Leaf segmentation and tracking using probabilistic parametric active contours

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset

Medical Image Segmentation by Water Flow

We present a new image segmentation technique based on the paradigm of water flow and apply it to medical images. The force field analogy is used to implement the major water flow attributes like water pressure, surface tension and adhesion so that the model achieves topological adaptability and geometrical flexibility. A new snake-like force functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method has been assessed qualitatively and quantitatively, and shows decent detection performance as well as ability to handle noise

A UNIFIED ENERGY APPROACH FOR B-SPLINE SNAKE IN MEDICAL IMAGE SEGMENTATION

 The parametric snake is one of the preferred approaches in feature extraction from images because of their simplicity and efficiency. However the method has also limitations. In this paper an explicit snake that represented using BSpline applied for image segmentation is considered. In this paper, we identify some of these problems and propose efficient solutions to get around them. The proposed method is inspired by classical snake from Kass with some adaption for parametric curve. The paper also proposes new definitions of energy terms in the model to bring the snake performance more robust and efficient for image segmentation. This energy term unify the edge based and region based energy derived from the image data. The main objective of developed work is to develop an automatic method to segment the anatomical organs from medical images which is very hard and tedious to be performed manually. After this segmentation, the anatomical object can be further measured and analyzed to diagnose the anomaly in that organ. The results have shown that the proposed method has been proven qualitatively successful in segmenting different types of medical images.

Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation