An accurate Cluster chaotic optimization approach for digital medical image segmentation

Ávalos, Omar; Ayala, Ernesto; Wario, Fernando; Pérez‐Cisneros, Marco

doi:10.1007/s00521-021-05771-8

Cited by 6 publications

(7 citation statements)

References 65 publications

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“…An evaluable emotion, interest can stimulate learning and exploration. If an artwork stimulates people to learn for their own interests, it can help them gain diverse knowledge, skills, and experience ( Avalos et al, 2021 ). Interest evaluation methods involve two aspects, namely the evaluation of novelty and the complexity of stimuli.…”

Section: Literature Discussionmentioning

confidence: 99%

“…Since the pioneering work of Berlyne (1971) , interest (in terms of the esthetic experience and in experiential esthetics) has been widely studied ( Tsutsui and Ohmi, 2001 ; Silvia, 2005c ; Silva, 2006 ; Avalos et al, 2021 ), but confusion in the context of art has only recently attracted attention ( Silvia, 2010 ; Silvia and Berg, 2011 ). Most studies on confusion have focused on facial and other physical expressions ( D’Mello and Graesser, 2009 ) while ignoring the parallel evaluation structure related to confusion and interest ( Silvia, 2010 ).…”

Section: Literature Discussionmentioning

confidence: 99%

“…Experts often feel that the more they learn, the more complex and mysterious they consider a field. Research also indicates that concepts that confuse novices may interest experts because they are better able to understand them ( Silvia, 2005a , b ; Silva, 2008 ; Avalos et al, 2021 ). For example, experts are more likely to find art interesting ( Millis, 2001 ; Avalos et al, 2021 ).…”

Section: Literature Discussionmentioning

confidence: 99%

“…On the basis of a literature review and research background, the study aimed to add a new dimension to the esthetic experience model ( Silvia et al, 2013 ; Avalos et al, 2021 ) of new media literacy that is suitable for representing esthetic experiences in the digital environment.…”

Section: Introductionmentioning

confidence: 99%

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Ameliorated New Media Literacy Model Based on an Esthetic Model: The Ability of a College Student Audience to Enter the Field of Digital Art

Wang²,

Hsu³

2022

Front. Psychol.

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In the current digital environment, people can visit every corner of the world without leaving their homes. New media technology compresses distance and time, but it also subverts the traditional mode of audience presence. Many traditional, offline content expression modes are also moving toward the digital field, and digital art is among them. Digital new media is a new art form that requires its audience to have a new media literacy; this literacy is necessary for esthetic experience and for audience participation. At present, the relatively lack of objective methodology for scientific research on aesthetic and media literacy has limited our current understanding. Therefore, we need to develop a new model and conduct empirical research with college students as the audience. Empirical research was conducted with an audience of college students. The study had the following purposes: (1) to add a new dimension to the esthetic model, namely new media literacy, to align the model with the current digital environment, and (2) to test the moderating effect of new media literacy on esthetic emotion as represented by interest and confusion. The experiment verified the study’s hypothesis that higher new media literacy was associated with higher esthetic interest and lower confusion. By contrast, has a substantial influence on the cognitive processes in humans, lower new media literacy was associated with lower esthetic interest and higher confusion. New media literacy is an essential quality for contemporary audiences. This knowledge may be useful for effective design. It provides a traditional and favorable learning environment and empirical reference for the subsequent improvement of digital aesthetics and media literacy.

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Section: Literature Discussionmentioning

confidence: 99%

Section: Literature Discussionmentioning

confidence: 99%

Section: Literature Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ameliorated New Media Literacy Model Based on an Esthetic Model: The Ability of a College Student Audience to Enter the Field of Digital Art

Wang²,

Hsu³

2022

Front. Psychol.

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“…A basic task in medical IS [1,2] is to extract specific organs and tumors from different types of medical images. Organ and tumor segmentation provides an important basis for cancer diagnosis, surgical planning, and pathological analysis.…”

Section: Introductionmentioning

confidence: 99%

A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

Yang

2021

Scientific Programming

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Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

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Sailfish optimizer with Levy flight, chaotic and opposition-based multi-level thresholding for medical image segmentation

Shajin

Devi

Prakash³

et al. 2023

Soft Comput

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An accurate Cluster chaotic optimization approach for digital medical image segmentation

Cited by 6 publications

References 65 publications

Ameliorated New Media Literacy Model Based on an Esthetic Model: The Ability of a College Student Audience to Enter the Field of Digital Art

Ameliorated New Media Literacy Model Based on an Esthetic Model: The Ability of a College Student Audience to Enter the Field of Digital Art

A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

Sailfish optimizer with Levy flight, chaotic and opposition-based multi-level thresholding for medical image segmentation

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