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Browsing by Author "Huang, Wenhan"
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Item Image Processing Techniques for Bone Cell Analysis(Office of the Vice Chancellor for Research, 2014-04-11) Huang, Wenhan; Qian, EnlinOsteoblast and osteoclast are two different types of bone cell that are responsible for bone formation and bone resorption, respectively. Both cell types are very critical in maintaining, repairing, and remodeling of the skeleton in the human body. Moreover, they are involved in skeletal diseases such as osteoporosis and osteoarthritis. To absorb bone matrix, pre-osteoclasts infuse into one large multinucleated mature osteoclast. The area of the large multinucleated cell is measured to represent the formation and the activity of mature osteoclast cells. The number of osteoblast cells is a key factor that determines the rate of bone formation. Thus, the area of mature osteoclast and the number of osteoblast are two critical parameters to decide the effect of a stimulus on bone remodeling. In order to automatically obtain the number of osteoblast cells and the area of the osteoclast cells from bright field images, an image analysis technique, implemented in OpenCV, was developed. After cells are stained and photographed, edge maps of the acquired images are obtained using edge detection techniques such as the Canny edge detector. The scheme requires a threshold value from the user and employs it to determine an initial edge map, that is displayed to the user. If the user is not satisfied with the outcome they can request the threshold value to be adjusted and new edge map is consequently obtained. If the edge maps are satisfactory, they are subsequently converted into segmentation masks. The purpose of this step is to eliminate noise in the background while retaining objects/cells of interest. Once the cells have been identified the technique employs the Hough Circle Transform to identify and count the number of osteoblast cells present in the image. For the osteoclast cells, the scheme permits the user to manually select specific cells in order to determine their size as a ratio of the total image size.Item Parallelized Ray Casting Volume Rendering and 3D Segmentation with Combinatorial Map(2016-04-27) Huang, Wenhan; Salama, Paul; Rizkalla, Maher; Christopher, Lauren Ann; Dunn, Kenneth W.; King, BrianRapid development of digital technology has enabled the real-time volume rendering of scientific data, in particular large microscopy data sets. In general, volume rendering techniques project 3D discrete datasets onto 2D image planes, with the generated views being transparent and having designated color that is not necessarily "real" color. Volume rendering techniques initially require designating a processing method that assigns different colors and transparency coefficients to different regions. Then based on the "viewer" and the dataset "location," the method will determine the final imaging effect. Current popular techniques include ray casting, splatting, shear warp, and texture-based volume rendering. Of particular interest is ray casting as it permits the display of objects interior to a dataset as well as render complex objects such as skeleton and muscle. However, ray casting requires large memory and suffers from longer processing time. One way to address this is to parallelize its implementation on programmable graphic processing hardware. This thesis proposes a GPU based ray casting algorithm that can render a 3D volume in real-time application. In addition, to implementing volume rendering techniques on programmable graphic processing hardware to decrease execution times, 3D image segmentation techniques can also be utilized to increase execution speeds. In 3D image segmentation, the dataset is partitioned into smaller sized regions based on specific properties. By using a 3D segmentation method in volume rendering applications, users can extract individual objects from within the 3D dataset for rendering and further analysis. This thesis proposes a 3D segmentation algorithm with combinatorial map that can be parallelized on graphic processing units.