Breast border extraction and pectoral muscle removal in MLO mammogram images

Majeed, Taban F.; Al-Jawad, Naseer; Sellahewa, Harin

doi:10.1109/ceec.2013.6659457

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…As a well-known texture-based feature, Grey Level Co-occurrence Matrix (GLCM) has been used in many studies that relate to mammography classifications [15] [16]. In GLCM, the matrix is computed based on pixel neighbours to reflect the frequency of the occurrence of certain patterns, where the pattern can be identified in different distance and angles.…”

Section: Feature Extractionmentioning

confidence: 99%

Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding

Han¹,

Du²,

Jassim³

2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Gaussian Bayes classifiers are widely used in machine learning for various purposes. Its special characteristic has provided a great capacity for estimating the likelihood and reliability of individual classification decision made, which has been used in many areas such as decision support assessments and risk analysis. However, Gaussian Bayes models tend to perform poorly when processing feature vectors of high dimensionality. This limitation is often resolved using dimension reduction techniques such as Principal Component Analysis. Conventional approaches on reducing dimensionalities usually rely on using a simple threshold based on accuracy measurements or sampling characteristics but rarely consider the sensitivity aspect of the prediction model created. In this paper, we have investigated the influence of eigenvalue selections on Gaussian Bayes classifiers in the context of sensitivity adjustment. Experiments based on real-life data have shown indicative and intriguing results.

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Section: Feature Extractionmentioning

confidence: 99%

Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding

Han¹,

Du²,

Jassim³

2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

show abstract

“…Computer-aided diagnosis (CAD) is widely used to assist radiologists in detecting and identifying breast masses [62,63] and [64]. The adoption of computer-aided diagnosis reduces the number of misclassifications and increases accuracy and time management [65]. Computer-aided diagnosis is considered a second reading for mammograms and has been reported to be more efficient than traditional methods [16].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Michael

et al. 2021

BioMed Research International

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Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.

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Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique

Vikhe¹,

Thool²

2016

J Med Syst

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Detection of mass in mammogram for early diagnosis of breast cancer is a significant assignment in the reduction of the mortality rate. However, in some cases, screening of mass is difficult task for radiologist, due to variation in contrast, fuzzy edges and noisy mammograms. Masses and micro-calcifications are the distinctive signs for diagnosis of breast cancer. This paper presents, a method for mass enhancement using piecewise linear operator in combination with wavelet processing from mammographic images. The method includes, artifact suppression and pectoral muscle removal based on morphological operations. Finally, mass segmentation for detection using adaptive threshold technique is carried out to separate the mass from background. The proposed method has been tested on 130 (45 + 85) images with 90.9 and 91 % True Positive Fraction (TPF) at 2.35 and 2.1 average False Positive Per Image(FP/I) from two different databases, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). The obtained results show that, the proposed technique gives improved diagnosis in the early breast cancer detection.

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Breast border extraction and pectoral muscle removal in MLO mammogram images

Cited by 6 publications

References 18 publications

Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding

Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding

Breast Cancer Segmentation Methods: Current Status and Future Potentials

Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique

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