| Summary: | 碩士 === 中原大學 === 醫學工程研究所 === 96 === Breast cancer has becoming one of the leading cancers to women in Taiwan. Micro-calcification is one of the early sign for breast cancer. The screen of micro-calcifications mainly depends on mammography. Currently, full-field digital mammography (FFDM) has taking the place of screen-film mammography (SFM) gradually and it is advantageous to image processing. The purpose of this study is to develop a computer-aided detection (CAD) system to identify micro-calcification clusters automatically on FFDM.
The CAD system includes six stages which included: preprocessing; image enhancement; segmentation of suspicious micro-calcifications; false-positive (FP) reduction of micro-calcifications; clustering of micro-calcifications; and FP reduction of micro-calcification clusters. At preprocessing stage, the inverted log transform function was used to transform the raw FFDM. Then, this study enhanced the image contrast by using wavelet transform and preserved the brightest areas of the enhanced image as the locations of suspicious micro-calcifications. At the stage of FP reduction of suspicious micro-calcifications, the first artificial neural network (ANN) was used to identify micro-calcifications with the morphology and textural features from each suspicious micro-calcification. After micro-calcifications clustering, the second ANN identification with the features from each cluster was used to reduce FP of micro-calcification clusters. A data set of 16 images was collected, of which 6 images were normal and other 10 images contained 20 clusters with 490 individual micro-calcifications. All micro-calcifications and clusters were marked by experienced radiologist.
For performance evaluation, the purpose CAD system can achieve a sensitivity of 100% with 1.7 FP clusters/image. The performance of the CAD system using the first ANN only and using second ANN only also compared, it was found that the CAD system can achieve a sensitivity of 100% with 5 and 5.1 FP clusters/image, respectively. Consequently, using the ANN twice to reduce FP of micro-calcifications and clusters was able to improve effectively the performance of this system.
An automated CAD system for clusters of micro-calcifications which uses the raw FFDM as input was developed. This system has the potential to detect micro-calcification clusters with an acceptable sensitivity and low false positives. The use of the CAD system as “second reader” is considered to be one of the promising approaches that may help radiologists improve the diagnostic efficiency.
|
|---|
