected radiogenic risk during the procedure. In total, 42 male patients were undergone mammogram for breast cancer diagnosis during two consecutive years. The mean and range of patient age (years) is 45 (23-80). The mean and standard deviation (SD) of the peak tube potential and tube current time product are 28.64 ± 2. and 149 ± 35.1, respectively. The mean, and range of patients' entrance surface air kerma (ESAK, mGy) per single breast procedure was 5.3 (0.47-27.5). Male patient's received comparable radiation dose per mammogram compared to female procedures. With increasing incidence of male breast cancer, proper guidelines are necessary for the mammographic procedure are necessary to reduce unnecessary radiation doses and radiogenic risk.

Matched MeSH terms: Mammography/methods
Positron emission mammography is a useful adjunct in assessment of dense breasts

Kumar SK, Trujillo PB, Ucros GR

Med J Malaysia, 2017 04;72(2):138-140.
PMID: 28473683

Worldwide breast cancer remains as the most common malignancy in women and the numbers who form a subgroup with dense breast parenchyma are substantial. In addition to mammography, the adjuncts used for further evaluation of dense breasts have been anatomically based modalities such as ultrasound and magnetic resonance imaging. The practice of functionally based imaging of breasts is relatively new but has undergone rapid progress over the past few years with promising results. The value of positron emission mammography is demonstrated in patients with dense breasts and mammographically occult disease.

Matched MeSH terms: Mammography/methods*
Fulltext Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Rahman H, Naik Bukht TF, Ahmad R, Almadhor A, Javed AR

Comput Intell Neurosci, 2023;2023:7717712.
PMID: 36909966 DOI: 10.1155/2023/7717712

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

Matched MeSH terms: Mammography/methods
Standardisation of clinical breast-density measurement

Ng KH, Yip CH, Taib NA

Lancet Oncol, 2012 Apr;13(4):334-6.
PMID: 22469115 DOI: 10.1016/S1470-2045(12)70093-1
Matched MeSH terms: Mammography/methods*
Estimation of breast radiation dose in a mammographic system

Lee KH, Kandaiya S

Appl Radiat Isot, 1996 Mar;47(3):361-3.
PMID: 8935969
Matched MeSH terms: Mammography/methods*
Fulltext Evaluation of automated volumetric breast density software in comparison with visual assessments in an Asian population: A retrospective observational study

Rahmat K, Ab Mumin N, Ramli Hamid MT, Fadzli F, Ng WL, Muhammad Gowdh NF

Medicine (Baltimore), 2020 Sep 25;99(39):e22405.
PMID: 32991467 DOI: 10.1097/MD.0000000000022405

This study aims to compare Quantra, as an automated volumetric breast density (Vbd) tool, with visual assessment according to ACR BI-RADS density categories and to determine its potential usage in clinical practice.Five hundred randomly selected screening and diagnostic mammograms were included in this retrospective study. Three radiologists independently assigned qualitative ACR BI-RADS density categories to the mammograms. Quantra automatically calculates the volumetric density data into the system. The readers were blinded to the Quantra and other readers assessment. Inter-reader agreement and agreement between Quantra and each reader were tested. Region under the curve (ROC) analysis was performed to obtain the cut-off value to separate dense from a non-dense breast. Results with P value

Matched MeSH terms: Mammography/methods*
Volumetric breast density measurement: sensitivity analysis of a relative physics approach

Lau S, Ng KH, Abdul Aziz YF

Br J Radiol, 2016 Oct;89(1066):20160258.
PMID: 27452264 DOI: 10.1259/bjr.20160258

OBJECTIVE: To investigate the sensitivity and robustness of a volumetric breast density (VBD) measurement system to errors in the imaging physics parameters including compressed breast thickness (CBT), tube voltage (kVp), filter thickness, tube current-exposure time product (mAs), detector gain, detector offset and image noise.
METHODS: 3317 raw digital mammograms were processed with Volpara(®) (Matakina Technology Ltd, Wellington, New Zealand) to obtain fibroglandular tissue volume (FGV), breast volume (BV) and VBD. Errors in parameters including CBT, kVp, filter thickness and mAs were simulated by varying them in the Digital Imaging and Communications in Medicine (DICOM) tags of the images up to ±10% of the original values. Errors in detector gain and offset were simulated by varying them in the Volpara configuration file up to ±10% from their default values. For image noise, Gaussian noise was generated and introduced into the original images.
RESULTS: Errors in filter thickness, mAs, detector gain and offset had limited effects on FGV, BV and VBD. Significant effects in VBD were observed when CBT, kVp, detector offset and image noise were varied (p

Matched MeSH terms: Mammography/methods*
A comparative study of computed radiography-based mammography using digital phosphor storage plate and full field digital mammography

Chelliah KK, Tamanang S, Bt Elias LS, Ying KY

Indian J Med Sci, 2013 11 2;67(1-2):23-8.
PMID: 24178338

BACKGROUND: Two digital mammography systems, based on different physical concepts, have been introduced in the last few years namely the full-field digital mammography (FFDM) system and computed radiography-based mammography using digital storage phosphor plate (DSPM).
AIMS: The objective of this study was to compare the image quality for DSPM and FFDM using a grading scale based on previously published articles.
MATERIALS AND METHODS: This comparative diagnostic study was done for 5-month duration at the Breast Clinic. The system used was the Lorad Selenia FFDM system and the Mammomat 3000 Nova DSPM system. The craniocaudal and mediolateral oblique projections were done on both breast on 58 asymptomatic women using both DSPM and FFDM. The mammograms were evaluated for eight criteria of image quality: Tissue coverage, compression, exposure, contrast, resolution, noise, artifact, and sharpness by two independent radiologists.
STATISTICAL ANALYSIS: Wilcoxon Signed Rank Test and Weighted Kappa.
RESULTS: FFDM was rated significantly better (P < 0.05) for five aspects: Tissue coverage, compression, contrast, exposure, and resolution and equal to DSPM for sharpness, noise, and artifact.
CONCLUSION: FFDM was superior in five aspects and equal to DSPM for three aspects of image quality.

Matched MeSH terms: Mammography/methods*
Fulltext Moving into the digital era: a novel experience with the first full-field digital mammography system in Malaysia

Ranganathan S, Faridah Y, Ng KH

Singapore Med J, 2007 Sep;48(9):804-7.
PMID: 17728959

Breast cancer is the commonest cancer in women and represents a significant problem from the clinical and public health perspectives. The aim of this paper is to report our experience of transitioning from screen-film mammography (SFM) to computed radiography mammography (CRM), and finally to full-field digital mammography (FFDM), and to evaluate the performance of these three different types of mammographic systems.

Matched MeSH terms: Mammography/methods*
Causal relationships between breast cancer risk factors based on mammographic features

Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, et al.

Breast Cancer Res, 2023 Oct 25;25(1):127.
PMID: 37880807 DOI: 10.1186/s13058-023-01733-1

BACKGROUND: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology.
METHODS: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.
RESULTS: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P

Matched MeSH terms: Mammography/methods
Vision 20/20: Mammographic breast density and its clinical applications

Ng KH, Lau S

Med Phys, 2015 Dec;42(12):7059-77.
PMID: 26632060 DOI: 10.1118/1.4935141

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

Matched MeSH terms: Mammography/methods*
Validation of electrical impedance tomography qualitative and quantitative values and comparison of the numeric pain distress score against mammography

Juliana N, Shahar S, Chelliah KK, Ghazali AR, Osman F, Sahar MA

Asian Pac J Cancer Prev, 2014;15(14):5759-65.
PMID: 25081698

Electrical impedance tomography (EIT) is a potential supplement for mammogram screening. This study aimed to evaluate and feasibility of EIT as opposed to mammography and to determine pain perception with both imaging methods. Women undergoing screening mammography at the Radiology Department of National University of Malaysia Medical Centre were randomly selected for EIT imaging. All women were requested to give a pain score after each imaging session. Two independent raters were chosen to define the image findings of EIT. A total of 164 women in the age range from 40 to 65-year-old participated and were divided into two groups; normal and abnormal. EIT sensitivity and specificity for rater 1 were 69.4% and 63.3, whereas for rater 2 they were 55.3% and 57.0% respectively. The reliability for each rater ranged between good to very good (p<0.05). Quantitative values of EIT showed there were significant differences in all values between groups (ANCOVA, p<0.05). Interestingly, EIT scored a median pain score of 1.51±0.75 whereas mammography scored 4.15±0.87 (Mann Whitney U test, p<0.05). From these quantitative values, EIT has the potential as a health discriminating index. Its ability to replace image findings from mammography needs further investigation.

Matched MeSH terms: Mammography/methods*
Fulltext Is there an influence of dietary habits on breast density as seen on digital mammograms?

Voon NS, Chelliah KK

Asian Pac J Cancer Prev, 2011;12(8):1969-72.
PMID: 22292635

The purpose of this study was to evaluate the influence of dietary habit on breast density, which is an important risk factor for breast cancer. This cross-sectional study was performed on 64 Malaysian women of all races between the age of 35 to 70 years. All subjects underwent mammography and the breast density was analyzed from the images using BI-RADS by two independent radiologists. A validated food-frequency questionnaire was used to evaluate the nutrient intake. The data were analyzed using Chi-square test to evaluate the association of dietary habits to breast density. Based on the results, mutton, pork, vegetables, sweets, snacks, soy bean and eggs intake showed associations with increased breast density (p < 0.05) while grains, meat, beverages, oil and fruits, did not show any association (p > 0.05). As a conclusion, this study showed diet may make changes to the breast density as a risk factor for breast cancer.

Matched MeSH terms: Mammography/methods
Using autoencoders for mammogram compression

Tan CC, Eswaran C

J Med Syst, 2011 Feb;35(1):49-58.
PMID: 20703586 DOI: 10.1007/s10916-009-9340-3

This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious and difficult if the whole image is used for training. We show in this paper that the autoencoders can be trained successfully by using image patches instead of the whole image. The compression performances of different types of autoencoders are compared based on two parameters, namely mean square error and structural similarity index. It is found from the experimental results that the autoencoder which does not use Restricted Boltzmann Machine pre-training yields better results than those which use this pre-training method.

Matched MeSH terms: Mammography/methods*
Optimal beam quality selection based on contrast-to-noise ratio and mean glandular dose in digital mammography

Aminah M, Ng KH, Abdullah BJ, Jamal N

Australas Phys Eng Sci Med, 2010 Dec;33(4):329-34.
PMID: 20938762 DOI: 10.1007/s13246-010-0035-3

The performance of a digital mammography system (Siemens Mammomat Novation) using different target/filter combinations and tube voltage has been assessed. The objective of this study is to optimize beam quality selection based on contrast-to-noise ratio (CNR) and mean glandular dose (MGD). Three composition of breast were studied with composition of glandular/adipose of 30/70, 50/50, and 70/30. CNR was measured using 2, 4 and 6 cm-thick simulated breast phantoms with an aluminium sheet of 0.1 mm thickness placed on top of the phantom. Three target/filter combinations, namely molybdenum/molybdenum (Mo/Mo), molybdenum/rhodium (Mo/Rh) and tungsten/rhodium (W/Rh) with various tube voltage and mAs were tested. MGD was measured for each exposure. For 50/50 breast composition, Mo/Rh combination with tube voltage 26 kVp is optimal for 2 cm-thick breast. W/Rh combination with tube voltage 27 and 28 kVp are optimal for 4 and 6 cm-thick breast, respectively. For both 30/70 and 70/30 breast composition, W/Rh combination is optimal with tube voltage 25, 26 and 27 kVp, respectively. From our study it was shown that there are potential of dose reduction up to 11% for a set CNR of 3.0 by using beam quality other than that are determined by AEC selection. Under the constraint of lowest MGD, for a particular breast composition, calcification detection is optimized by using a softer X-ray beam for thin breast and harder X-ray beam for thick breast. These experimental results also indicate that for breast with high fibroglandular tissues (70/30), the use of higher beam quality does not always increase calcification detection due to additional structured noise caused by the fibroglandular tissues itself.

Matched MeSH terms: Mammography/methods*
Breast cancer diagnosis in digital mammogram using multiscale curvelet transform

Eltoukhy MM, Faye I, Samir BB

Comput Med Imaging Graph, 2010 Jun;34(4):269-76.
PMID: 20004076 DOI: 10.1016/j.compmedimag.2009.11.002

This paper presents an approach for breast cancer diagnosis in digital mammogram using curvelet transform. After decomposing the mammogram images in curvelet basis, a special set of the biggest coefficients is extracted as feature vector. The Euclidean distance is then used to construct a supervised classifier. The experimental results gave a 98.59% classification accuracy rate, which indicate that curvelet transformation is a promising tool for analysis and classification of digital mammograms.

Matched MeSH terms: Mammography/methods*
Mammography screening: Female doctors'attitudes and practice in Sana'a, Yemen

Al-Naggar RA, Isa ZM, Shah SA, Chen R, Kadir SY

Asian Pac J Cancer Prev, 2009;10(5):743-6.
PMID: 20104962

A cross-sectional study was conducted at the main hospitals in Sana'a, Yemen to determine the attitude and practice of Yemen female doctors on mammography screening. Study subjects were all female doctors who were on duty during the questionnaire distribution. Those who agreed to participate were given the questionnaire to complete. Descriptive statistics were used to analyse socio-demographic variables and variables related to general health. Participants in this study were 105 female doctors with mean age of 32.1 years (SD = 7.17). Thirty-four respondents (36.6%) did not send asymptomatic women for mammography screening. The reasons were because of high cost (58.0%, n= 25), availability of other methods (23.3%, n= 10), instrument not available (11.6%, n= 5) and high risk of radiation (7.0%, n= 3). Twenty-five participants (26.9%) sent patients on regular basis if there was a family or personal history of breast cancer. Twenty-three participants (24.7%) sent the patients for mammogram screening every year regardless of the patients'history or symptoms. Although most doctors (36.5%) do not refer patients for mammography screening, seventy-seven (74.0%) indicated that they would refer patients for mammography screening on personal request by the patients. This study showed a low percentage of doctors who referred patients for routine mammography. The major reason given was the high cost of the procedure.

Matched MeSH terms: Mammography/methods
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

Jalalian A, Mashohor SB, Mahmud HR, Saripan MI, Ramli AR, Karasfi B

Clin Imaging, 2013 May-Jun;37(3):420-6.
PMID: 23153689 DOI: 10.1016/j.clinimag.2012.09.024

Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.

Matched MeSH terms: Mammography/methods*

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