Evaluation of an automated wavelet-based system dedicated to the detection of clustered microcalcifications in digital mammograms

• Published in: Medical informatics and the internet in medicine 2001, Vol.26 (3), p.149-163
• Main Authors: LADO, Maria J, TAHOCES, Pablo G, MENDEZ, Arturo J, SOUTO, Miguel, VIDAL, Juan J
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Breast Neoplasms - diagnostic imaging; Calcinosis - diagnostic imaging; Computer-AIDED Diagnosis (CAD) Digital Mammography Mammographic Screening Microcalcifications; Diagnosis, Computer-Assisted - instrumentation; Diagnosis, Computer-Assisted - methods; False Positive Reactions; Female; Genital system. Mammary gland; Gynecology. Andrology. Obstetrics; Humans; Investigative techniques, diagnostic techniques (general aspects); Mammary gland diseases; Mammography - methods; Medical sciences; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Radiographic Image Enhancement; ROC Curve; Sensitivity and Specificity; Spain; Tumors
• ISSN: 1463-9238; 1464-5238
• DOI: 10.1080/14639230110062480

Mammographic screening programs are delivering reductions in breast cancer mortality. However, breast cancer screening will be cost effective and will provide a real profit only when both high sensitivity and specificity levels are reached. To date, due to human or technical factors, a significant number of breast cancers are still missed or misinterpreted on the mammograms. Computer methodologies, developed to assist radiologists, could represent further amelioration by increasing diagnostic accuracy in the screening programs. We have tested a computerized scheme to detect clustered microcalcifications in digital mammograms, employing 360 mammograms that were randomly selected from the mammographic screening program, currently undergoing at the Galicia Community (Spain). After the digitization process, the breast border was initially determined. A wavelet-based algorithm was employed to detect the clusters of microcalcifications. The performance of the automated system over the test set was evaluated employing Free-response Receiver Operating Characteristic (FROC) methodology. The sensitivity achieved was 74% at a false positive detection rate of 1.83. The corresponding area under the Alternative FROC (AFROC) curve was A 1 =0.667 &#45 0.09.