Improved Zhang-Suen thinning algorithm in binary line drawing applications

Chen, Wei; Sui, Lichun; Xu, Zhengchao; Yu, Lang

doi:10.1109/icsai.2012.6223430

Cited by 57 publications

(28 citation statements)

References 2 publications

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“…After these pre-processing steps, the image was prepared for input to our proposed Concavity Point with Skeleton Algorithm. Here, a part of algorithm in [10] was used to get the concave point along with skeleton algorithm [15]. As proven in previous research, the skeleton algorithm are best performed in binary images [16], [17] to get the backbone of the object.…”

Section: Methodsmentioning

confidence: 99%

Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

Ahmad

Abdullah

Sabudin

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In practice, most hospitals use light microscope to examine the smeared blood for blood quantification. This visual quantification is subjective, laborious and time-consuming. Although automating the process is a good solution, the available techniques are unable to count or ignore the clumpy red blood cells (RBC). Moreover, clumping cell can affect the whole counting process of RBC as well as their accuracy. This paper proposes a new quantization process called concavity point and skeleton analysis (CP-SA) for heavily clump RBC. The proposed methodology is based on induction approach, enhanced lime blood cell by using gamma correction to get the appropriate edges. Then, splitting the clump and single cells by calculating each object area in pixel. Later, the quantification of clumpy cells with the proposed CP-SA method is done. This algorithm has been tested on 556 clump RBC taken from thin blood smear images under light microscope. All dataset images are captured from Hematology Unit, UKM Medical Centre in Kuala Lumpur. On all tested images, the cells of interest are successfully detected and counted from those clump cells. A comparative study and analysis to evaluate the performance of the proposed algorithm in three levels of clump have been conducted. The first level was with two clumps, second level with three clumps and third level with four clumps. The counting number of clump cells has been analyzed using quantitative analysis, resulting in much better results compared to other recent algorithms. The comparison shows that the proposed method gives better precision result at all levels with respect to ground truth: two clump cells (92%), three clump cells (96%) and four clump cells (90%). The results prove that this study has successfully developed a new method to count heavily clump RBC more accurately in microscopic images. In addition, this can be considered as a low-cost solution for quantification in massive examination.

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Section: Methodsmentioning

confidence: 99%

Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

Ahmad

Abdullah

Sabudin

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In other words, selection of the object to be recognized from the general document, clearing the object from the noise, extracting its skeleton and reducing it to the same size ( Figure 2). Zhang-Suen algorithm is used for extracting the skeleton of the selected characters after being reduced to the same size [11].…”

Section: Processing Of Charactersmentioning

confidence: 99%

Study of Azerbaijani Hand-Printed Characters Recognition System by New Feature Class and SVM Method`

Ismayilov¹,

Oil²

2018

JPIT

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Although there are widely spread Latin scripts in Azerbaijani alphabet, intending special symbols and morphological content of the language requires individual approach for character recognition. In this paper, "soft" (close to human mind, constructed on base of characteristics used in alphabet learning) features and SVM for recognition of Azerbaijani hand-printed characters are used. For character classification, bootstrap resampling procedure of support vector machines is used. Results are compared with results of other feature classes and methods.

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“…Furthermore, each iteration depends on the previous one and parallelization in terms of iterations is not easily achievable. Among the most important ITAs, we can find the Arcelli solution [5], the Zhang-Suen algorithm [6] and its improvements [7].…”

Section: Related Workmentioning

confidence: 99%

Hardware accelerator for fast image/video thinning

Davalle

Carnevale

Saponara

et al. 2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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Image thinning algorithms are widely used in image processing to simplify elaboration preserving geometrical features. Standard approaches are based on iterative methods and on distance transforms. Both techniques are well known to be computationally intensive. In this work we propose a parallel, fast and flexible hardware architecture for image thinning to achieve real-time performance. The test case is the 720 × 576 PAL standard video at 25 frame per second (fps). Synthesis was performed for a Stratix II FPGA EP2S30 and for a standard cell 65nm CMOS technology. The former showed a usage of 4%slices and 1% registers, the latter gave an occupation of 5 kgates for the processing core. The execution time for one frame was 0.03 s on the FPGA and 0.009 s on the 65 nm, resulting in a maximum throughput of 33 fps and 111 fps, respectively

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Improved Zhang-Suen thinning algorithm in binary line drawing applications

Cited by 57 publications

References 2 publications

Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

Concavity Point and Skeleton Analysis Algorithm for Detection and Quantization in Heavily Clumped Red Blood Cells

Study of Azerbaijani Hand-Printed Characters Recognition System by New Feature Class and SVM Method`

Hardware accelerator for fast image/video thinning

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