A survey on Image Segmentation Techniques

(1)

International Journal of Advanced Computer Engineering and Communication Technology (IJACECT) _______________________________________________________________________________________________

_______________________________________________________________________________________________

ISSN (Print): 2278-5140, Volume-4, Issue-4, 2015 39

1Rashmi Savita, ²Samta Jain Goyal

1,2Amity University, Madhya Pradesh, Gwalior

Abstract: In the advancement of computer technology, wide variety of image processing applications introduce in market. An increased requirement of applications in the field of image processing is a classical subject for researchers. Over here, Image segmentation is more concerned and important phase for Image analysis. For image segmentation, various segmentation techniques and algorithms have been developed .This paper presents an overview of some primitive techniques of segmentation.

Index Terms: Image processing, Image segmentation, Image analysis, Digital Image, Filtering, Classification

I. INTRODUCTION

For research and application of the images, image engineering is useful. It consists 3 levels like image processing, image analysis and image understanding.

Image processing level deals with the transformation of images. Image analysis is used to monitor and target the images to get descriptive information whereas image understanding is s specially used to focus on descriptive information of image the main objective of digital image processing is to fetch features from the digital image so that it will not affect the other images Many phases are used to describe in image processing or image analysis but the segmentation phase of an image is an active research area for the past two decades in image segmentation, image is subdivided into number of segments which represents important information. In this process of segmentation, single value is assign to each pixel of an image in order to differentiate different regions of an image Generally differentiation of different segments of an image is done through 3 basic properties. These are-

1. Texture of the image 2. Color of the image 3. Intensity of the image

It’s really hard to say that there is no perfect method for segmentation due to image properties even then we have lots of application areas where segmentation is required like-Filtering noise from medical or other images, computer vision, satellite imaging, biometrics, micvision, military, feature extraction, remote sensing, brain image analysis, Number plates of illegal, Context based image retrieve ,locate tumors, pathologies, tissue- volume measuring, anatomy structure, face

recognisition, finger print reading .Many segment techniques have been proposed based the type & level of image and its characteristics

II. DIGITAL IMAGE PROCESSING

Digital Image Processing is to very latest in computer technique. It's very important domain in research area many applications related with every field are the part of research in image-processing .In DIP we design and use algorithms for the purpose of research DIP is more advance and effective then Analog processing because it gives more cost-effective solution.

III. DIGITAL IMAGE PROCESSING PHASES-

Image Analysis- This phase is used to get quantitative measurements from input image to produce descriptive information. Image Analysis methods are uses this descriptive information with the help of feature extraction, to identify object in image.

Image segmentation-This is a process used to segment an image into objects. Very is the process that subdivides an image into it’s constitute much commonly used techniques for segmentation are Image thresholding.

Classification –It is the based on grey value which are labeled on pixel of input image. This phase is very commonly used for extracting information. In Classification, Generally multiple features are used for a set of pixels in Classification Phase. The Multispectral classification uses two methods for classifications:

Supervised Classifications & Unsupervised Classifications. In supervised classification, extracting quantitative information from remotely sensed image data. The most commonly used supervised classification is maximum likelihood classification (MLC).

But in Unsupervised Learning, it doesn’t require foreknowledge of the classes. Genrally it uses some clustering algorithm to classify an image. Most commonly used unsupervised classifications are the migrating means clustering classifier (MMC).

Image Restoration/ image deblurring /image deconvolution: This is used to minimize or remove degradations of an image. This phase is concerned with the reconstruction or estimation of the uncorrupted

(2)

_______________________________________________________________________________________________

image from a blurred and noisy one.

Image Compression: This phase is very important because it is used to archive image data, transfer on network etc.Lossy and Lossless Compression Techniques are used for the Image compression. JPEG (Joint Photographic Experts Group) uses Discrete Cosine Transformation (DCT) based compression technique which is quiet popular. For higher compression ratios with minimal loss of data, wavelet based compression techniques are used.

IV. IMAGE SEGMENTATION

Since computers are not much intelligent to recognize or separate objects needs process for the same. The process of Image segmentation is use to separate an image into different segments in DIP. By this separation-process, extracting a meaningful region which is known as ROI (Region of Interests) from the image .All Proposed segment algorithm are based on the following principles –

1-For grouping segmented pixels, use similarity principles

2-for extracting regions based on intensity color and texture, use discontinuity principles

On Intent, large multimedia data-streams are uses but due to limitations with bandwith, need video and image segmentations.To managing and indexing data or information use color and texture segmentation .In color image segmentation create histograms or find information regarding texture /boundaries/edges.

V. SEGMENTATION TECHNIQUES

There are many segmentation techniques have been developed in the area of digital image processing all image segmentation techniques are categorized into discontinuity and similarity principles. Region based methods based on similarity principles and boundary based methods based on discontinuity principles.

Structural segmentation techniques stochastic segmentation technique and Hybrid techniques Region based techniques-These are based on discontinuity .In this technique, we divide the entire image into sub regions based on rules. These are generally use common patterns on intensity pattern. Based on their automatical functional roles, segmentation technique groups the regions. These algorithms are relatively simple and very immune to noise. In these methods grouped and marked pixels corresponds to an object, which also require thresholding techniques.

The methods based on Region wise segments are 1- region grouping 2-region splitting and merging

Thresholding –This technique classified the region on the basis of range value .This is applied on the intensity value of the image pixel This is comparatively simpler ,inexpensive and fast and oldest way of digital image segmentation That is why for simple application is commonly used. Based on Threshold values or range

value Thresholding can be of 3 types- 1 Global Thresholding

2. Local thresholding 3. Multilevel thresholding

Thresholding require some pre and post-processing techniques for segments .Based on these, most important and widely used thresholding techniques are-P-tile method, edge maximize method mean method visual technique etc.

Boundary based techniques-To find discontinuities in gray level images edge detection techniques are generally used .During segmentation discontinuity detection are basically boundary based methods An important feature for image analysis is edge detection which is generally focus on the edges of lines curves and corner of segmented image. Spatial masks can be used to detect edge point and line greylevel discontinuities of an image. Gradient and gray histogram is 2 major and commonly used techniques for detection of edge in digital segmentation. Commonly used operator edge detection techniques we color edge detection, laplacian of Gaussian, classical edge detectors; zero crossing etc.

Image segmentation based on ANN: The various features extracted from the image are assigned as the input patterns to the ANNs. To create a network which is used to handle noisy input vectors should be train to handle ideal and noisy vectors. Using back-propagation, all training is done with the adaptive learning rate and momentum. During training, an optional parameter is defined which is used to set the number of epochs between feedback. Finally a trained network is created.

This trained network is used to create the segmentation system classifies each region automatically.

Edge based Techniques for Segmentation: Edge detection is a basic step for image segmentation processes these techniques is most common for detecting boundaries and discontinuities of an input digital image. Basically, an edge is a set of connected pixels. It divides an image into object and its background .Here it is observing the change in intensity or pixels of an image. Gray histogram and Gradient are two most commonly used methods in Edge Detection for Image segmentation.

VI. CONCLUSION

This paper is used to show the survey of many Image segmentation techniques. It’s very difficult to examine the performance of these segmentation methods. For Image segmentation, hybrid solution consists of two or more techniques which would be the better approach, used to solve the problem of image segmentation.

REFERENCES

[1] Jesmin F. Khan, Sharif M. A. Bhuiyan, and Reza R. Adhami,” Image Segmentation and Shape Analysis for Road-Sign Detection”, IEEE

(3)

_______________________________________________________________________________________________

TRANSACTIONS ON INTELLIGENT

TRANSPORTATION SYSTEMS, VOL. 12, NO.

1, MARCH 2011.

[2] S.Lakare and A.Kaufman,”3D Segmenatation techniques For Medical Volumes”, Center For Visual Computing, department Of Computer Science, State University Of New York,dec.2000 [3] Andrew Janowczyk, Sharat Chandran, Rajendra

Singh, Dimitra Sasaroli, George Coukos, Michael D. Feldman, and Anant Madabhushi,” High- Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 59, NO.

5, MAY 2012

[4] W. X. Kang, Q. Q. Yang, R. R. Liang, “The Comparative Research on Image Segmentation Algorithms”, IEEE Conference on ETCS, pp.

703-707,2009

[5] H. Zhang, J. E. Fritts, S. A. Goldman,“Image Segmentation “Evaluation: A Survey of unsupervised methods”, computer vision and image understanding, pp.260-280, 2008

[6] H. G. Kaganami, Z. Beij,“Region Based Detection versus Edge Detection”, IEEE Transactions on Intelligent information hiding and multimedia signal processing, pp. 1217- 1221, 2009.

[7] R.Xu, D.Wunsch A.Jain, M.Murty, P.Flynn,”Data Clustering: A Review”,ACm Computing Surveys,31,1999,pp-264

[8] Rajeshwar Dass, Priyanka, Swapna Devi- “Image Segmentation echniques”

[9] H.P Narkhede-“Review of Image Segmentation Tehniques”

[10] L.Aurdal,“Image Segmentation beyond thresholding”, Norsk Regnescentral, 2006.

[11] Rafael C. Gonzalez, Richard E. Woods, ―Digital Image Processing‖, Second Edition, Pearson Education.

[12] M. Ameer Ali, Gour C. Karmakar, Laurence S.

Dooley, ―Fuzzy Clustering For Image Segmentation Using Generic Shape Information‖, Malaysian Journal of Computer Science, Vol.

21(2), 2008

[13] T. Lindeberg and M.-X. Li "Segmentation and classification of edges using minimum description length approximation and complementary junction cues", Computer Vision and Image Understanding, vol. 67, no. 1, pp. 88-- 98, 1997.

[14] H. Zhang, J. E. Fritts, S. A. Goldman,“Image Segmentation Evaluation: A Survey of unsupervised methods”, computer vision and

image understanding, pp. 260-280, 2008

[15] H. Zhang, J. E. Fritts, S. A. Goldman,“Image Segmentation Evaluation: A Survey of unsupervised methods”, computer vision and image understanding, pp. 260-280, 2008.

[16] K. K. Singh, A. Singh,“A Study of Image Segmentation Algorithms for Different Types of Images”, International Journal of Computer Science Issues, Vol. 7, Issue 5, 2010

[17] Nikhil R Pal and Sankar K Pal, A review on Image Segmenatation Techniques, Indian Statistaical Institute, Pattern Recognition, Vol 26,No.9,pp.1277,1993.

[18] W. X. Kang, Q. Q. Yang, R. R. Liang,“The Comparative Research on Image Segmentation Algorithms”, IEEE Conference on ETCS, pp.

703-707, 2009.

[19] H. G. Kaganami, Z. Beij,“Region Based Detection versus Edge Detection”, IEEE Transactions on Intelligent information hiding and multimedia signal processing, pp. 1217- 1221, 2009.

[20] Alasdair McAndraw, School of Computer Science and Mathematics Victoria University of Technology” An Introduction To digital Image Processing With MATLAB”, September 2004 [21] Changyang Li, Xiuying Wang, Junli Li, Stefan

Eberl, “Joint Probabilistic Model of Shape and Intensity for Multiple Abdominal Organ segmentation From Volumetric CT Images”, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 1, JANUARY 2013

[22] Andrew Janowczyk, Sharat Chandran, Rajendra Singh, Dimitra Sasaroli, George Coukos, Michael D. Feldman, and Anant Madabhushi,” High- Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 59, NO.

5, MAY 2012

[23] T. Lindeberg and M.-X. Li "Segmentation and classification of edges using minimum description length approximation and complementary junction cues", Computer Vision and Image Understanding, vol. 67, no. 1, pp. 88-- 98, 1997.

[24] P. Karch, I. Zolotova,“An Experimental Comparison of Modern Methods of Segmentation”, IEEE 8th International Symposium on SAMI, pp. 247-252, 2010.

[25] T.F. Wang, D.Y. Li et al. Automatic segmentation of medical ultrasound image using self-creating and organizing neural network.

Journal of electronics.1999,21(1),pp.124-127.

(4)

_______________________________________________________________________________________________

[26] Celso T. N.Suzuki, Jancarlo F. Gomes, Alexandre X. Falc˜ao, Jo˜ao P. Papa‖, and Sumie Hoshino- Shimizu, “Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 60, NO. 3, MARCH 2013

[27] Y. Zhang, H. Qu, Y. Wang,“Adaptive Image Segmentation Based on Fast Thresholding and Image Merging”, Artificial reality and Telexistence-Workshops, pp. 308-311, 1994.

[28] H. Zhang, J. E. Fritts, S. A. Goldman,“Image Segmentation Evaluation: A Survey of

unsupervised methods”, computer vision and image understanding, pp. 260-280, 2008

[29] H. Zhang, J. E. Fritts, S. A. Goldman,“Image Segmentation Evaluation: A Survey of unsupervised methods”, computer vision and image understanding, pp. 260-280, 2008.

[30] P. Karch, I. Zolotova,“An Experimental Comparison of Modern Methods of Segmentation”, IEEE 8th International Symposium on SAMI, pp. 247-252, 2010.

[31] F .Z. Kettaf, D. BI, J. P.,“A Comparison Study of Image Segmentation by Clustering Technique”, Proceedings of ICSP, pp. 1280-1282, 1996.

