Segmentation of volumetric images for accurate distinction of

biologically significant entities

Ryan Green

Contents

• Problems with volumetric images• Current approaches to image analysis• Flood-Fill• Parallelised RAM-efficient Flood-Fill• Conclusion

Problems with volumetric images

• Difficult to determine biologically significant entities in 2D

• Difficult to visualise in 3D

• Large processor and RAM requirements

Magnetic Resonance Images

Standard MR Images in 2 and 3 Dimensions

How images are currently analysed• Manual 2D visual analysis• Manual 3D visual analysis via transparencies

according to tissue intensity• Automated/Semi-Automated Image segmentation via

Fuzzy C-Means (FCM) and extensions to FCM

FCM segmentation in to Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF)

Standard Flood-fillFlood-fill (node, target-color, replacement-color): 1. If the color of node is not equal to target-color, return. 2. Set the color of node to replacement-color. 3. Perform Flood-fill (one step to the west of node, target-color, replacement-color).     Perform Flood-fill (one step to the east of node, target-color, replacement-color).     Perform Flood-fill (one step to the north of node, target-color, replacement-color).     Perform Flood-fill (one step to the south of node, target-color, replacement-color). 4. Return.

• Theoretically sound• Realistically breaks down in a

stack overflow

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Improved Flood-Fill

Flood-fill (node, target-color, replacement-color): 1. Set Q to the empty queue. 2. If the color of node is not equal to target-color, return. 3. Add node to the end of Q. 4. While Q is not empty:  5.     Set n equal to the first element of Q 6.     If the color of n is equal to target-color, set the color of n to replacement-color. 7.     Remove first element from Q 8.     If the color of the node to the west of n is target-color: 9.         Add that node to the end of Q10.     If the color of the node to the east of n is target-color: 11.         Add that node to the end of Q12.     If the color of the node to the north of n is target-color:13.         Add that node to the end of Q14.     If the color of the node to the south of n is target-color:15.         Add that node to the end of Q16. Return.

Real-World implementations

• Many optimisations are possible such as:o East-West loopso Scanline Fillso Boundary condition checks

• Yet most still conform to the same logical basis as the recursive algorithm

User driven centroid selection

• Used for isolation of a specific pre-defined biologicially significant entity via a modified parallel flood-fill algorithm

3 images from a 4000x4000x4000 volumetric image, at depths at the end of the first, second, and third quarters

Parallelised RAM-Efficient 3D Flood-Fill• Improve Queue based Flood-Fill with East-West

optimisation• Each slice of the volumetric image on the z-axis

possesses it's own queue• The volumetric image is divided along the z-axis

according to the number of processes available• Boundary slices (the single slices between divided

segments) are left until last to prevent clashes• Only slices currently being used by a process are

loaded into RAM

Possible example output

Images from Google Body showing differing layers of anatomy of a synthetic person

Conclusion

• There is a problem faced by many researchers dealing with large volumetric images

• Current processes are insufficient to handle the increasing number and complexity of images

• Parallelised 3D flood-fill with user defined centroids will assist researchers in accurately separating biologically significant entities of interest for isolated analysis