Malignant and benign clustered microcalcifications: automated feature analysis and classification.

Jiang, Yu; Nishikawa, Robert M.; Wolverton, Dulcy; Metz, Charles E.; Giger, Maryellen L.; Schmidt, Robert A.; Vyborny, Carl J.; Doi, Kunio

doi:10.1148/radiology.198.3.8628853

Cited by 209 publications

(138 citation statements)

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“…For training the network, such curves were compared to those more used according to the literature ( [2], [3], [4], [5] and [7]) in order to select the most adequate characteristics to be used. The red curve present the "suspect" class and the blue curve present the "non-suspect" class.…”

Section: Methodsmentioning

confidence: 99%

“…By the digitized mammogram, features are extracted from the images, which characterize clusters of microcalcifications, so that they are classified according to shape, size, number of microcalcifications in a cluster, among other factors. Information can be obtained by mathematical morphology in image processing, for example [5].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Neural Networks (ANNs) are often used as classifiers in CAD schemes, with good results ( [2], [5] to [7]). Characteristics extracted from digitized mammograms are the input data for lesion classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classifier scheme for clustered microcalcifications in digitized mammograms by using Artificial Neural Networks

Patrocinio¹,

Schiabel²

2016

Anais Do 5. Congresso Brasileiro De Redes Neurais

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Computer-Aided Diagnosis (CAD) schemes have presented good results in aiding the early diagnosis of breast cancer. The detected signals classification demands multi-works investigations, since cytological characteristics concerning the mammographic findings have to be investigated in addition to computer techniques. Artificial neural networks (ANN) have been successfully used in CAD classifiers, with success in the classification in CAD. For example, the classification of clustered microcalcifications has been made from individual microcalcifications analysis. In this work, regarding characteristics determined only from the cluster itself, and discarding the characteristics analysis and extraction from individual microcalcifications, such a classification was made in two classes: "non-suspect" and "suspect". Dismissing microcalcifications individual features for the network input has allowed to eliminate procedures intended to separate each structure from the whole image. The classifier using ANN has shown the geometric descriptors efficiency for characterizing microcalcifications clusters as well as the influence of features extracted from images reports, as "age" and "density". The best data have shown 92% of correct results, with A z = 0.96.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classifier scheme for clustered microcalcifications in digitized mammograms by using Artificial Neural Networks

Patrocinio¹,

Schiabel²

2016

Anais Do 5. Congresso Brasileiro De Redes Neurais

View full text Add to dashboard Cite

show abstract

“…Analysis of microcalcifications is usually based on the radiologist's subjective judgment; this process is sometimes difficult as well as inaccurate, resulting in many unnecessary breast biopsies performed on benign calcification clusters. Several articles have described computerized methods that extract features of clustered microcalcifications to improve radiologists' performance in differentiating malignant from benign clustered microcalcifications [7][8][9]. To improve accuracy of identifying clustered microcalcification patterns through both computeraided feature extraction and classification methods, it is worth developing mathematically a model or method, by which radiologists can evaluate quantitatively the difference between benign clustered microcalcification and its malignant counterpart.…”

Section: Introductionmentioning

confidence: 99%

“…To improve accuracy of identifying clustered microcalcification patterns through both computeraided feature extraction and classification methods, it is worth developing mathematically a model or method, by which radiologists can evaluate quantitatively the difference between benign clustered microcalcification and its malignant counterpart. These methods include estimating the likelihood of malignancy by using an artificial neural network [7] or analyzing malignant and benign microcalcifications through various feature classifiers with morphologic and texture features [8]. Nakayama et al [9] developed a computerized method for distinguishing between five different types of histological classifications: invasive carcinomas, noninvasive carcinomas of the comedo type, noninvasive carcinomas of the noncomedo type, mastopathies, and fibroadenomas.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach

Shao¹,

Liu²,

Bie³

et al. 2011

J Digit Imaging

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In this study, we explore a mathematical model to characterize the clustered microcalcifications on mammograms for predicting the pathological classification and grading. Our database consists of both retrospective cases (78 cases) and prospective cases (31 cases) with pathologically diagnosed clusters of microcalcifications on mammograms. The microcalcifications were divided into four grades: grade 0, benign breast disease including mastopathies (n=12) and fibroadenomas (n=20); grade 1, well-differentiated infiltrating ductal carcinoma (n=12); grade 2, moderately differentiated infiltrating ductal carcinoma (n=38); grade 3, poorly differentiated infiltrating ductal carcinoma (n=27). A feature parameter, defined as the pattern form factor of microcalcification cluster θ by us, combines five computer-extracted image parameters of microcalcification clusters of those mammograms. In every case, only one imaging was selected for modeling analysis. A total of 109 imagings were adopted in current study. We find the existence of a positive relationship between the feature parameter θ and pathological grading G of microcalcifications in retrospective cases, which was expressed as G=6.438+1.186×Ln <θ>. The model above has been verified further by the prospective study with a comparative evaluation accuracy of approximately 77.42%. The binary predication simply for both benignancy and malignancy was also included using same but reshuffled data, and the receiver operating characteristic (ROC) analysis was performed with ROC value 0.74351∼0.79891. As one candidate for feature parameter in computer-aided diagnosis, the pattern form factor θ of clustered microcalcifications may be useful to predict the pathological grading and classification of microcalcification clusters on mammography in breast cancer.

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Performance analysis of EM, SVD, and SVM classifiers in classification of carcinogenic regions of medical images

Rajaguru

Bojan

2014

Int J Imaging Syst Tech

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In this article, the performance analysis of Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM) classifiers for classification of carcinogenic regions from various medical images is carried out. Cancer detection is one of the critical issues where excessive care needs to be taken for better diagnosis. Any classifier needs to detect the cancer with respect to the efficiency in time of detection and performance. Due to these, three classifiers are selected: Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM). EM classifier performs as the optimizer and SVD classifier performs as the dual class classifier. SVM classifier is used as both optimizer and classifier for multiclass classification procedure and for wide stage cancer detection procedures. The performance analysis of all the three classifiers are analyzed for a group of 100 cancer patients based on the benchmark parameter such as Performance Measures and Quality Metrics. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.

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Malignant and benign clustered microcalcifications: automated feature analysis and classification.

Abstract: Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.

Cited by 209 publications

References 0 publications

Classifier scheme for clustered microcalcifications in digitized mammograms by using Artificial Neural Networks

Classifier scheme for clustered microcalcifications in digitized mammograms by using Artificial Neural Networks

Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach

Performance analysis of EM, SVD, and SVM classifiers in classification of carcinogenic regions of medical images

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