Abstract

BackgroundDigital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading.MethodsFirstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system.ResultsThe results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85.ConclusionsCompared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Highlights

  • Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis

  • The most effective method for early detection of breast cancer is mammography [1,2], and micro-calcifications are important sign of early-stage breast cancer, which are tiny deposits of calcium that appear on the mammograms as bright spots

  • Dataset and Regions of interest (ROI) selection The mammograms used in our experiments were selected from the Mammography Image Analysis Society (MIAS) and a clinical dataset of mammograms from the Capital Medical University affiliated hospital

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Summary

Introduction

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. A computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. As the causes are unknown, early diagnosis and treatment is important in reducing the mortality rate. The most effective method for early detection of breast cancer is mammography [1,2], and micro-calcifications are important sign of early-stage breast cancer, which are tiny deposits of calcium that appear on the mammograms as bright spots. With the development of computer technology, a computeraided diagnosis system comes into being, which can be useful for the radiologists as a second reader. Balleyguier et al [3] showed that the use of CAD is more useful for the junior radiologist with an improvement in sensitivity from 61.9% to 84.6% compared to a slight improvement from 76.9% to 84.6% for the experienced radiologist

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