A Filter-Based Approach Towards Automatic Detection of Microcalcification

Wu, Zhi Qing; Jiang, Jianmin; Peng, Yong Hong; Gulsrud, Thor Ole

doi:10.1007/11783237_57

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…The technology extracts local frequencies in the mammogram where one of the textures has low signal energy and the other texture has high, and its filter is optimised with respect to the Fisher criterion. Segmentation of the calcifications is completed by a simple thresholding based on the threshold described in [20] and segmented regions are clustered according to the characteristics of MCC -MCC usually takes up a small part of the whole mammogram and they tend to exist in several clusters. Some artefacts such as white & black spots and scratches, which have no meaningful relationships with their contexts, are removed by using the algorithms in [20].…”

Section: Experiments and Discussionmentioning

confidence: 99%

Effective features based on normal linear structures for detecting microcalcifications in mammograms

Jiang

Peng

2008

2008 19th International Conference on Pattern Recognition

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Many features have been proposed for the detection of microcalcification clusters (MCCs) or classification of benign/malignant MCCs. However, most of them were designed based on the characteristics of MCC. In this paper, 16 features, which have been commonly adopted in many applications, are examined and six new features based on the linear structure are proposed. To evaluate the effectiveness of these six features, 800 suspicious regions detected from 320 full-field mammograms are equally divided into two parts for training and testing respectively. Experiments demonstrate that the area under the receiver operating characteristic (ROC) is increased from 0.86 to 0.89 after the new features are added into the set of feature selection. In the best feature sequence selected by the sequential floating forward search (SFFS) algorithm, the new proposed features take up the half number of features in the sequence

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Section: Experiments and Discussionmentioning

confidence: 99%

Effective features based on normal linear structures for detecting microcalcifications in mammograms

Jiang

Peng

2008

2008 19th International Conference on Pattern Recognition

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“…Finally, adaptive thresholding is utilized as post-processing for further robustness. Relevant details can be found in Wu et al (2006).…”

Section: Data Setmentioning

confidence: 98%

“…To detect suspicious MCC regions, optimal filtering using texture measurements is employed (Wu et al, 2006(Wu et al, , 2008. Firstly, some preprocessing is applied to remove the influence of background and several artifacts like white/black spots and scratches.…”

Section: Data Setmentioning

confidence: 99%

Effective recognition of MCCs in mammograms using an improved neural classifier

Ren

Wang

Jiang

2011

Engineering Applications of Artificial Intelligence

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“…Suspicious MCC regions are detected through optimal filtering using texture measurements [45][46]. Firstly, some preprocessing is applied to remove the influence of background and several artefacts like white/black spots and scratches.…”

Section: A Data Collectionmentioning

confidence: 99%

“…Finally, adaptive thresholding is utilized as post-processing for further robustness. Relevant details can be found in [45].…”

Section: A Data Collectionmentioning

confidence: 99%

ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

Ren

2012

Knowledge-Based Systems

228

104

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Classification of microcalcification clusters from mammograms plays essential roles in computer-aided diagnosis for early detection of breast cancer, where support vector machine (SVM) and artificial neural network (ANN) are two commonly used techniques. Although some work suggest that SVM performs better than ANN, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve A z . This performance may become much worse when the training samples are imbalanced. As a result, a new strategy namely balanced learning with optimized decision making is proposed to enable effective learning from imbalanced samples, which is further employed to evaluate the performance of ANN and SVM in this context. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from both ANN and SVM has been significantly improved. Although ANN outperforms SVM when balanced learning is absent, the performance from the two classifiers becomes very comparable when both balanced learning and optimized decision making are employed. Consequently, an average improvement of more than 10% in the measurements of F1 score and Az measurement are achieved for the two classifiers. This has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.Index Terms-microcalification clusters (MCC), balanced learning, optimized decision making, neural network, support vector machine, mammography, computer-aided diagnosis.

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A Filter-Based Approach Towards Automatic Detection of Microcalcification

Cited by 5 publications

References 14 publications

Effective features based on normal linear structures for detecting microcalcifications in mammograms

Effective features based on normal linear structures for detecting microcalcifications in mammograms

Effective recognition of MCCs in mammograms using an improved neural classifier

ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

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