A study on the effect of morphological filters on computer-aided medical image diagnosis

Noriyasu Homma, Yuko Kawai, Satoshi Shimoyama, Tadashi Ishibashi, Makoto Yoshizawa

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


We have developed several morphological image filters that can be useful for computer-aided medical image diagnosis. Several computer-aided diagnosis (CAD) systems for lung cancer and breast cancer have been developed to assist the radiologist's diagnostic work. The CAD systems for lung cancer can automatically detect pathological changes (pulmonary nodules) with a high true-positive rate (TP) even under low false-positive rate (FP) conditions. On the other hand, the conventional CAD systems for breast cancer can automatically detect some pathological changes (calcifications and masses), but the TP for other changes, such as architectural distortion, is still very low. Motivated by the radiologist's cognitive processes to increase TP for breast cancer, we propose new methods to extract novel morphological features from X-ray mammography. Simulation results demonstrate the effectiveness of the morphological methods for detecting tumor shadows.

Original languageEnglish
Pages (from-to)191-194
Number of pages4
JournalArtificial Life and Robotics
Issue number2
Publication statusPublished - 2009 Nov


  • Computer-aided diagnosis
  • Morphological filters
  • X-ray mammography


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