Wavelet-based autofocusing and unsupervised segmentation of microscopic images

This paper reports on the construction of two new focus measure operators M_WT¹ an M_WT² defined in the wavelet transform domain. M_WT² provides significantly better focus performance in depth resolution than previously reported spatial domain operators. M_WT¹ provides performance equivalent to that of the best spatial domain operator but has lower computational cost than M_WT². Both operators can be used with a wide variety of wavelet bases optimized for different applications. Selection of wavelet bases is studied based on their number of vanishing moments, size of support and symmetry. The depth resolution of these operators makes them an important cue in the segmentation of low depth-of-field microscopic images. An unsupervised segmentation technique based on graph partition is then introduced. It uses M_WT² together with proximity and image intensity as segmentation features. This segmentation method does not depend on the connection of local image features and remains robust under defocusing. Experimental results confirm the effectiveness of the proposed focus measures and the segmentation algorithm. These techniques are especially suitable for high resolution microscopic computer vision tasks in high precision micromanipulation and microassembly applications.