Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
A Survey on Image Segmentation and its Applications in Image Processing Kamaljeet Kainth1, Amritpal Singh2, Priya Chitkara3 1,2,3 1
Dept. of ECE, Guru Nanak Dev University Regional Campus, Jalandhar, India
kainth108@gmail.com, 2asdhunna91@gmail.com, 3chitkara23@gmail.com
Abstract: As technology grows day by day computer vision becomes a vital field of understanding the behavior of an image. Image segmentation is a sub field of computer vision that deals with the partition of objects into number of segments. Image segmentation found a huge application in pattern reorganization, texture analysis as well as in medial image processing. This paper focus on distinct sort of image segmentation techniques that are utilized in computer vision. Thus a survey has been created for various image segmentation techniques that describe the importance of the same. Comparison and conclusion has been created within the finish of this paper. Keywords: Image segmentation, Image processing,Fuzzy-C Means (FCM) ,Edge based segmentation.
I. INTRODUCTION Image segmentation plays an important role in computer vision. Pattern recognition, texture analysis, medical image processing, facial detection and many security system deployed segmentation technique. These segmentation techniques are broadly classified into following categories, Edge based segmentation, Fuzzy based detection, PDE based segmentation, Region based segmentationThreshold based segmentation .Edge detection is one of the major tool for image segmentation. Edge detection is the approach for detecting significant discontinuities in intensity values. In gray level main discontinuities are point, lines and edges. In order to find the discontinuities these techniques utilize are Robert edge detection, Log Gabor edge detection [1], Sobel edge detection, Prewitt edge detection and canny edge detection. An image is segmented on the basis of gray level discontinuities which are present on the boundary the image [2].Fuzzy based detection applies the principle of clustering. Clustering is the process in which similar objects are grouped into one cluster. Fuzzy clustering is one step ahead to hard (crisp) clustering and mainly employed for acquiring fuzzy patterns. Fuzzy clustering is further categorized into fuzzyc means (FCM) and fuzzy kernel c-means algorithms (KFCM) [3]. In FCM distance between data points are considered to form a cluster and on the basis of this cluster centers are created [4]. However KFCM overcomes the disadvantage of FCM and kernel information is added to FCM, to cover small differences between clusters. Partial differential equation (PDE) based segmentation is an efficient technique for image segmentation. PDE based model is a geometrical active counter model which utilize fuzzy classification and can handle variation in topology of shape [5]. This model overcomes the disadvantages of fuzzy based detection hence a better option for image segmentation. Region based segmentation employ the approach of dividing the image into small regions. These regions are created on the basis of color, texture, intensity 83
value. The segmented regions are assigned with a label. Region based segmentation uses region transformation or histogram method. This approach provides advantage of continuous segmentation maps of closely related objects [6]. Threshold values for detecting corresponding area are enforced for efficient segmentation.Threshold based segmentation method is the simplest method for segmentation. In this method threshold values are obtained by histogram of the edges of the image. Global thresholding, variable thresholding and multiple thresholding technique are employed to select the threshold value. A threshold selection component is defined to select the threshold value which is automatically updated according to the contrast of the edges detected. But this technique has a disadvantage that this cannot be applied to complex images as partitioning of pixels is difficult [7].This paper will survey all the techniques employed for segmentation purposes. II. LITERATURE SURVEY Shinn-Ying Ho and Kual-Zheng Lee[8]. , proposed a novel approach which satisfies the basic objectives of image segmentation i.e. an algorithm must have an efficient segmented contour , computation time must be very small without setting any threshold and segmented regions should be segmented robustly. This efficient technique is termed as evolutionary image segmentation algorithm. This technique employ K-means algorithm which is utilized to split an image into several homogeneous regions. This particular algorithm also overcomes the issues which are faced by traditional image segmentation techniques like, over segmentation, continuous contour. The overall algorithm is split into two major categories named as split procedure and merge procedure respectively. First one groups the pixels of small region into the larger adjacent region. Second one utilized binary chromo sense channel encoding and further dived into seven steps to achieve the above mention objectives. The whole scenario reduces the computation time, improves the quality of splinted image and hence results into efficient robust segmentation techniques in comparison to traditional segmented technique.Various threshloding techniques have been developed for image segmentation in [9]. H.D. Cheng et.al in [10], presented an image segmentation technique which is based on homogram thresholding in conjunction with region merging technique. First one consider local and global information whereas later one finds peaks of homogram .Region merging gives free space and spatial relation between pixels. Further this paper compare histogram thresholding approach their approach which proves that afterward one estimate more information about gray level NITTTR, Chandigarh
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Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
of an image and also provides spatial information. On the other hand histogram thresholding analyze only spatial dependencies amongst pixels when entropy is calculated .Hence present approach is much effective and provides better segmentation results. Mahmoud R. Rezaee et.alin [11], suggested an invincible segmentation technique which employ pyramid image segmentation followed by fuzzy c-means (FCM) clustering algorithm. First of all the image is segmented into distinct regions by pyramid segmentation algorithm [8], which obtain global view of an image representation and also acquires location of all the objects in the image. Afterward fuzzy c- means clustering algorithm [13]performs feature analysis, shape analysis and clustering. Authors claims that this combined algorithm have several advantages over traditional image segmentation techniques which are listed as: a) cut downs computational overhead, b) can be enforced to N-dimension images, c) protect information; d) segmented image is employed to fuzzy system.Shijuan He, et.al in [14] introduces a MRI image segmentation algorithm established on histogram and fuzzy c-means techniques. This algorithm is dived into sub steps i.e. a) pre-processing has been done to remove extracranial tissue to develop brain image, b) initial centroids, c) connectivity based segmentation for leveling of homogenous regions of an image. This integrated algorithm overcomes the issue like overlapping of intensity distribution, partial volume effect. Apart of great advantages the overall algorithm enhances the complexity of the overall algorithm.Gour C. Karmakar and Laurence S. Dooley in [15] introduced a novel generic fuzzy rule based image segmentation (GFRIS) which overcomes the issue of previous segmentation technique defined in [6] FCM is sensitive to parameter value selection. This algorithm is overcomes this issue and manipulate its advantages to determine inter pixel spatial relation. This algorithm divided into major six steps elaborated as i) classification of pixels ii) deduce the threshold and key weight iii) arrange center of regions, iv) calculation of membership function , v) employ fuzzy role to compute pixel into region ,vi) classify the pixel and return to step (iv) if it fails to do so . Further comparison has been made among FCM, possibilistic c-means and GFRIS. Hence it controls the maximum permitted pixel intensity variation and enhances the inherent concurrency. Ying-Tung Hsiao et.al in [16] proposed a novel approach for segmentation of graph based images. They utilized morphological edge detection technique (MED) with another technique named as region growing or integrated version of both. Erosion and dilution morphological operators an applied also known as shirking and expansion operators respectively. Later region growing technique [17] is employed. After applying mathematical closing region merging technique is applied. The whole scenario decreases the noise and as a result it enhances the edge features. Also it reduces the redundant regions which do not have adequate edge points between them to cut down the overall cost.Anastasia Sofou et.al in [18], presented an image segmentation technique which is based on morphological Partial Differential Equation (PDE) along with watershed transom segmentation [15] to analyze intensity contrast and region size criteria of an image. Further this algorithm analyzes the modulation texture features with multiple cues. Since our main goal is to NITTTR, Chandigarh
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e-ISSN: 1694-2310 | p-ISSN: 1694-2426
examine segmentation techniques used in segmentation we did not further analyze the paper as it is more concerned about the feature analysis. However, authors claimed that to have better segmentation result by employing multiple cues rather than utilizing single cue. Anil K. Jain and Michael E. Farmer in [20], provides an alternative segmentation method top overcome the issues faced by conventional segmentation algorithms. Conventional algorithms employs segmentation accompanied by classification hence confronts some issues defined as: i) there is no prior knowledge about the object which is to be extracted; ii) there are no proper metrics which are previously defined for comparison of image segmentation algos ad iii) all the techniques are failed to adapt real world challenges. Hence they proposed a new method which combines segmentation and classification in one step hence named as wrapper method [21] in conjunction with filter method. This method performs image segmentation, feature extraction and classification in one go and hence turn s out to be an algorithm which is much effective and resolves all the above issues.During survey we found an exciting paper which actually demonstrating the edge based segmentation technique on a hardware setup. Krzysztof Strzecha et.al in [21] presents a computerized system for the measurement of temperature (high) of superficial properties. Before this most of the methods which measures surface properties need operators to take observation and make a record of it. But this technology automates the process for the measurement of various surface quantities values. Jinsheng Xiao et.al in [22], introduces a nonlinear PDE for the processing of gray scale images. Conventional morphological images detect edges for the segmentation but lags due to sensitive gradient operators. Hence before segmentation, smoothening of an image is required to overcome this problem. But also liner something of an image results into blurred edge of the segmented image. So the proposed nonlinear discontinue PDE method can solve this issue hence provide better results for segmentation of morphological gray scale images. Hui Zhang et.al in [23] , presents survey on the unsupervised methods of image segmentation. As in [18] it is stated that supervised methods needs an observer to take care of all records and data which is not an efficient method. Hence there is need of unsupervised methods which automate this sector. Hence this paper elaborate about the various supervised method, system level method [24], analytical method [25] and unsupervised methods. Hence they conclude that proposed method is much effective than the supervised method and also overcome its issues which are mentioned above.Wen-Xiong Kang, et.al in [25], provides a base for the thorough study of image segmentation and its techniques along with the benefits and imperfections. They focused on segmentation as well as evaluation techniques for the same .They concern about conventional segmentation algorithms that are edge based, region based and special theory based. Furthermore, they covered evaluation techniques which are analytical [26] and experiment techniques [27] respectively. Analytical techniques measure the segmentation algorithm by examining the principle of algorithm, whereas later one is employed to comparison of results which are obtained by various experiments on the image. Hence this paper delivers a comparison between all segmentation techniques 84
Int. Journal of Electrical & Electronics Engg.
Vol. 2, Spl. Issue 1 (2015)
and their future use. Siti N. Sulaiman et.al in [28] proposed a novel algorithm which is not restricted only up to specific images rather one can do real world image segmentation, i.e. segmentation of images which are captured by any CCTV camera, digital cameras etc. can be possible. Han Lili et.al in [29] proposed an image segmentation algorithm for medical images. They utilized edge segmentation with prewitt operator and Hough transform. They extract contour of objects from medical images. Hence this is an efficient way to segment medical images and extract objects. For example, it can be tissues of heart. Image restoration is also a major application of the image processing. M. Erdt et.al in [30] presents a dual technique of medical image segmentation and restoration.S. Sridevi and Dr. M. Sundaresan, in [31] explored a new area of image segmentation i.e. segmentation of Ultrasound images. Ultrasound imaging is also termed as sonography and used to test tissues of a human body. TABLE I: Comparison of various segmentation techniques S.No.
Segmentation technique Edge based[29]
1.
Advantages
Disadvantages
Easy to detect edges and their orientation
Sensitive to noise and inaccurate
2.
Region based[30]
Easy to detect small regions
Some tolerance is required.
3.
Threshold based[28]
4.
Fuzzy based[31]
Technique work well even in presence of noise. Able to detect small variations in intensity
Incorrect pixel added to region can’t differentiate properly Not able to differentiate noise
5.
ANN based
Changes in location of centroid provide different results.
6.
Sobel, prewitt edge detection[32]
Does not require prior information of image to segment it. Detection of edges and their orientation is simple Improve S/ N ratio ,
7.
Canny edge detection
Sensitive to noise
Complex computations and time consuming
III. CONCLUSION A survey has been compiled on distinct segmentation approaches. On the basis of survey various advantages and disadvantages has been made in TABLE I. Indeed this field of image processing has vast number of application in computer vision, associated fields and in medical image processing. REFERENCES [1]. Siti N. Sulaiman and Nor A. M. Isa , “Adaptive Fuzzy-K-means Clustering Algorithm for Image Segmentation”,pp.2661-2668, 2010. [2]. Wen-Xiong Kang, Qing-Qiang , Run-Peng Liang, “The comparative research on image segmentation algorithms,” International Workshop on Education Technology and Computer Science, pp. 703-708,2009. [3]. Wen-XiongKang,Qing-Qiang , Run-Peng Liang, “The comparative research on image segmentation algorithms,” International Workshop on Education Technology and Computer Science, pp. 703-708,2009.
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[4]. A.Sofou and P.Maragos, “PDE-based modeling of image Segmentation using volume flooding”, IEEE ICIP, 2003. [5]. Kaganami, H.G., Zoubeiji, “Region-based segmentation versus Edge Segmentation”, Intelligent Information Hiding and Multimedia Signal Processing, pp 1217-1221, 2009. [6]. Shiping Zhu, Xi Xia &et al, “An Image Segmentation Algorithm in Image Processing Based on Threshold segmentation”, IEEE (SITIS), pp. 673-678, 2007. [7]. Claudio Rosito Jung, “Multiscale image segmentation using wavelets and watersheds,” IEEE Symp.2003, Computer Graphics and Image Processing, SIBGRAPI XVI Brazilian Symposium, pp.278–284, 2003. [8]. Shinn-Ying Ho and Kual-Zheng Lee, “An Efficient Evolutionary Image Segmentation Algorithm”, IEEE Conf., pp.1327-1334,2001. [9]. J. E Canny, “A computational approach to edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679698, 1986. [10]. H.D. Cheng , X.H. Jiang and Jingli Wang, “Color image segmentation based on homogramthresholding and region merging” , Pattern Recognition 35, pp. 373-393, (2002) . [11]. Mahmoud R. Rezaee et.al , “A Multiresolution Image Segmentation Technique Based on Pyramidal Segmentation and Fuzzy Clustering” , IEEE Transactions On Image Processing, VOL. 9, NO. 7, pp.12381248, 2000. [12]. R. Pal and S. K. Pal, “A review in image segmentation techniques”, Pattern Recognition, vol. 26, pp. 1277-1294, 1993. [13]. A. Boudraa, “Automated detection of the left ventricular region in magnetic resonance images by Fuzzy C-means model,” International journal. Cardiac Image. VOL. 13, pp. 347–355, 1997. [14]. Shijuan He , Xia Wang and Yamei Yang , “MRI Brain Images Segmentation” , IEEE Conf. , pp. 113-116, 2000 [15]. P. J. Burt, “The pyramid as a structure for efficient computation,” in Multiresolution Image Processing and Analysis, A. Rosenfeld, Ed, Berlin, Germany: Springer-Verslag, pp. 6–35 ,1984 . [16]. Gour C. Karmakar and Laurence S. Dooley , “A generic fuzzy rule based image segmentation algorithm”, Pattern Recognition Letters 23 ,pp. 1215–1227,2003. [17]. Wen-Bing Tao, Jin-Wen Tian, Jian Liu, “Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm” , Pattern Recognition Letters , pp.3069–3078, 2003 . [18]. Y.L. Chang and X. Li, “Adaptive image region-growing,” IEEE Trans. Image Processing, vol. 3, pp. 868–872, 1994. [19]. Anastasia Sofou, GeorgiosEvangelopoulos, and Petros Maragos, “Coupled Geometric And Texture PDE-Based Segmentation”,2005. [20]. L. Vincent and P. Soille, “Watershed in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Transaction, PAMI, pp. 583-598, 1991. [21]. EtyNavon, Ofer Miller and Amir Averbuch, “Color image segmentation based on adaptive local thresholds” , Image and Vision Computing 23 ,pp. 69–85 , 2005 . [22]. Krzysztof Strzecha, Anna Fabijan'ska, DominikSankowski , “Application Of The Edge-Based Image Segmentation”, MEMSTECH'2006, pp.28-31, 2006. [23]. Jinsheng Xiao et.al, “An image segmentation algorithm based on level set using discontinue PDE”, First International Conference on Intelligent Networks and Intelligent Systems, pp. 503-506, 2008. [24]. HuiZhang, Jason E. Fritts and Sally A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods” , Computer Vision and Image Understanding 110 ,pp. 260–280 , 2008 . [25]. Y. Zhang, A survey on evaluation methods for image segmentation, Pattern Recognition 29 (8) ,pp.1335–1346, 1996. [26]. ShitalRaut et.al, “Image Segmentation – A State-Of-Art Survey for Prediction” , International Conference on Advanced Computer Control , pp.420-424,2008 [27]. Brink A. D, “Thresholding of digital images using of two dimensional entropies” ,Pattern Recognition, vol.25 (8), pp.803–808, 1992. [28]. Siti N. Sulaiman and Nor A. M. Isa , “Adaptive Fuzzy-K-means Clustering Algorithm for Image Segmentation”, pp.2661-2668, 2010. [29]. Han Lili, Ma Wanjun and Ning Yi, “The Study of Image Edge Segmentation based on Hough Transform”, 2011. [30]. M. Erdt, S. Steger and G. Sakas, “Regmentation: A New View of Image Segmentation and Registration” , Journal of Radiation Oncology Informatics, pp. 1-23 , 2012. . [31]. S. Sridevi and Dr. M. Sundaresan “Survey Of Image Segmentation Algorithms On Ultrasound Medical Images” ,pp.215-220,2013.
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