Genetic Snakes for Color Images Segmentation

Ballerini, Lucia

doi:10.1007/3-540-45365-2_28

Cited by 18 publications

(14 citation statements)

References 11 publications

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“…In the literature, model fitting usually relies on generic models, such as deformable image models in 2-D [11], [12], and their extensions in 3-D [13], [14]. Similar work on 3-D surface registration [15] is also dealing with generic models for segmentation purposes.…”

Section: B Medical Applications Of Artificial Evolutionmentioning

confidence: 99%

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

Vidal

Villard

Lutton

2012

IEEE Trans. Biomed. Eng.

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We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patient's specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test cases: 1) three patient datasets have been acquired with the "breath hold" protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

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Section: B Medical Applications Of Artificial Evolutionmentioning

confidence: 99%

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

Vidal

Villard

Lutton

2012

IEEE Trans. Biomed. Eng.

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“…The total number of snake control points is stored in the chromosomes. This is the encoding scheme employed in all the works developed by Ballerini [75,106,108,116,117] and other related approaches [119,122]. Alternatively, other works encode the location of each control point using Cartesian coordinates.…”

Section: Encodingmentioning

confidence: 99%

“…With respect to the selection mechanism, roulette wheel is the most frequently employed method [75,98,100,106,108,116,117,119,122], followed by tournament selection [92,97,101]. A rank-based selection operator was implemented in [77] while in [110,111] the authors differentiate between inter-and intra-population selection mechanisms.…”

Section: Operatorsmentioning

confidence: 99%

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A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Mesejo

Ibáñez

Cordón

et al. 2016

Applied Soft Computing

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Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires that an optimization framework be defined whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecting the optimization and segmentation algorithms' parameters; choosing the representation for encoding prior knowledge on the image domain of interest; initializing the curve in a location which favors its convergence onto the boundary of the object of interest. All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem. After recalling the principles underlying deformable models and metaheuristics, we broadly review the different metaheuristic-based approaches to image segmentation based on deformable models, and conclude with a general discussion about methodological and design issues as well as future research and application trends.

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“…Genetic Snakes [2] are active contour models with an energy minimization procedure based on Genetic Algorithms (GAs). In [14], GAs are applied to evolve a population of shapes, using prior shape knowledge to produce feasible deformations while also controlling the scale and localization of these deformations.…”

Section: Related Workmentioning

confidence: 99%

Automatic hippocampus localization in histological images using PSO-based deformable models

Ugolotti

Mesejo

Cagnoni

et al. 2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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International audienceThe Allen Brain Atlas (ABA) is a cellular-resolution, genome-wide map of gene expression in the mouse brain which allows users to compare gene expression patterns in neuroanatomical structures. The correct localization of the structures is the first step to carry on this comparison in an automatic way. In this paper we present a completely automatic tool for the localization of the hippocampus that can be easily adapted also to other subcortical structures. This goal is achieved in two distinct phases. The first phase, called "best reference slice selection", is performed by comparing the image of the brain with a reference Atlas provided by ABA using a two-step affine registration. By doing so the system is able to automatically find to which brain section the image corresponds and wherein the image the hippocampus is roughly located. The second phase, the proper "hippocampus localization", is based on a method that combines Particle Swarm Optimization (PSO) and a novel technique inspired by Active Shape Models (ASMs). The hippocampus is found by adapting a deformable model derived statistically, in order to make it overlap with the hippocampus image. Experiments on a test set of 120 images yielded a perfect or good localization in 89.2% of cases

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Genetic Snakes for Color Images Segmentation

Cited by 18 publications

References 11 publications

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

Tuning of Patient-Specific Deformable Models Using an Adaptive Evolutionary Optimization Strategy

A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Automatic hippocampus localization in histological images using PSO-based deformable models

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