A neural network method for mammogram analysis ]based on statistical features

Mini, M. G.; Thomas, Tessamma

doi:10.1109/tencon.2003.1273167

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Heine et al [11] used multi-resolution statistical analysis to identify normal mammograms. Wavelet transform accompanied with a simple linear marking were also used to subtract normal tissue from the mammogram in [18]. Later, the idea of characterizing the normal mammograms began to get more attention.…”

Section: Related Workmentioning

confidence: 99%

Pre-CAD system for normal mammogram detection using local binary pattern features

Elshinawy

Abdelmageed

Badawy

et al. 2010

2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS)

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Breast cancer is the second leading cause of cancer deaths in women in the U.S. Two main problems appear to affect the decision of detecting and diagnosing breast cancer: the accuracy of the CAD systems used, and the radiologists' performance in reading mammograms. We aim here to improve CAD system's performance by adding a preprocessing step based on the density of the breast to reduce the false negative rate significantly. Mammograms are divided into two distinct categories according to breast density (fatty, and dense). Three LBP-basedfeatures are extracted for each of dense and fatty mammograms. A oneclass classifier is used for each tissue-type separately to enhance the performance of the overall classification task. The sensitivity for each tissue type was improved significantly when used separately compared to the sensitivity of existing systems that uses all mammograms regardless of tissue type.

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Section: Related Workmentioning

confidence: 99%

Pre-CAD system for normal mammogram detection using local binary pattern features

Elshinawy

Abdelmageed

Badawy

et al. 2010

2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS)

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show abstract

“…[14] used multi-resolution statistical analysis to identify normal mammograms. Wavelet transform accompanied with a simple linear marking were also used to subtract normal tissue from the mammogram in [15]. Later, the idea of characterizing the normal mammograms began to get more attention.…”

Section: Related Workmentioning

confidence: 99%

Detection of Normal Mammograms Based on Breast Tissue Density Using GLCM Features.

Elshinawy¹,

Badawy²,

Abdelmageed

et al. 2010

Biomedical Engineering

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Breast cancer is the second leading cause of cancer-related deaths in women in the US. Two main problems appear to affect the decision of detecting and diagnosing breast cancer: the accuracy of the CAD systems used, and the radiologists' performance in reading and diagnosing mammograms. In this work we aim to improve CAD system's performance by adding a preprocessing step to reduce the false negative rate signi cantly. We propose to divide mammograms into two distinct categories according to tissue type (fatty, and dense). A one-class classi er is used for each tissue-type separately to enhance the performance of the overall classi cation task. GLCM features are extracted for each of dense and fatty mammograms. The sensitivity for each tissue type was improved signi cantly ( 100%) when used separately compared to the sensitivity of existing systems (90%) that uses all mammograms regardless of tissue type.

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“…These methods are usually composed of a wide range of combination of fuzzy logic, wavelet transformation or that of the neural network [2][3][4][5][6]. Since the mammograms show larger areas of varying contrast and brightness, thus the information is highly susceptible to being correlated [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Transformation for Enhancing Mammographic Images

Rai

2017

Asian J Pharm Clin Res

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Objective: Mammographic images are often prone to noises and consequently make the task of radiologist to come up with the precise diagnosis. Although there are several denoising techniques for the same is available, while denoising they often suffer from the problem of eliminating the micron level details in the noise influenced images. It's a trade-off which prohibits efficient micro-classification of mammary tissues. Methods:In this study, we present a solution for the same by utilizing multi-level wavelet transformation to enable preservation of micron level details in the images. Results and Conclusion:The quality denoising without elimination of the features of the mammographic imagery data by MWTA will allow the medical practitioner to easily identify and consequently diagnose properly to cancer influenced patients.

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A neural network method for mammogram analysis ]based on statistical features

Cited by 14 publications

References 9 publications

Pre-CAD system for normal mammogram detection using local binary pattern features

Pre-CAD system for normal mammogram detection using local binary pattern features

Detection of Normal Mammograms Based on Breast Tissue Density Using GLCM Features.

Wavelet Transformation for Enhancing Mammographic Images

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