No Cover Image

Conference contribution 290 views

Automatic Segmentation of Lymph Vessel Wall using Optimal Surface Graph Cut and Hidden Markov Models / Jonathan Jones; Ehab Essa; Xianghua Xie

IEEE Engineering in Medicine and Biology Society

Swansea University Author: Xie, Xianghua

Abstract

We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determi...

Full description

Published in: IEEE Engineering in Medicine and Biology Society
Published: IEEE 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa22237
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: We present a novel method to segment the lymph vessel wall in confocal microscopy images using Optimal Surface Segmentation (OSS) and hidden Markov Models (HMM). OSS is used to preform a pre-segmentation on the images, to act as the initial state for the HMM. We utilize a steerable filter to determine edge based filters for both of these segmentations, and use these features to build Gaussian probability distributions for both the vessel walls and the background. From this we infer the emission probability for the HMM, and the transmission probability is learned using a Baum-Welch algorithm. We transform the segmentation problem into one of cost minimization, with each node in the graph corresponding to one state, and the weight for each node being defined using its emission probability. We define the inter-relations between neighboring nodes using the transmission probability. Having constructed the problem, it is solved using the Viterbi algorithm, allowing the vessel to be reconstructed. The optimal solution can be found in polynomial time. We present qualitative and quantitative analysis to show the performance of the proposed method.
Keywords: Medical image analysis, image segmentation, HMM, optimal surface
College: College of Science