Soo-Min Song

Morphological change tracking of dendritic spines based on structural features.

Identification and tracking of dendritic spine morphology from two‐dimensional time‐lapsed images plays an important role in neurobiological research. Such analysis can enable us to derive a correlation between morphological characteristics and molecular mechanism of dendritic spine development and remodelling. Moreover, Neuronal morphology of hippocampal Cornu Ammonis 1 region is critical for understanding the Alzheimers disease. Therefore, we need to extract and trace the dendritic spines accurately for examining their development and remodelling processes, which are related to functions of hippocampal Cornu Ammonis 1. There are some problems to be solved in related researches. Noise due to the properties of optical microscopes makes it difficult to identify and trace dendritic spines accurately. To solve these problems, in this paper we present a local spine detection technique minimizing noise influence in two‐dimensional optical microscopy images. Also, we suggest an efficient mapping method for tracking the dynamics of dendritic spines to measure their morphological changes quantitatively. First, to utilize structural feature of spines, which are small protrusions of tree‐like dendrites, we extract the tips of each dendritic branch and use this position as an initial contour position for a deformable model‐based segmentation. We then use a geodesic active contour model to detect the spines accurately. Secondly, we apply an optical flow method, which takes into account both structure and movement of objects, to map every time‐series image frame. Proposed method provides accurate measurements of dendritic spine length, volume, shape classification for time‐lapse images of dendrites of hippocampal neurons. We compared the proposed spine detection algorithm with manual method performed by biologists and noncommercial software NeuronIQ. In particular, this method is able to segment dendritic spines better than existing methods with high sensitivity in adjacent spines and noisy images. Also the algorithm performs well compared to a human analyser.

Coregistration of small animal PET and autoradiography for in vivo-ex vivo comparison

Non-invasive imaging of small animal and its quantification techniques are needed to be evaluated by comparison with ex vivo image. To overcome the existing method, hand-operated comparison with the unaided eye observation, we present an algorithm that matches the target area in PET scanned in vivo with an autoradiography image acquired ex vivo. We applied a coregistration algorithm that uses voxel similarity to find the corresponding slices and to make quantitative measurements. Automatic matching of in vivo and ex vivo images is novel, and can provide better validation than manual matching techniques.

Cell morphology classification in phase contrast microscopy image reducing halo artifact

Since the morphology of tumor cells is a good indicator of their invasiveness, we used time-lapse phase-contrast microscopy to examine the morphology of tumor cells. This technique enables long-term observation of the activity of live cells without photobleaching and phototoxicity which is common in other fluorescence-labeled microscopy. However, it does have certain drawbacks in terms of imaging. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we examined cell morphology to classify tumor cells as malignant and benign.

Stitching of microscopic images for quantifying neuronal growth and spine plasticity

In neurobiology, morphological change of neuronal structures such as dendrites and spines is important for understanding of brain functions or neuro-degenerative diseases. Especially, morphological changes of branching patterns of dendrites and volumetric spine structure is related to cognitive functions such as experienced-based learning, attention, and memory. To quantify their morphologies, we use confocal microscopy images which enables us to observe cellular structure with high resolution and three-dimensionally. However, the image resolution and field of view of microscopy is inversely proportional to the field of view (FOV) we cannot capture the whole structure of dendrite at on image. Therefore we combine partially obtained several images into a large image using image stitching techniques. To fine the overlapping region of adjacent images we use Fourier transform based phase correlation method. Then, we applied intensity blending algorithm to remove uneven intensity distribution and seam artifact at image boundaries which is coming from optical characteristics of microscopy. Finally, based on the integrated image we measure the morphology of dendrites from the center of cell to end of each branch. And geometrical characteristics of spine such as area, location, perimeter, and roundness, etc. are also quantified. Proposed method is fully automatic and provides accurate analysis of both local and global structural variations of neuron.

Tumor detection from small animal PET using clustering based on intensity and connectivity

We present an efficient clustering method for detecting the tumor in positron emission tomography(PET) of the tumor bearing small animal. We used iterative threshold method to remove the background noise and then we applied two clustering procedures in order. The one is clustering method based on intensity to segment the tumor region and the other is clustering based on connectivity to remove false positive region from the segmented region. The tumor tissue looks bright in the image compared to surrounding normal tissue because of glucose uptake. Therefore, based on volume intensity, we divided all elements of the image into several clusters, the tumor, living bodies, background using improved fuzzy c-means clustering(FCM). Using FCM with the sorted initial mean of each cluster gets out of the wrong optimization and reduces the amount of time-consumed. However, not only the tumor tissue, but also the other organs like heart, bladder can also have high intensity value because of glucose metabolism. So in order to separate the tumor and false positive region, we applied geometric clustering based on connectivity. Proposed segmentation method can lead a robust analysis of the tumor growth with the aid of the quantitative measurements such the tumor size or volume.

Quantitative tracking of tumor cells in phase-contrast microscopy exploiting halo artifact pattern

Tumor cell morphology is closely related to its invasiveness characteristics and migratory behaviors. An invasive tumor cell has a highly irregular shape, whereas a spherical cell is non-metastatic. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use phase-contrast microscopy to analyze single cell morphology and to monitor its change because it enables observation of long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring, among others. Thus, we first applied a local filter to compensate for non-uniform illumination. Then, we used intensity distribution information to detect the cell boundary. In phase-contrast microscopy images, the cell normally appears as a dark region surrounded by a bright halo. As the halo artifact around the cell body is minimal and has an asymmetric diffusion pattern, we calculated the cross-sectional plane that intersected the center of each cell and was orthogonal to the first principal axis. Then, we extracted the dark cell region by level set. However, a dense population of cultured cells still rendered single-cell analysis difficult. Finally, we measured roundness and size to classify tumor cells into malignant and benign groups. We validated segmentation accuracy by comparing our findings with manually obtained results.

Segmentation of abdominal organs incorporating prior knowledge in small animal CT

For quantification of drugs delivery using small animals measuring biochemical changes in abdominal organs based on functional images is essential. However, in those images, the object boundaries are not clearly enough to locate its shape and position. And even though the structural information is compensated using image registration technique, delineation of organs is difficult and time-consuming. So we suggested an automatic procedure for delineation of organs in mouse PET image with the aid of atlas as a priori anatomical information. Prior information was given by voxel label number. CT used to construct an atlas is transformed to match mouse CT to be segmented. For each label corresponding voxels represent the same organ. Then, mouse CT-PET pairs should be aligned to identify organ area in PET. After all images are aligned and fused each other both structural and functional information can be observed simultaneously for several organs.

Atlas Guided Identification of Multiple Abdominal Organs in Small Animal PET

For quantification of drug’s delivery using small animals measuring biochemical changes in abdominal organs based on functional images is essential. However, in those images, the object boundaries are not clearly enough to locate its shape and position. And even though the structural information is compensated using image registration technique, delineation of organs is difficult and time-consuming. So we suggested an automatic procedure for delineation of organs in mouse PET image with the aid of atlas as a priori anatomical information. Prior information was given by voxel label number. CT used to construct an atlas is transformed to match mouse CT to be segmented. For each label corresponding voxels represent the same organ. Then, mouse CT-PET pairs should be aligned to identify organ area in PET. After all images are aligned and fused each other both structural and functional information can be observed simultaneously for several organs.

Reconstruction and navigation of carotid artery based on ultrasonic volume data

In this paper, we introduce new diagnosis tool to observe carotid artery based on ultrasonic volume data. The main components and applied algorithms of the developed diagnosis tool are explained. As one of main components, the semi-automatic segmentation method includes an effective speckle reducing filter and an automatic ROI tracking scheme. Furthermore, we present the reconstruction method that is effective for Y-typed carotid artery and the navigation path generation method that applies interpolation of medial points of ROI. To support the objective diagnosis, we provide the automatic measurement method of artery’s diameter. To show usefulness of the developed tool, we constructed 3D model for carotid artery of 34-year-old person and the diameter of carotid artery was automatically measured.