This paper describes a novel structural adaptation artificial immune network (SAAN) clustering algorithm for texture
segmentation. In the SAAN, a new immune antibody neighborhood and an adaptive learning coefficient are presented.
The model can adaptively map input data into the antibody output space, which has a better adaptive net structure.
Images are first partitioned into a set of regions by using the watershed segmentation. Then the nonsubsampled
contourlet texture features are extracted from each watershed region as the antigens of the SAAN. Finally the antibodies
clustering results of the SAAN are combined to yield a global clustering solution by the minimal spanning tree, which
need not a predefined number of clustering. The experimental results with various texture images illustrate the
effectiveness of the proposed novel segmentation algorithm.