Automated detection of masses in mammograms by local adaptive thresholding

Kom, Guillaume; Tiedeu, Alain; Kom, Martin

doi:10.1016/j.compbiomed.2005.12.004

Cited by 114 publications

(50 citation statements)

References 11 publications

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“…This approach is proposed by Kom et al [15]. The corresponding algorithm is based on the thresholding mammographic image obtained by subtracting from the mammogram a linear filtered representation of itself.…”

Section: Thresholding Approachmentioning

confidence: 99%

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LBPV descriptors-based automatic ACR/BIRADS classification approach

et al. 2013

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Mammogram tissue density has been found to be a strong indicator for breast cancer risk. Efforts in computer vision of breast parenchymal pattern have been made in order to improve the diagnostic accuracy by radiologists. Motivated by recent results in mammogram tissue density classification, a novel methodology for automatic American College of Radiology Breast Imaging Reporting and Data System classification using local binary pattern variance descriptor is presented in this article. The proposed approach characterizes the local density in different types of breast tissue patterns information into the LBP histogram. The performance of macro-calcification detection methods is developed using FARABI database. Performance results are given in terms of receiver operating characteristic. The area under curve of the corresponding approach has been found to be 79%.

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Section: Thresholding Approachmentioning

confidence: 99%

“…To be able to evaluate the effectiveness of the training, one can measure the relative error as follows: 15) where I N is the image resulting from ANN output and I T is the target. After training steps, generalization error was evaluated for different features and network conditions.…”

Section: Breast Tissue Density Classification Module With Annmentioning

confidence: 99%

LBPV descriptors-based automatic ACR/BIRADS classification approach

et al. 2013

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“…Deshpande et al [8] present mammogram classification using texture based association rule mining but result of classification obtained in malignant type stage is only 84%. Kom et al [9] present an algorithm for suspicious masses identification in mammogram but, result of mass detection obtain sensitivity is 95.91% Eltonsy et al [10] present a technique for the automatic detection of malignant masses in mammogram while screening but it only examine on malignant type stage. L. F. A. Campos et al [11], present A discrimination and classification method for mammographic image with benign, malignant and normal tissues implementing independent component analysis and multilayer neural networks.…”

Section: Review Workmentioning

confidence: 99%

Review on diagnosis cancerous tissue using image data mining

2016

IJLTET

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“…Kom et al [18] used local adaptive thresholding technique to segment the suspicious regions. A threshold value is calculated according to the neighborhood of the corresponding pixel and based on this threshold value, abnormal regions are segmented.…”

Section: Introductionmentioning

confidence: 99%

Detection of Microcalcification Clusters Using Hessian Matrix and Foveal Segmentation Method on Multiscale Analysis in Digital Mammograms

Thangaraju¹,

Vennila

Chinnasamy³

2012

J Digit Imaging

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Mammography is the most efficient technique for detecting and diagnosing breast cancer. Clusters of microcalcifications have been mainly targeted as a reliable early sign of breast cancer and their earliest detection is essential to reduce the probability of mortality rate. Since the size of microcalcifications is very tiny and may be overlooked by the observing radiologist, we have developed a Computer Aided Diagnosis system for automatic and accurate cluster detection. A three-phased novel approach is presented in this paper. Firstly, regions of interest that corresponds to microcalcifications are identified. This can be achieved by analyzing the bandpass coefficients of the mammogram image. The suspicious regions are passed to the second phase, in which the nodular structured microcalcifications are detected based on eigenvalues of second order partial derivatives of the image and microcalcification pixels are segmented out by exploiting the foveal segmentation in multiscale analysis. Finally, by combining the responses coming out from the second order partial derivatives and the foveal method, potential microcalcifications are detected.The detection performance of the proposed method has been evaluated by using 370 mammograms. The detection method has a TP ratio of 97.76 % with 0.68 false positives per image. We have examined the performance of our computerized scheme using free-response operating characteristics curve.

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Automated detection of masses in mammograms by local adaptive thresholding

Cited by 114 publications

References 11 publications

LBPV descriptors-based automatic ACR/BIRADS classification approach

LBPV descriptors-based automatic ACR/BIRADS classification approach

Review on diagnosis cancerous tissue using image data mining

Detection of Microcalcification Clusters Using Hessian Matrix and Foveal Segmentation Method on Multiscale Analysis in Digital Mammograms

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