Automated breast profile segmentation for ROI detection using digital mammograms

Nagi, Jawad; Kareem, Sameem Abdul; Nagi, Farrukh; Ahmed, Syed Khaleel

doi:10.1109/iecbes.2010.5742205

Cited by 91 publications

(50 citation statements)

References 15 publications

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“…2(d) present output mammogram of contrast enhancement technique. Region growing algorithm is applied to segment pectoral muscles of input mammogram, in which seed is located inside the pectoral muscles [7]. Fig.…”

Section: Resultsmentioning

confidence: 99%

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GLCM based Improved Mammogram Classification using Associative Classifier

Deshmukh¹,

Bhosle²

2017

IJIGSP

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Abstract-Among women, 12% possibility of developing a breast cancer and 3.5% possibility of mortality due to this cause is reported [1]. Nowadays early detection of breast cancer became very important. Mammogram -a breast X-ray is used to investigate and diagnose breast cancer. In this paper, authors propose GLCM (Grey Level Co-occurrence Matrix) feature based improved mammogram classification using an associative classifier. Mining of association rules from mammogram dataset discovers frequently occurring patterns. It depends on user specified minimum confidence and support value. This dependency causes an increase in search space. The authors propose two-phase optimization procedure to overcome these limitations.The initial phase comprises feature optimization by adopting proposed PreARM (Pre-processing step for Association Rule Mining) method. The next phase comprises association rule optimization by adopting proposed ESAR (Extraction of Strong Association Rules) method to generate efficient, highly correlated and robust rules. Proposed associative classification method is substantiated by adapting authentic MIAS and DDSM mammogram database. The experimentation concedes 91% and 90% trimming of GLCM features and association rules by adopting PreARM and ESAR algorithms respectively. Using optimized association rules, the classification accuracies procured for MIAS and DDSM datasets are 92% and 94% respectively. Area under Receiver Operating Characteristic (ROC) curves obtained by proposed system for MIAS and DDSM datasets are 0.9656 and 0.9285 respectively. Results of GLCM based associative classifier are compared with GLCM based Random Forest (RF), an ensemble learning method. The experimental result shows that GLCM based associative classifier outperforms RF method with respect to accuracy and AUC, and it is a promising method for mammogram classification.

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Section: Resultsmentioning

confidence: 99%

“…In [7], process of automatic segmentation of breast image is carried out to detect its area of concern, with the help of region growing technique. Relation in feature discrimination power and feature vector dimension is described in [8].…”

Section: Introductionmentioning

confidence: 99%

GLCM based Improved Mammogram Classification using Associative Classifier

Deshmukh¹,

Bhosle²

2017

IJIGSP

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“…Chandrasekhar and Attikiouze [35] use all the images from the MIAS database, with the algorithm providing about 94% acceptable results. Others, like Ojala et al [36] make no mention of the extent of the testing, and the illustrated mammograms contain visible contours. Yet what constitutes an "acceptable" result differs significantly, and is often based on visual subjective opinion with very little quantitative endorsement.…”

Section: Discussionmentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

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“…Intensity-based approaches are based on the fact that the intensity range of a pectoral muscle region should be higher than the range of breast parenchyma. These approaches directly utilize the pixel intensities [2][3][4][5][6][7], image histograms [8][9][10], and image gradients [11], or they are applied to image gradients [12]. Additionally, there are also some studies that segment pectoral muscles in wavelet domain instead of spatial domain [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

“…Line-detection methods aim to determine the hypotenuse of this triangle. For this reason, straight line estimation [4,11,[16][17][18][19][20][21], Hough transform [14,22], and curve fitting [23] are used. The hypotenuse of the triangle pectoral muscle shows a curved structure rather than an exact line.…”

Section: Related Workmentioning

confidence: 99%

A novel multistage system for the detection and removal of pectoral muscles in mammograms

Esener¹,

Ergin²,

Yüksel³

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a novel multistage scheme for pectoral muscle removal from mammography images is proposed, and the performance of this system is verified using the publicly available Mammographic Image Analysis Society digital mammogram database. This database is composed of mediolateral oblique mammography images including three different tissue types (fatty, fatty-glandular, and dense-glandular) with three health status types (normal, benign cancer, and malignant cancer). In the implementation of the proposed system, a mammography image is first preprocessed by performing noise reduction background removal followed by artifact suppression processes. Then a presegmentation procedure is applied using region growing and line fitting is executed. Finally, pixels including pectoral muscle regions are removed from mammography images with an accuracy of 94.40%, sensitivity of 89.62%, and specificity of 99.99% after some postprocessing operations. Although the mean false positive rate obtained by the proposed approach is higher than that of other studies in the literature, not only the lower mean false negative rate but also the enhancement in the quality of pectoral muscle segmentation for all 322 images in the database evidently show the success of the proposed approach.

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Automated breast profile segmentation for ROI detection using digital mammograms

Cited by 91 publications

References 15 publications

GLCM based Improved Mammogram Classification using Associative Classifier

GLCM based Improved Mammogram Classification using Associative Classifier

Review on Mammogram Mass Detection by Machine Learning Techniques

A novel multistage system for the detection and removal of pectoral muscles in mammograms

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