A chain code for representing high definition contour shapes

Bribiesca, Ernesto; Bribiesca-Contreras, Fernanda; Carrillo-Bermejo, Ángel; Bribiesca-Correa, Graciela; Hevia–Montiel, Nidiyare

doi:10.1016/j.jvcir.2019.03.015

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The use of SCC as a representation for 2D curves provides important advantages for computing tortuosity, as it is independent of translation, rotation, and scaling. Moreover, this approach has shown promising results in high-definition contour shapes, as demonstrated in [17], and the application of grammatical techniques simplifies the tortuosity computation process. For a review of techniques used to measure the tortuosity of retinal blood vessels, please refer to [18].…”

Section: Introductionmentioning

confidence: 99%

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis

Mateos,

Bribiesca,

Guzmán-Arenas

et al. 2024

BMC Med Imaging

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In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity ($$\tau _{3D}$$ τ 3 D ) as a shape descriptor was investigated by characterizing brain structures. The results of the $$\tau _{3D}$$ τ 3 D computation on the central sulcus and the main lobes revealed significant differences between Alzheimer’s disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a $$p<0.05$$ p < 0.05 for the left central sulcus and the four brain lobes.

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Section: Introductionmentioning

confidence: 99%

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis

Mateos,

Bribiesca,

Guzmán-Arenas

et al. 2024

BMC Med Imaging

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“…The chain code is actually a sequence of instructions by which a walk-about is performed along the rasterized shape's border. Various types of chain codes have been proposed: Freeman chain code in four (F4) and in eight (F8) directions [5], Vertex Chain Code (VCC) [6], Three-OrThogonal chain code (3OT) [7], Unsigned Manhattan Chain Code (UMCC) [8], mid-crack chain code (MDCC) [9], slope chain code (SCC) [10], and extended slope chain code (ESCC) [11]. In this paper, however, F4 is used, which is, together with the F8, the oldest and the most widely used chain code.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multi-Resolution Chain Coding

Nerat,

Strnad,

Žalik

et al. 2024

IEEE Access

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A new compact encoding is presented of rasterized bi-level shapes at multiple resolutions. The encoder accepts the Freeman chain code in four directions (F4) at the input, and builds a multi-resolution code named MrCC. The encoding process constructs the coarser representation of F4 chain code, and the resulting MrCC codes simultaneously. MrCC encodes the differences between sequences of F4 chain code from successive resolutions. Several transformations are performed during this process. Various rasterized shapes were used to analyse the efficiency of the new code, which was, on average, 22% better than the concatenation of F4 chain codes of various resolutions. INDEX TERMS chain code, progressive chain code, multi-resolution chain code

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“…He employed 4-or 8-connectivity to generate the so-called Freeman chain code in four (F4) or eight (F8) directions. Later, the vertex chain code (Bribiesca, 1999), three-orthogonal chain code (Sánchez-Cruz and Rodríguez-Dagnino, 2005), Unsigned Manhattan chain code (Žalik et al, 2016b), slope chain code (Bribiesca and Bribiesca-Contreras, 2014) and extended slope chain code (Bribiesca et al, 2019) were presented, too. Chain codes were also developed for 3D (Sánchez-Cruz et al, 2014;Lemus et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Extended algorithm to construct a quadtree from Freeman chain code in four directions

et al. 2019

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This paper introduces improvements to the algorithm that was proposed in 2001 by Chen and Chen. The algorithm constructs a quadtree directly from Freeman chain code in four directions. We have improved the algorithm in two ways: Firstly, a time efficient solution using the space filling Z-order curve is proposed for a self-intersection case that was not considered by Chen and Chen. Secondly, the algorithm is expanded to handle geometric objects containing holes. The computational efficiency of the extended algorithm was confirmed by the experiments.

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A chain code for representing high definition contour shapes

Cited by 3 publications

References 18 publications

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis

3D Tortuosity computation as a shape descriptor and its application to brain structure analysis

An Efficient Multi-Resolution Chain Coding

Extended algorithm to construct a quadtree from Freeman chain code in four directions

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