custom-based algorithm matlab r2022b Search Results


custom code matlab r2022a  (MathWorks Inc)
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matlab r2020b  (MathWorks Inc)
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matlab r2021b  (MathWorks Inc)
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matlab r2022  (MathWorks Inc)
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Schematic illustration of macular curve fitting methodology. (1) Foveal center (white arrow) determination. (2) Central horizontal and vertical cross-section images passing through the foveal center were recognized. RPE boundary was extracted automatically (blue lines). Macular region was divided to three concentric circles in this study (R1: 1-mm-diameter circle at fovea, R2: 3-mm-diameter circle at fovea, R3: 6-mm-diameter circle at fovea). (3) OCT images were processed under the <t>MATLAB</t> environment, and the macular contour was fitted into a two-dimensional polynomial curve. RPE retinal pigment epithelium. Bar = 200 um
Matlab R2022, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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intel i7 mac mini  (MathWorks Inc)
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Schematic illustration of macular curve fitting methodology. (1) Foveal center (white arrow) determination. (2) Central horizontal and vertical cross-section images passing through the foveal center were recognized. RPE boundary was extracted automatically (blue lines). Macular region was divided to three concentric circles in this study (R1: 1-mm-diameter circle at fovea, R2: 3-mm-diameter circle at fovea, R3: 6-mm-diameter circle at fovea). (3) OCT images were processed under the <t>MATLAB</t> environment, and the macular contour was fitted into a two-dimensional polynomial curve. RPE retinal pigment epithelium. Bar = 200 um
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matlab r2022b  (MathWorks Inc)
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Schematic illustration of macular curve fitting methodology. (1) Foveal center (white arrow) determination. (2) Central horizontal and vertical cross-section images passing through the foveal center were recognized. RPE boundary was extracted automatically (blue lines). Macular region was divided to three concentric circles in this study (R1: 1-mm-diameter circle at fovea, R2: 3-mm-diameter circle at fovea, R3: 6-mm-diameter circle at fovea). (3) OCT images were processed under the <t>MATLAB</t> environment, and the macular contour was fitted into a two-dimensional polynomial curve. RPE retinal pigment epithelium. Bar = 200 um
Matlab R2022b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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eeglab toolbox  (MathWorks Inc)
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Schematic illustration of macular curve fitting methodology. (1) Foveal center (white arrow) determination. (2) Central horizontal and vertical cross-section images passing through the foveal center were recognized. RPE boundary was extracted automatically (blue lines). Macular region was divided to three concentric circles in this study (R1: 1-mm-diameter circle at fovea, R2: 3-mm-diameter circle at fovea, R3: 6-mm-diameter circle at fovea). (3) OCT images were processed under the <t>MATLAB</t> environment, and the macular contour was fitted into a two-dimensional polynomial curve. RPE retinal pigment epithelium. Bar = 200 um
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Image Search Results


Journal: iScience

Article Title: A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

doi: 10.1016/j.isci.2024.109093

Figure Lengend Snippet:

Article Snippet: Data processing was performed using a custom code based on MATLAB R2022a (MathWorks, Natick, MS, USA).

Techniques: Software

Schematic illustration of macular curve fitting methodology. (1) Foveal center (white arrow) determination. (2) Central horizontal and vertical cross-section images passing through the foveal center were recognized. RPE boundary was extracted automatically (blue lines). Macular region was divided to three concentric circles in this study (R1: 1-mm-diameter circle at fovea, R2: 3-mm-diameter circle at fovea, R3: 6-mm-diameter circle at fovea). (3) OCT images were processed under the MATLAB environment, and the macular contour was fitted into a two-dimensional polynomial curve. RPE retinal pigment epithelium. Bar = 200 um

Journal: Ophthalmology and Therapy

Article Title: Assessment of OCT-Based Macular Curvature and Its Relationship with Macular Microvasculature in Children with Anisomyopia

doi: 10.1007/s40123-024-00956-5

Figure Lengend Snippet: Schematic illustration of macular curve fitting methodology. (1) Foveal center (white arrow) determination. (2) Central horizontal and vertical cross-section images passing through the foveal center were recognized. RPE boundary was extracted automatically (blue lines). Macular region was divided to three concentric circles in this study (R1: 1-mm-diameter circle at fovea, R2: 3-mm-diameter circle at fovea, R3: 6-mm-diameter circle at fovea). (3) OCT images were processed under the MATLAB environment, and the macular contour was fitted into a two-dimensional polynomial curve. RPE retinal pigment epithelium. Bar = 200 um

Article Snippet: OCT-based MCIs were determined from horizontal and vertical B-scans by a customized curve fitting model in MATLAB R2022 at 1-mm-, 3-mm-, and 6-mm-diameter circles at fovea.

Techniques: