Automatic threshold selection using histogram quantization.

An automatic threshold selection method is proposed for biomedical image analysis based on a histogram coding scheme. We show that the threshold values can be determined based on the well-known Lloyd-Max scalar quantization rule, which is optimal in the sense of achieving minimum mean square error distortion. We derive an iterative self-organizing learning rule for determining the threshold levels which does not require any prior information about the his-togram, and hence is fully automatic. Experimental results show that this new approach is very simple and eecient to implement yet yields reliable estimates of the threshold levels and is robust with respect to noise eeect.