REVIEW ON THINNING TO INCREASE THE THINNING RATE OF BINARY IMAGE

REVIEW ON THINNING TO INCREASE THE

THINNING RATE OF BINARY IMAGE

Ramneet Kaur

, Er. Amritpal kaur

,

1

M.Tech Researcher Student,

Assistant Professor,Department of CSE,

Sri Guru Granth Sahib World University(Fatehgarh Sahib)(India)

ABSTRACT

Thinning is the preprocessing stage to make easy higher level analysis and recognition for such applications

like OCR, thinning techniques have been applied in many fields such as automated industrial inspection, pattern

recognition, biological shape description and image coding etc. The main objective of thinning is to improve

efficiency, to reduce transmission time. There are various algorithms to improve thinning rate. It is concluded

that there are some loopholes in thinning algorithm. So there is a need to improve thinning rate .In the proposed

enchancement in Zhang-suen algorithm to improve thinning rate.

I. INTRODUCTION

Digital Image Processing comprises of three words: Digital, Image & Processing. Image processing is a method

to perform some operations on an image, in order to get an enhanced image or to extract some useful

information from it. By transforming the binary image into skeleton which reduce the storage of data and reduce

the transmission time also. Skeletonization is the process of extracting skeletons from an object in a digital

image. It is morphological operation that deletes black foreground pixels iteratively layer by

layer until one pixel width skeleton is obtained. There are many image analysis techniques in which

skeletonization is a “pre-processing” step [2]. It is a process of

reducing an object in a digital image to the minimum size necessary for machine recognition of that object

[3].Binary image is obtain by the combination of black and white pixels. By converting the input binary image

into output .

II. APPLICATIONS OF THINNING

Skeletonization has been used for variety of image processing applications like:

 Fingerprint classification

 Biometric authentication

 Signature verification

 Medical imaging

III. RELATED WORK

3.1 Homotopic thinning

The homotopic thinning is a morphological image trasformation that aims to represent object by their medical

axis lines while preserving image topology. Homotopic thinning is applied on binary image which proceed by

removing the pixels called simple pixels. By removing the border pixels while not change the background nor

the foreground connectedness.Simple pixels configuration is more important than introduce end pixel notation

to obtain the thin skeleton that preserve the extremities as well as the topology. Infact, adapting the binary

thinning to the grayscale case by preprocessing and thresholding may cause irrevocable information loss. The

gray skeleton is obtained after stability of an iterative peeling consisting here in lowering gray values. It is

obtain by the composed of pixels located at the crest lines. We mainly focused on fitting the thinning parameter

for removal of insignificant information and this approach implement for considering the connectedness

preservation .Firstly leads to the adjustment of thinning parameters and secondly leads to overcome the

algorithms challenges related to the ascendant gray level treatment by using hierarchical queues.

3.2 Grayscale thinning

Binary image consist of background pixels({x ,F(x)=0}) and foreground pixels ({x ,F(x)=1}) also known as

object pixels . Homotopic thinning is applied on binary image to remove the object pixels without changing the

connectedness of both foreground and background pixels .The 4connectivity N4 is classically used for

background and the 8 connectivity N8 for the foreground. There are two conditions and the first condition states

simply that x is a border object and second condition that removing x doesn’t change the object connectedness.

This process obtain thin skeleton composed only of barrier between connected components with no unblocked

branches. The end pixels are introduced in order to protect extremities was also extended to gray tone images.

The behavior of thinning algorithms for noisy images are leads to over connected skeletons. Then thinning

measurement does not allow disconnection and dissociate noise related and significant information.

3.3 Parameter based thinning

For the noisy image result show a lot of non significant crests .To improve this a local contrast parameter

introduce called The thinning constraints are relax by parametric thinning by lowering low contrast crests, peak

and ends according to manually selected parameters. Parametric thinning process having the local adjustment

and standardization .The thinning parameters by using the statistical adjustment linked to both noise standard

deviation and image contrast .Result of statistical adjustment and implementation illustrated on both synthetic

IV. REVIEW OF LITERATURE

Abu-Ain W, et al. in this paper a new skeletonization algorithm was proposed to combine both iterative parallel

and sequential approaches .This algorithm Is made up of three stages .Skeletons are obtain using first two steps

and third one is used to optimizing the extracted skeletons into single pixel width. An experimental result gives

better results than the previous skeletonization algorithms.

Padole G.V.Pokle S.B. describe two iterative algorithm for the binary image thinning. In the first given

algorithm, two operations are described edge detection and subtraction are used for the thinning of binary

image. Second algorithm is based on repeatedly deleting the pixels until a one pixel thick pattern in a binary

image is obtained. Erosion conditions are devised to assure preserving connectivity. Experimental results show

that edge based iterative thinning algorithm is time consuming as compared to optimized skeletonization

algorithm.

T. Zhang et al. in this paper thinning of binary images is done by repeating two sub-iterations in which one

delete the south-east boundary points and the west corner points while the second is for delete the

north-west boundary points and south-east corner points .The point are deleting according to a specific set of rules.

The two sub-iterations are repeated until no more points validate the deleting rules.

Shang L., Yi Z. a new sequential algorithm is proposed in which uses flag map and bitmap simultaneously to

decide whether a boundary pixel should be deleted or not. To compare the previous algorithm with other there

are three performance criteria are proposed in this paper. Experimental results given by the proposed algorithm

is not only one pixel thick and perfectly connected, well defined but are also immune to noise.

Datta A. et al. mainly two new parallel algorithms are proposed in this paper. Those algorithm convert the image

into one pixel thick width and help us to maintain the 8neighbour connectivity in binary image. This proposed

algorithm gives the better result than the previous algorithms.

Ahemed P. in this paper a new K3M skeletonization algorithm was proposed that show some interesting

properties related to processing quality and algorithm clarity, enriched with examples.

Li Z. et al. to maintain the connectivity of the binarized fingerprint image a new robust parallel thinning

algorithm is proposed to obtain the skeleton which is one pixel wide which gets extremely close to the medical

axis .Three sub iterations are repeated by proposed thinning method .Result gives by proposed robust parallel

thinning algorithm obtain better skeletons than the previous algorithm.

Kumar V. et to make the algorithm automatic the proposed algorithm measure the value of connected

component. In this algorithm there is no requirement of human interaction and also free from the shape and font

and does not required any preprocessing .So it is better than others.

The following table1 shows the advantage and disadvantages of different thinning techniques

Table 1. Comparison between different thinning techniques

Author Description Advantages Disadvantages

Padole G.V

(2010)

Two new iterative

algorithms are proposed

T. Zhang et al.

(1984 )

Perform thinning by

repeating two sub-iteration

Efficient, fast and flexible Problem in reliable

segmentation

Ahmed P.

(2002)

Propose new

skeletonization algorithm

K3M

Symmetrical thinning,

speed and thin symbol of

any language

Expansive

Datta A. et al. Shape extraction Grid independent Noise sensitivity and

rotation dependency

Li Z. et al. Propose a robust parallel

thinning algorithm

Remove unexpected edge

disconnection

Problem in the quality of

skeleton

Kumar V.et al. Describe new novel

scheme for thinning

Measure value of

connected component, it is

automatic

Overcome loss of

information problem

Abu-ain W, et al.

(2013)

Combine sequential and

parallel approaches

Reduction of memory

space required to store

essential structure

Some portion of same is

totally disapperared

V. CONCLUSION

This paper provides the survey of various algorithms for binary images thinning. The main aspects of

skeletonization algorithms is noise immunity. In the proposed work by the use of neural network and Zhang and

Suen algorithm we make the algorithm more robust than the previous skeletonization techniques. By comparing

the existing techniques with the proposed techniques in the terms of parameters such as execution time, memory

,thinning rate, number of connected components, PSNR,MSE etc which makes the proposed algorithm better

than the previous skeletonization algorithms.

REFERENCES

[1] Abu-Ain W, et al. “Skeletonization Algorithm for Binary Images” The 4th International Conference

onElectrical Engineering and Informatics (ICEEI 2013) pp 704-709

[2] Padole G.V, Pokle S. B. “New Iterative Algorithms For Thinning Binary Images” Third International

Conference on Emerging Trends in Engineering and Technolgy IEEE 2010 pp. 166-171

[3] Datta A. et al “Shape Extraction: A rative Study Between Neural Network-Comparative Study

Between Neural Network-Based and Conventional Techniques” Neural Computing & Applications (1998)

Springer pp. 343-355

[4] T. Zhang et al. “A fast parallel algorithm for thinning digital patterns”, Commun. ACM 27 (3) (1984) pp.

236-239

[5] Ahmed et al. “A Rotation Invariant Rule-Based Thinning Algorithm for Character Recognition” IEEE

[6] Kumar V. et al. “A New Skeletonization Method Based on Connected Component Approach” IJCSNS

International Journal of Computer Science and Network Security, VOL.8 No.2, February 2008 pp. 133-137

[7] Li Z. et al. “Modified Binary Image Thinning Using Template-Based PCNN” (2013) International

conference on information technology and software engineering volume 212 pp.731 -740

[8] Shang L., Yi Z. “ A class of binary images using two PCNNs” Neurocomputing 70(2007) pp.