The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:301076 |
| Date | January 1999 |
| Creators | Al-Hinnawi, Abdel-Razzak |
| Publisher | University of Aberdeen |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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