• Journal of Biomedical Engineering Research
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• v.21 no.2
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• pp.137-144
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• 2000

Clustered microcalcifications in X-ray mammograms are an important sign for the diagnosis of breast cancer. A shape-based method, which is based on the morphological features of clustered microcalcifications, is proposed for classifying clustered microcalcifications into benign or malignant categories. To verify the effectiveness of the proposed shape features, clinical mammograms were used to compare the classification performance of the proposed shape features with those of conventional textural features, such as the spatial gray-leve dependence method and the wavelet-based method. Image features extracted from these methods were used as inputs to a three-layer backpropagation neural network classifier. The classification performance of features extracted by each method was studied by using receiver operating-characteristics analysis. The proposed shape features were shown to be superior to the conventional textural features with respect to classification accuracy.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.1-8
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• 1998

Clustered microcalcifications on X-ray mammograms are an important sign for early detection of breast cancer. This paper proposes a computer-aided diagnosis method for the detection of clustered microcalcifications and marking their locations on digitized mammograms. The proposed detection method consists of the region of interest (ROI) selection, the film-artifact removal, the surrounding texture analysis method for the detection of clustered microcalcifications, which is based on the second-order histogram in two nested surrounding regions on the current pixel. This paper also describes the effectiveness of the proposed film-artifact removal filter in terms of the classification performance with the receiver operating-characteristics(ROC) analysis. A three-layer backpropagation neural network is employed as a classifier. The appropriate marking for the locations of clustered microcalcifications can be used to alert radiologists to locations of suspicious lesions.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.319-322
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• 1997

This paper presents a CAD (Computer-Aided Diagnosis) system or detection of breast cancer, which is composed of personal computer, X-ray film scanner, high resolution display and application softwares. There are three major algorithms implemented in the application software. The irst algorithm is the adaptive enhancement of the digitized X-ray mammograms based on the first derivative and the local statistics. The second one is to detect the clustered microcalcifications by using the statistical texture analysis, and the third one is the classification of the clustered microcalcifications as malignant or benign by using the shape analysis. These algorithms were verified by real experiments.

• Journal of the Korean Society of Radiology
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• v.84 no.3
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• pp.676-685
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• 2023

Purpose To investigate the incidence, outcomes, and imaging characteristics of clustered microcysts detected on breast US in asymptomatic women, and suggest appropriate management guidelines. Materials and Methods We identified and reviewed the lesions recorded as "clustered microcysts" on breast US performed in asymptomatic women between August 2014 and December 2019. The final diagnosis was based on pathology and imaging follow-up results for at least 12 months. Results The incidence was 1.5% and 100 patients with 117 lesions were included. Among 117 lesions, 3 (2.6%), 2 (1.7%), and 112 (95.7%) were malignant, high-risk benign, and benign lesions, respectively. The malignant lesions included two cases of ductal carcinoma in situ and one invasive ductal carcinoma. Two of them were assessed as category 4, showing mammographic suspicious microcalcifications and internal vascularity on Doppler US. The remainder was a false negative case and showed echo pattern change on the 12-month follow-up US. Conclusion The incidence of clustered microcysts on breast US in asymptomatic women was 1.5% and malignancy rate was 2.6% (3 of 117). Knowledge of outcomes and imaging features of benign and malignant clustered microcysts may be helpful for radiologists, thereby aiding categorization and management recommendations.