Three-dimensional light field imaging in laparoscopic surgery is an emerging technology, which has the potential to enable three-dimensional imaging. Calibration of the three-dimensional light field endoscope (3D LFE) is essential but challenging as the disparity is much smaller than that of the conventional light field camera. The geometrical model for 3D LFE is established, and a calibration method based on virtual objective lens and virtual feature points is proposed. First, the virtual objective lens is introduced and the parameters about it are calibrated using corner features in center subaperture images. Second, two types of virtual feature points are proposed to calibrate the parameters about the microlens array, one is on the black-and-white board line and the other is selectively determined but can be anywhere on the checkerboard. Moreover, the relationship between the virtual feature points mapping in the microlens image and the virtual feature points mapping in the central subaperture image is deduced to overcome tiny light field disparity. Experimental results verify the performance of our calibration method.
In this paper, a three-dimensional (3D) shape measurement method based on structured light field imaging is proposed. Generally, light field imaging is challenging to accomplish the 3D shape measurement accurately, as the slope estimation method based on radiance consistency is inaccurate. Taking into account the special modulation of structured light field, the phase information is derived with Fourier transform profilometry, which is utilized to substitute the phase consistency for the radiance consistency in epipolar image (EPI) at first. Therefore, the 3D coordinates are derived after light field calibration, but the results are coarse due to slope estimation error and need to be corrected. Furthermore, the 3D coordinates refinement is performed based on relationship between the structured light field image and DMD image of the projector, which allows to improve the performance of the 3D shape measurement. The necessary light field camera calibration is described to generalize its application. Subsequently, the effectiveness of the proposed method is demonstrated with a sculpture and compared to the results of a conventional PMP system.
Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.