Clustered microcalcification screened from mammograms provides an early sign of breast cancer. Many impalpable in situ ductal carcinomas and minimal carcinomas can be identified by using X-ray mammography. Generally, microcalcifications are tiny clustered particles and probably smallest structures within the breast, which are difficult to detect. Therefore, microcalcifications are generally overlooked by physicians if they do not carefully screen the mammograms. Consequently, it may cause the delay of medical treatment. So far, X-ray mammography is the only effective screening procedure to detect breast cancer in early stage. However, due to the increasing incidence rates of breast cancer and public awareness, mammography has been also increasingly used by physicians for screening purpose. As a result, a large volume of mammograms will be required to be read by radiologists. Due to shortage of experienced physicians, this tremendous workload creates dilemma to maintain quality of medical diagnosis. This paper presents a computer-aided diagnostic system for detection of clustered microcalcifications, which can help physicians reduce errors in medical diagnosis while improving the quality of medical service. The proposed system includes three stages. The first stage extracts the breast region from a digital mammogram, and the second stage detects the suspicious area in the extracted breast region. Finally, the last stage segments microcalcifications from the suspicious area. In order to evaluate the designed system, a preliminary study was conducted using the public Nijmegen database provided by the Department of Radiology at the Nijmegen University Hospital, Netherlands. According to the different tolerance of Fractal. The experimental results show that the AZ of ROC distribution can achieve 0.96. When the database of TCVGH is used, three categories of mammograms (Obvious, Possibly neglected and Difficult to be identified) were studied according three radiologists' reports. In obvious cases, the rate of true positive could achieve as high as 98%. For all cases, the rate of true positive could also achieve 86%. The results of this paper will provide a further development of mass detection used in computer-aided diagnosis system.
|Number of pages||13|
|Journal||Chinese Journal of Radiology|
|Publication status||Published - 2002|
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging