We introduce an image segmentation algorithm, called GC sum max, which combines, in
novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness …

Many image segmentation methods utilize graph structures for representing images, where
the flexibility and generality of the abstract structure is beneficial. By using a fuzzy object …

Interactive image segmentation methods normally rely on cues about the foreground
imposed by the user as region constraints (markers/brush strokes) or boundary constraints …

Purpose Automated segmentation of brain structures (objects) in MR three‐dimensional
(3D) images for quantitative analysis has been a challenge and probabilistic atlases (PAs) …

We propose a novel efficient seed-based method for the multi-object segmentation of
images based on graphs, named Hierarchical Layered Oriented Image Foresting Transform …

In this work, we propose a hybrid method for seeded image segmentation, named Relaxed
OIFT, which extends a method by Malmberg et al. to directed graphs, to properly incorporate …

In this work, we propose a hybrid method for image segmentation based on the selection of
four extreme points (leftmost, rightmost, top and bottom pixels at the object boundary) …

This paper describes the development of an algorithm to automatically generate seeds for
lung CT (Computed Tomography) segmentation. The segmentation algorithm used is ROIFT …

Essentially all imaging techniques provide some kind of geometric information about the
object: some provide information about anatomy and/or function (eg,< noindent magnetic …

Medical image segmentation is crucial to extract measures from 3D objects (body
anatomical structures) that are useful for diagnosis and treatment of diseases. In such …