Visualization and Computer Graphics Lab International University Bremen Converting RGB Volume Data to Scalar Fields Tetyana Ivanovska and Lars Linsen School of Engineering and Science International University Bremen* WSCG January – 1 February 2007 * Jacobs University Bremen as of Spring 2007

Visualization and Computer Graphics Lab International University Bremen WSCG Existing scanning techniques CT, MRI, PET In vivoEx vivo Cryosections Results Stacks of 2d grayscale imagesStacks of 2d RGB images Common Segmentation algorithms DIRECTLY ADOPTED

Visualization and Computer Graphics Lab International University Bremen WSCG Goal Create a general procedure that allows: conversion 3D RGB color fields to 3D scalar fields application any existing segmentation algorithm to scalar data

Visualization and Computer Graphics Lab International University Bremen WSCG General approach 1.Input: the initial stack of RGB images 2.Convert the RGB data to L*a*b* 3.Apply one of quantization algorithms 4.Apply the Color2Gray algorithm 5.Apply any segmentation algorithm

Visualization and Computer Graphics Lab International University Bremen WSCG Quantization techniques Known applied algorithms: 1.Static look-up table algorithm 2.Popularity algorithm 3.Median cut algorithm 4.Genetic algorithm 5.K means algorithm 6.C means algorithm

Visualization and Computer Graphics Lab International University Bremen WSCG Quantization techniques Proposed algorithms: Axes-aligned binary-space partitioning Improved genetic algorithm

Visualization and Computer Graphics Lab International University Bremen WSCG Quantization techniques Proposed algorithms: Axes-aligned binary-space partitioning ……….. Repeat merging until get K clusters

Visualization and Computer Graphics Lab International University Bremen WSCG Quantization techniques Proposed algorithms: Improved genetic algorithm 1.A gene - a center of a cluster; 2.A chromosome consists of K genes; 3.The initial population is the centers of the cubes built by Median cut tree with log2(K) depth; 4.The fitness function:

Visualization and Computer Graphics Lab International University Bremen WSCG Color-to-Scalar Conversion: Color2Gray Non-linear color mapping Radius of neighboring pixels  Max chrominance offset  Map chromatic difference to increases or decreases in luminance values  Adjustable parameters

Visualization and Computer Graphics Lab International University Bremen WSCG Color-to-Scalar Conversion: Color2Gray is the signed distance scalar based on luminance and chrominance differences between pixels i, j. Complexity without quantizationComplexity after quantization and restructuring O(N6)O(N6)O(K2)O(K2) for NxNxN image with K unique colors after quantization

Visualization and Computer Graphics Lab International University Bremen WSCG Conversion results Left: Cryosection of Macaque monkey brain with encircled region of interest. Middle: Converted images using luminance-based approach. Right: Converted images using Median cut quantization and Color2Gray approach.

Visualization and Computer Graphics Lab International University Bremen WSCG Conversion results Left: A cut from a Horizontal slice through Visible Female data set. Middle: Converted images using luminance-based approach. Right: Converted images using Color2Gray.

Visualization and Computer Graphics Lab International University Bremen WSCG Segmentation results Left: Segmentation after luminance-based conversion cannot detect the formally purple region. Right: Segmentation after conversion with our approach allows to separate the tissues.

Visualization and Computer Graphics Lab International University Bremen WSCG Segmentation results Left: Horizontal slice through Visible Female data set. Region of interest is encircled. Middle: For the image generated by a luminance-based conversion, the segmentation of the initially red region fails. Right: For the image generated with our conversion algorithm, the region can be segmented very well.

Visualization and Computer Graphics Lab International University Bremen WSCG Segmentation results Left : Fluorescence microscopy data set of a cancer cell. Middle: 3D segmentation of yellow regions via our color-to-scalar conversion approach. Right: the segmentation after luminance-based conversion does not separate the yellow from the green and red regions completely.

Visualization and Computer Graphics Lab International University Bremen WSCG Conclusions The procedure for conversion of color data to scalar data for segmentation purposes is presented. Two clusterization methods have been proposed.

Visualization and Computer Graphics Lab International University Bremen WSCG Thank you for your attention!!! Any questions???