Biomedical image segmentation using geometric deformable models and metaheuristics

Mesejo, Pablo; Valsecchi, Andrea; Marrakchi-Kacem, Linda; Cagnoni, Stefano; Damas, Sergio

doi:10.1016/j.compmedimag.2013.12.005

Cited by 49 publications

(33 citation statements)

References 50 publications

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“…According to the type of shape representation used to define the model, DM methods can be categorized in: parametric or explicit deformable models [23,41,42,57,91,92] and geometric or implicit deformable models [27,37,43,58,59,[93][94][95][96][97][98].…”

Section: Deformable Modelsmentioning

confidence: 99%

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Dolz

Massoptier²,

Vermandel

2015

IRBM

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This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.

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Section: Deformable Modelsmentioning

confidence: 99%

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Dolz

Massoptier²,

Vermandel

2015

IRBM

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“…HLS METHOD A. Level set and force definition HLS combines edge, region and prior shape knowledge of the target object to guide the LS evolution [4]. Figure 1 provides an overview of HLS.…”

Section: Related Workmentioning

confidence: 99%

“…The number of iterations for GAC and CV was set to 500 to ensure the process reached convergence. With respect to the parameters used in ST and DS, all configurations were taken from the original proposals [4].…”

Section: Parameter Settingsmentioning

confidence: 99%

“…However, although the overall performance of the method was excellent, we did not investigate extensively the benefit of the learning process over the segmentation results. The main contribution of this work is to extend the research performed in [4] by studying the contribution of the tuning to the final results. To this end, we replicated the original experimental study using different approaches for the machine learning step, comparing our GA with random and grid search, two popular techniques in parameter tuning.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], we introduced Hybrid Level Set (HLS), a segmentation framework for medical images, combining region, edge and prior shape information. The algorithm uses a machine learning process, based on a Genetic Algorithm (GA), to automatically adapt the segmentation method to different kinds of images and target regions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic evolutionary medical image segmentation using deformable models

Valsecchi

Mesejo

Marrakchi-Kacem³

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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Abstract-This paper describes a hybrid level set approach to medical image segmentation. The method combines regionand edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation.The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six stateof-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed.

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Fused deep learning based Facial Expression Recognition of students in online learning mode

Sumalakshmi

Vasuki²

2022

Concurrency and Computation

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In this research work, Facial Expression Recognition (FER) is used in the analysis of facial expressions during the online learning sessions in the prevailing pandemic situation. An integrated geometric and appearance feature extraction is presented for the FER of the students participating in the online classes. The integrated features provided a low-dimensional significant feature area for better facial data representation. Feasible Weighted Squirrel Search Optimization (FW-SSO) algorithm is applied for selecting the optimal features due to its efficient exploration of the search space and enhancement of the dynamic search. The output of the FW-SSO algorithm is used for tuning the autoencoder. Autoencoder is used for combining the G&A features, for feature optimization process. Classification is done by using Long Short-Term Memory (LSTM) network with Attention Mechanism (ALSTM), as it is highly efficient in capturing the long-term dependency of the facial landmarks in the image/video sequences.The proposed fused deep learning method focuses on the fusion of the G&A features for high discrimination. Experimental analysis using FER-2013 and LIRIS datasets demonstrated that the proposed method achieved maximum accuracy of 85.96% than the existing architectures and maximum accuracy of 88.24% than the VGGNet-CNN architecture.

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Biomedical image segmentation using geometric deformable models and metaheuristics

Cited by 49 publications

References 50 publications

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey

Automatic evolutionary medical image segmentation using deformable models

Fused deep learning based Facial Expression Recognition of students in online learning mode

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