Medical image recognition and analysis can provide quantitative evidence for medical diagnosis

while medical image segmentation is the first and significant step in this procedure. A novel feature distance based thresholding associated with color feature classification for ophthalmologic image segmentation is presented

and the segmented results can be used for the analysis of blood vessel region distribution on these images. Feature distance based thresholding can be widely used for segmenting the objects with any kinds of feature space structure

especially those with uncompact support

blurred boundary and uneven quantity

while Euclidean distance based method is only suitable for the sphere shaped feature space. Ophthalmologic image segmentation does not satisfy the conditions of Euclidean distance based method. In addition

a blocking algorithm based on 3D histogram to determine typical samples is proposed in this paper. Lots of experiments show that the algorithm in this paper can be effectively used in ophthalmologic image segmentation and good segmentation results have got. And the analysis results from segmented images meet the requirements of medical diagnosis.