Application of ant colony optimization for lymph node classification in ultrasound images

Ultrasound (US) imaging is more popular as a diagnostic tool than magnetic resonance imaging (MRI) and computerized tomography (CT) because it is inexpensive and easy to use. Most lymph nodes (LN) tend to have various internal echogenicities in the sonogram, which makes a definite diagnosis difficult. If the characteristic echogenicities for the major components of the lymph node can be identified, the interpretation of lymph sonography can be more accurate. In this paper, an ant colony optimization (ACO) algorithm is applied to select significant features from different ultrasound imaging systems for lymph node classification. The support vector machine (SVM) is employed to classify the lymph nodes into six categories. Experimental results show that the proposed approach achieve higher performance than those of other methods.