Dong-Hyeop Han

Fast and accurate retinal vasculature tracing and kernel-Isomap-based feature selection

The blood vessels in the retina have a characteristic radiating pattern, while there exists a significant variation dependent on the individual and/or medical condition. Extracting the geometric properties of these blood vessels have several important applications, such as biometrics (for identification) and medical diagnosis. In this paper, we will focus on biometric applications. For this, we propose a fast and accurate algorithm for tracing the blood vessels, and compare several candidate summary features based on the tracing results. Existing tracing algorithms based on a detailed analysis of the image can be too slow to quickly process a large volume of retinal images in real time (e.g., at a security check point). In order to select good features that can be extracted from the traces, we used kernel Isomap to test the distance between different retinal images as projected onto their respective feature spaces. We tested the following feature set: (1) angle among branches, (2) the number of fiber based on distance, (3) distance between branches, and (4) inner product among branches. Our results indicate that features 3 and 4 are prime candidates for use in fast, realtime biometric tasks. We expect our method to lead to fast and accurate biometric systems based on retinal images.

Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope

The common octopus, or Octopus vulgaris, has the largest nervous system of any invertebrate, and has been shown to possess learning and memory capabilities that in many ways rival those of some vertebrates [1]. Nevertheless, the neural architecture of this cephalopod mollusk differs markedly from that of any vertebrate. Investigating the differences and similarities between the neural architecture—or connectome—of the octopus and mammals, such as the mouse, may lead to deep insights into the computational principles underlying animal cognition. The octopus brain provides some unique advantages for anatomical research, since its axons are generally thick and unmyelinated, allowing traditional staining methods, such as Golgi, to be used effectively. With this in mind, we first imaged the brain using the Knife-Edge Scanning Microscope [2], a custom serial sectioning microscope that can image large blocks of tissue (1 cm3) at sub-micrometer resolution. We imaged large portions of the octopus subesophageal mass (SUB) and the optic lobe (OL) which were stained using Golgi. In order to extract the geometry of the neuronal morphology, we used our Maximum Intensity Projection (MIP)-based tracing algorithm [3]. The imaging results are shown in Figure 1(a-d), and tracing results are shown in 1(e). Although quite preliminary, to our knowledge this is the first time large volumes of the octopus brain have been imaged at sub-micrometer resolution, allowing us to resolve many of the processes that make up the neural network. We expect that this pilot study and the more detailed investigations to follow will allow fruitful comparisons of the neural circuitries of individual octopuses with different ecological life histories, as well as of animals that have been exposed to a variety of neurodegenerative insults. Moreover, such explorations will engender a greater understanding of how functional neural architecture is altered by learning in invertebrates such as the octopus and vertebrates such as the mouse. In sum, this approach should contribute greatly to our understanding of the computational architecture of invertebrates and ultimately provide insights into the differences between invertebrate and vertebrate cognitive capabilities. Figure 1 Octopus subesophageal mass (SUB) and optic lobe (OL) imaged with the KESM (a–d), and tracing results (e). Scale (block width): (a) 1.44 mm, (b) 0.72 mm, (c) 1.44 mm, (d-f) 76.8 μm. Voxel resolution: 0.6 μm x 0.7 μm x 1.0 ...

A local maximum intensity projection tracing of vasculature in Knife-Edge Scanning Microscope volume data

A local maximum intensity projection (MIP) approach to the extraction of a 3D vascular network, acquired by the Knife-Edge Scanning Microscope (KESM), is presented. We build a local volume for local MIP processing at each tracing step in order to reduce the dimension of input data from 3D to 2D, which leads to a 65.22% reduction of computation time compared to 3D tracing method. The proposed method makes use of existing 2D tracing methods, extending them into a 3D tracing method. Our experimental results show that our approach can rapidly and accurately extract the medial axis of vascular data acquired by the KESM.

Biologically grounded synthetic cerebrovasculature models for validation of segmentation algorithms

We introduce a novel model-based generator that produces biologically grounded synthetic volumes of the cerebrovasculature. Our models are synthesized stochastically, according to the biological characteristics of venule arborescence in the human collateral sulcus. Each synthetic volume produced is individually unique, yet representative of this cerebral region. As the locations and characteristics of filaments embedded within our models is known, ground truth data is easily derived. Therefore, our synthetic volumes provide a feasible foundation for the model-based validation of vascular segmentation algorithms.