3. Architecture

Abstract: Techniques developed in visualization facilitate multiple data-stream presentation and navigation through huge data sets. These techniques are particularly useful for improving insight into complex biomedical phenomena, which are naturally multidimensional. In this poster we present a novel framework for high-resolution, high throughput image data visualization with possible applications in biomedical image analysis research, particularly for microscopic imagery. Kolam is a software package for the interactive visualization of massive datasets and 2D imagery of varying resolutions. It is platform and operating system independent, and supports embedded datasets scalable from hundreds of gigabytes to terabytes in size on desktop computers without any specialized hardware . In addition to extremely rapid roam, zoom and hyper-jump spatial operations, Kolam supports an arbitrary number of simultaneously visible embedded layers, on-the-fly colormap lookup and histogram enhancement, projection of images onto a spherical surface and elevation maps. Kolam also provides a robust framework for the animation of image sequences. A feature-rich tracking interface supports bioanalytics for measuring cellular and sub-cellular motion in brightfield, phase-contrast and fluorescent microscopy imagery, manual trajectory drawing and cell lineage analysis.

An Open Framework for Interactive Visualization of High Resolution, High Throughput Imagery

Anoop Haridas1, Joshua Fraser1, Kannappan Palaniappan1

1 Department of Computer Science, University of Missouri-Columbia, Columbia MO 65211-2060, USA

4. Biological Applications

5. Conclusions

Kolam is a cross-platform interactive visualization tool that supports extremely large datasets hundreds of gigabytes in size and larger. Kolam has smooth roam, zoom, trajectory drawing, animation and tracking capabilities that have been extended from the geospatial to the biomedical domain. Kolam runs on Mac OS X, Windows and Linux platforms and has been successfully tested on a wide range of microscopy imagery.

1. Motivation 2. Features

Kolam provides a highly flexible and extensible framework for visualization. Its main features are the following:• Interactive display of massive datasets.• Use of layers for data display.• Overlays for user drawing/annotation.• Terrain visualization.• Animation of time – varying data.• Sphere mapping.• Lossless compression.• Efficient memory usage.• Robust animation and tracking capability.• Platform for algorithm testing and verification.• Cross – platform compatibility.

Figure 2. The Kolam user interface showing the main display window and several Qt dialog widgets.

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Kolam is a custom designed research tool for interactive visualization and analysis of massively large image and volume datasets. Kolam was originally developed to visualize time-varying geospatial imagery. Using Kolam's extensible design architecture we have added functionality to visualize high-throughput biomedical imagery data, particularly time-varying microscopy imagery. Annotation operations supported by Kolam include fast manual object tracking at the cellular and sub-cellular level, generating ground-truth trajectories for hundreds of objects and cell lineages.

Figure 1. Example of hi-res microscopy data (17630 x 21977 pixels, 1.16 GB). The foreground high-resolution view is a zoomed view of the green ROI marked in the low resolution image. Histopathology imagery courtesy of Michael Feldman, Dept. of Surgical Pathology, Univ. of Pennsylvania.

(a) Data Organization: Kolam uses a Scale and Tile Method to handle large image data. The data is segmented into spatially coherent pieces and then provided at multiple resolutions.

• Only displayed regions need be resident in memory.• Updates are incremental.• Tile – based disc access.• Concurrency for overlapping read/decompress/display operations.• Tile – based lossless compression.• Caching (for primary and video memory). Figure 3. Scale and Tile approach.

(b) Performance: Multithreading is used for maintaining an interactive display.

Figure 4. Multithreading architecture in Kolam.

(c) Class Hierarchy:

Data represented as layers.A layer may contain an image or a sequence.Overlays (for user drawing) ‘sit on top’ of layers.

Kolam is currently being used for animation, annotation and ground truth generation of several microscopy datasets. Two representative examples are given below. Work is currently being done to manually generate and visualize cell lineages in mitosis image sequences.

Figure 5: Manual track generation of cell movement in genetically modified human HeLa Kyoto cell lines. The track for cell 1 is not visible as it has been turned off in the tracking interface. Tracks created are automatically saved.

For time lapse analysis, images were acquired with a Zeiss LSM 510 Meta laser scanning confocal microscope using the 488 nm laser line of an Argon ion laser at low power every 15 minutes. The image sequence has 174 1024×1024 frames.

HeLa imagery courtesy of M.C. Cardoso, Technische Universitaet Darmstadt, Darmstadt Germany.

Figure 6: Use of Kolam’s manual tracking capability for ground truth generation, from a dataset generated by LSDCAS (Large Scale Digital Cell Analysis System). Noteworthy Kolam features include functionally rich animation tool, interactive tracking of biological objects of interest, customizing trajectory appearance such as color and labels.

Imagery courtesy of M. A. Mackey, Departments of Biomedical Eng. and Pathology, College of Medicine, University of Iowa.