IJEEE, Vol. 1, Spl. Issue 1 (March 2014)
e-ISSN: 1694-2310 | p-ISSN: 1694-2426
Improved Segmentation Technique for Enhancement of Biomedical Images Abhishek Thakur1, Rajesh Kumar2, Amandeep Bath3, Jitender Sharma4 1,2,3,4
Electronics & Communication Engineering Department, Indo Global College of Engineering, Punjab, India
1 abhithakur25@gmail.com, 2errajeshkumar2002@gmail.com, amandeep_batth@rediffmail.com, 4er_jitender2007@yahoo.co.in
3
Abstract- The aim of this paper is to develop a fast and reliable segmentation method to segment the haemorrhage region from brain CT images. To calculate area of segmented hemorrhage region that could be useful for physicians or researchers involved in the treatment or investigation of intracranial brain haemorrhage. Thus improving the machine generated automated results and reducing the human effort for better segmentation and saving vital time for the treatment of a patient. Keywords- Magnetic resonance imaging (MRI) and Computed tomography (CT) scan also known as CAT (Computer Axial Tomography), IV’s (Intravenous therapy). I. INTRODUCTION Magnetic resonance imaging (MRI) and Computed tomography (CT) scan also known as CAT (Computer Axial Tomography) scan, are the two main ways by which physicians take a picture of brain. In CT scan, testing is fast and results are quick and thus making it exceptionally valuable when prompt diagnosis and treatment are critical. CT scan can be taken while patient is hooked up to IV’s (Intravenous therapy) or other medical equipment, unlike some other scanning methods. CT scans can disclose hematomas, hemorrhages, and skull fractures and thus providing exact information to neurologist, necessary for deciding whether emergency treatment is required. An MRI process can take about 30-45 minutes to complete while a CT scan may only take 5 to 10 minutes. So, a severe hemorrhage could kill patient in the time consumed to take pictures in MRI machine. Further, in some situations a CT scan can actually detect abnormalities more easily than an MRI like a CT scan is good at detecting acute haemorrhage and problems in bone, for example fractures. On the other hand, an MRI is best at detecting small or subtle lesions. CT scans deliver a relatively high dose of radiation to a patient in comparison to other diagnostic tests. This is not usually a problem for a single scan, but patients who need to undergo repeated tests can be subjected to a significant level of radiation, hence increasing their cancer risk. MRI makes use of powerful magnetic fields and the magnetic reaction of the body cells to construct crosssectional images is similar to CT scans. MRI does not use Xrays, so it can be safer than CT if multiple imaging sessions are expected. The variations of MRI technology can also examine brain functioning and identify injuries which are International Journal of Electrical & Electronics Engineering
4
not visible in CT scans. But even the detail available using MRI cannot detect mild concussions. In acute head injury cases, MRI is not often used. MRI has some drawbacks, although MRI images yield finer detail than CT scans. Some drawbacks include it takes longer to perform, it is not as readily available as a CT scanner in most hospitals, it is not practical for patients hooked up to medical equipment and it cannot be used if patient has metal embedded anywhere in the body. The greatest danger of an MRI is to those with metal in their bodies that could be moved around or heated up by the powerful magnetic force created by MRI machine. MRI scans also require that a patient stay very still for a long period of time, which may be difficult if a patient is confused or fidgety. Each type of scan is susceptible to different kinds of artefact i.e. blurring of the image. The definitive tool for accurate diagnosis of an intracranial hemorrhage is CT scan i.e. computed tomography as shown in fig1.1. Typically computed tomography scanning of head is used to detect infarction, tumors, calcifications, hemorrhage and bone trauma. Head CT is the mainstay of diagnosis in ICH. Acute bleeding appears hyper dense (whiter) on a CT, relative to the surrounding tissues as shown in figure:
Fig.1 CT scan of a spontaneous intracranial hemorrhage
Image segmentation is the process of partitioning an image into different segments. These segments often correspond to different tissue classes, organs, pathologies, or other biologically relevant structures in medical imaging. In medical image analysis, one fundamental problem is image segmentation which identifies the boundaries of objects such as organs or abnormal regions like tumors in images. Due to noise, low contrast and other imaging ambiguities medical image segmentation becomes difficult. It is possible with the segmentation results to have shape analysis, detecting volume change, and making a precise radiation therapy treatment plan. Segmentation is a low-level operation, which is www.ijeee-apm.com
necessary in order to perform high-level operations like analysis of shape and size of the organs, 3-D volume visualization and other such operations. In image processing, image segmentation techniques are considered a critical operation because further process steps have to rely on the segmentation results. Two principal ways exists in order to perform segmentation. First one is manual segmentation which is performed by medical experts. In this case medical expert has to manually outline region of interest using a pointer device, usually mouse. Another way is to perform as much as possible of the segmentation automatically and the whole process is performed by means of a segmentation algorithm with minimal user interaction. Some of the segmentation techniques include shape based segmentation and interactive segmentation. In shape based segmentation many methods parameterize a template shape for a given structure, often relying on control points along the boundary. The entire shape is then deformed to match a new image. Two of the most common shape-based techniques are active shape models and active appearance models. These methods have been very influential and have given rise to similar models. On the other hand, interactive methods are useful when clinicians can provide some information like a seed region or rough outline of the region to segment. Further, an algorithm can then iteratively refine such segmentation with or without guidance from the clinician. Manual segmentation, using tools such as a paint brush to explicitly define the tissue class of each pixel, remains the gold standard for many imaging applications such as radio and telecommunications. II. TECHNIQUES FOR IMAGE SEGMENTATION Approaches of image segmentation can be classified according to both the features and the type of techniques used. The features include pixel intensities, edge information, and texture etc. There exist several common approaches on medical image segmentation. However, multiple techniques are often used in conjunction with one another for solving different segmentation problems. 1.
Thresholding methods: The Segmentation algorithms are based on one of two basic properties of intensity values discontinuity and similarity. Discontinuity includes partitioning an image based on abrupt changes in intensity, such as edges in an image. Similarity includes partitioning an image into regions that are similar according to predefined criteria. Threshold segmentation techniques can be grouped in different classes which includes local techniques that are based on the local properties of the pixels and their neighbourhoods. Another are global techniques to segment an image on the basis of information obtain globally such as by using image histogram; global texture properties. Last one split merge and growing techniques use both the notions of homogeneity and geometrical proximity in order to obtain good segmentation results [7]. www.ijeee-apm.com
The gray levels of pixels belonging to the object are different from the gray levels of the pixels belonging to the background in many applications of image processing. Therefore, thresholding becomes a simple but effective tool to separate objects from the background. Thresholding operation outputs a binary image whose one state will indicate the foreground objects while the complementary state will correspond to the background. On the basis of an application, the foreground can be represented by gray-level 0 i.e. black and the background by the highest luminance i.e. 255 in 8-bit images, or conversely the foreground by white and the background by black. It is one of the important approaches to image segmentation. Often an image histogram is used to determine the best setting for the threshold. A thresholded image is defined as:
Below is an example of image on which threshold is applied.
Fig.2: Thresholding method a. Original CT scan brain image b. Brain image after thresholding Thresholding is a simple yet often effective means for obtaining segmentation in images. Many times thresholding is used as an initial step in a sequence of image processing operations. Some of its main limitations includes that in its simplest form only two classes are generated and it cannot be applied to multi-channel images. Also, thresholding typically does not take into account the spatial characteristics of an image. Due to this it becomes sensitive to noise and intensity inhomogeneities, which can occur in images like MRI. Due to both these artifacts the histogram of image is corrupted, making separation more difficult. There are single and multiple thresholds as shown:
Fig. 3: Gray level histograms that can be partitioned as a. single threshold b. multiple thresholds Thresholding can be viewed as: T = T[ x, y, p(x, y), f(x, y) ] Here, T stands for the threshold. f (x, y) is the gray value of point (x, y) and p(x, y) denotes some local property of the International Journal of Electrical & Electronics Engineering
5
point like the average gray value of the neighborhood centered on point (x, y) . Based on above equation thresholding techniques can be mainly divided into global, local, and dynamic thresholding techniques. When T = T [ f(x, y) ] then threshold is global. For T = T[ p(x, y), f(x, y) ] threshold is local and when T = T[ x, y, p(x, y), f(x, y) ] then threshold is dynamic or adaptive. There are a number of global thresholding techniques like minimum thresholding, Otsu, optimal thresholding, histogram concave analysis, iterative thresholding, entropy-based thresholding and so on. Similarly the Main local thresholding techniques are simple statistical thresholding, 2-D entropy-based thresholding, histogram-transformation thresholding etc. 2.
Region growing methods: Region growing method is region based image segmentation. It involves the selection of initial seed points therefore also a pixel-based image segmentation method. This method examines neighbouring pixels of initial seed points and then determines whether the pixel neighbours should be added to the region. Basically it involves to start from some pixels (seeds) representing distinct image regions and to grow them, until they cover the entire image. This method needs a rule describing a growth mechanism and a rule checking the homogeneity of the regions after each growth step. This method for extracting a region of the image that is connected based on some predefined criteria that can be based on intensity information and/or edges in the image. The simplest form of region growing requires a seed point that is manually selected by an operator, and extracts all pixels connected to the initial seed with the same intensity value. Same as thresholding, region growing is not often used alone but within a set of image processing operations, particularly for the delineation of small and simple structures such as tumors and lesions. Its primary disadvantage is that it requires manual interaction to obtain the seed point. Hence, a seed must be planted for each region that needs to be extracted. Figure showing region growing method effect is as shown.
Fig. 4: Region growing method a. Original CT scan brain image b. Image after region growing method applied on original image The Split and merge algorithms are related to region growing but do not require a seed point. Region growing techniques can also be noise sensitive that causes extracted regions to have holes or even become disconnected. The partial volume effects can also cause separate regions to become connected. International Journal of Electrical & Electronics Engineering
6
3. Classifiers: The classifier methods are pattern recognition techniques. These methods seek to partition a feature space derived from the image using data with known labels. Classifiers are known as supervised methods because of requirement of training data that are manually segmented and then used as references for automatically segmenting new data. Number of ways exists in which training data can be applied in classifier methods. The nearest-neighbor classifier is a simple classifier, where each pixel or voxel is classified in the same class as the training datum with the closest intensity. A generalization of this approach is k nearest neighbor (kNN) classifier where the pixel is classified according to the majority vote of the k closest training data. This classifier is considered a nonparametric classifier as it makes no underlying assumption about the statistical structure of the data. Maximum likelihood (ML) or Bayes classifier is a commonly-used parametric classifier. 4. Clustering methods: Clustering algorithms essentially perform the same function like classifier methods without the use of training data. Therefore, they are termed unsupervised methods. To compensate for the lack of training data, clustering methods iterate between segmenting the image and characterizing the properties of the each class. In other words, clustering methods train themselves using the available data.
Fig. 5: Colouring of squares into three clusters representing results of cluster analysis. Some typical cluster models include: 1. Connectivity models: Example is hierarchical clustering that builds models based on distance connectivity. 2. Centroid models: Example for these models includes the kmeans algorithm which represents each cluster by a single mean vector. 3. Distribution models: In this, clusters are modelled using statistical distributions, such as multivariate normal distributions used by the Expectation-maximization algorithm (EM). The commonly used clustering algorithms are the K-means or ISODATA algorithm, the fuzzy c-means algorithm and the expectation-maximization algorithm. The K-means clustering algorithm clusters data by iteratively computing a mean intensity for each class and segmenting the image by classifying each pixel in the class with the closest mean. The result of applying the K-means algorithm to a slice of a MR
www.ijeee-apm.com
brain image is shown in figure below. Cerebrospinal fluid, gray matter and white matter regions are there.
perceptron and unsupervised techniques such as pulse coupled neural network (PCNN).
Fig. 6: Segmentation of MRI brain image a. Original brain MRI b. Segmentation using K-means algorithm Clustering algorithms do not require training data, but they do require an initial segmentation or initial parameters. 5. Markov random field models: Markov random field modelling itself is not a segmentation method but a statistical model which can be used within segmentation methods. In medical imaging, they are typically used to take into account the fact that most pixels belong to the same class as their neighbouring pixels. Markov random field are often incorporated into clustering segmentation algorithms like the K-means algorithm under a Bayesian prior model. These models have difficulty of proper selection of the parameters controlling the strength of spatial interactions. Too high a setting can result in an excessively smooth segmentation and a loss of important structural details. Further, computationally intensive algorithms are required by MRF methods. But in spite of this, MRFs are widely used not only to model segmentation classes, but also to model intensity in homogeneities that can occur in MRI images and texture properties.
Fig. 7: Segmentation of MRI brain image a. Original brain MRI b. Segmentation using K-means algorithm c. Segmentation using K-means algorithm with MRF prior. 6. Artificial neural networks: Artificial Neural Networks are electronic models based on the neural structure of the brain. ANN is capable of machine learning and pattern recognition. These are presented as systems of interconnected neurons that can compute values from inputs by feeding information through the network. Neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision. Neural network based image segmentation techniques include supervised techniques such as feed-forward neural network, Multilayer
www.ijeee-apm.com
Fig. 8: Circular nodes represent artificial neurons and arrows represent input output connections from one neuron to other neurons 7. Deformable models: Deformable models are physically motivated, model-based techniques. These are used for delineating region boundaries using closed parametric curves or surfaces that deform under the influence of internal and external forces. To delineate an object boundary in an image, a closed curve or surface must first be placed near the desired boundary. It is then allowed to undergo an iterative relaxation process. The internal forces are computed from within the curve or surface to keep it smooth throughout the deformation and external forces are usually derived from the image to drive the curve or surface towards the desired feature of interest. Advantages of deformable include their ability to directly generate closed parametric curves or surfaces from images and their incorporation of a smoothness constraint that provides robustness to noise and spurious edges. Disadvantage include requirement of manual interaction to place an initial model and choose appropriate parameters. The standard deformable models can also exhibit poor convergence to concave boundaries. This difficulty can be reduced to some extent through the use of pressure forces and other modified external force model. 8. Atlas guided approaches: For medical image segmentation atlas-guided approaches are a powerful tool when a standard atlas or template is available. The atlas is generated by compiling information on the anatomy that requires segmenting and then used as a reference frame for segmenting new images. The atlas-guided approaches are similar to classifiers except they are implemented in the spatial domain of the image rather than in a feature space. The standard atlas-guided approach treats segmentation as a registration problem. Firstly it finds a one International Journal of Electrical & Electronics Engineering
7
to one transformation that maps a pre-segmented atlas image to the target image that requires segmenting. This process is often known as atlas warping. These approaches have been applied mainly in MR brain imaging. Advantages include that labels are transferred as well as the segmentation. III. RESULTS AND DISCUSSION In this paper, algorithm for segmentation and area calculation of intracranial brain hemorrhage from CT scan images has been implemented in matlab R2011b. All the images has dimension of 512 x 512. Tests were performed on 11 human brain CT scan hemorrhage images. The segmentation results are as shown in figures below. The percentage of correct classification (PCC) and computational time results are also shown in table 4.
Binary image after bwlabel morphological operation is shown. bwlabel has syntax : [L,num] = bwlabel(f,conn) which returns a matrix L, of the same size as BW, containing labels for the connected objects in BW. conn can have a value of either 4 or 8, where 4 specifies 4-connected objects and 8 specifies 8-connected objects. L is the label matrix, num gives total number of connected components and is optional. f is input binary image and conn parameter has default value of 8. The elements of L are integer values greater than or equal to 0. The pixels labelled 0 are the background. The pixels labelled 1 make up one object, the pixels labelled 2 make up a second object, and so on. d) Skull portion Binary image: The skull portion comprises of white pixels i.e. 1’s. Basically this skull portion has to be removed in order to get only intracranial region (ROI). e) Holes filled image: Holes filling is done using imfill morphological operation. imfill fills image regions and holes. Image after holes filling is shown in e. It has syntax BW2 = imfill(BW,'holes') which fills holes in the binary image BW. Basically, a hole is a set of background pixels that cannot be reached by filling in the background from the edge of the image. f) Image after logical operations Image after logical operations & and ~ is shown. & returns 1 for every element location that is true means nonzero in both arrays and 0 for all other elements. ~ complements each element of the input array.
Fig. 8: a. Original brain hemorrhage CT scan, b. Binary image, c. Binary image after bwlabel, d. Skull portion Binary image, e. Holes filled image, f. Image after logical operations, g. Image in which extra pixels are removed by morphological operation, g. Image in which extra pixels are removed by morphological operation, h. Extracted ROI image (Intracranial), i. Sobel edge operator segmented image. a) Original brain hemorrhage CT scan: The original image of brain hemorrhage CT scan is showing hemorrhage region which is appearing white in the center. Grey matter is the brain region. The outermost white region surrounding gray matter is skull. b) Binary image The binary image has pixel values in 1’s and 0’s. The white region pixels correspond to 1’s and black region pixels correspond to 0’s. c)
Binary image after bwlabel
International Journal of Electrical & Electronics Engineering
8
g) Image in which extra pixels are removed by morphological operation The small objects in the image background are removed by bwareaopen morphological operation. bwareaopen remove small objects from binary image. It has syntax: BW2 = bwareaopen(BW, P). It removes from a binary image all connected components (objects) that have fewer than P pixels, producing another binary image, BW2. This operation is known as an area opening. h) Extracted ROI image (Intracranial): Extracted ROI image The extracted ROI image is shown with removed background pixels and skull portion. In other words, the image is left with ROI (intracranial) portion and the segmentation is applied on this image further. IV. CONCLUSION AND FUTURE WORK In this work sub-blocking rule based criteria is used for intracranial hemorrhage segmentation in CT brain images and also hemorrhage area is calculated. The CT scan brain images with hemorrhage (ICH) are taken for segmentation. This segmentation method used has main advantage of fast the processing speed and better results for percentage of correct classification with less noise in processed images. Except one case, in rest all cases, the algorithm segments hemorrhage.
www.ijeee-apm.com
However, in some cases the segmentation is not completely exact. Practically, the issue is very difficult of avoiding and until now, a perfect automatic segmentation algorithm does not exist. For such a reason, to analyse the performance of algorithm, a comparison with manual segmentation (ground truth) is done. The achievement of better results lies in the use of sub-blocking rule based criteria for the segmentation rather than multilevel otsu thresholding method. The Otsu thresholding does not claim to be the best automatic thresholding ever and can be extended to a multilevel thresholding which results in segmentation. Thresholding is a technique often applied to image segmentation with a basic objective to classify the pixels of a given image into two classes i.e. those pertaining to an object and those pertaining to the background. In case of an image with clear objects in the background, the bi-level thresholding method can easily divide the object from the background. On the other hand, to segment complex images a multilevel threshold method is required. The multilevel thresholding method segments the pixels into several distinct groups in which the pixels of the same group have gray levels within a specific range. However, when the thresholding method is extended to multi-level thresholding, the computation time grows exponentially with the number of thresholds. Comparison of Sub-blocking rule based criteria and MLSA (Multi-level local segmentation approach) in terms of average computational time and average PCC is shown in table 1. Table1. Comparison of Sub-blocking rule based criteria and MLSA (Multi-level local segmentation approach) in terms of average computational time and average PCC Methods Average Average Computational PCC (%) Time (seconds) 0.986 Sub blocking rule 0.10 based ctiteria 0.17 0.971 MLSA The Sub-blocking rule based criteria include k-means clustering in addition to sobel edge and weighed sum method. The MLSA (Multi-level local segmentation approach) includes multilevel otsu thresholding method. Table6. shows that obtained results for proposed algorithm are better in comparison to MLSA method. The future work will focus to further improve the results using more image datasets of medical images and other robust image segmentation techniques. A combination of different methods may be applied to obtain a complete effective and robust solution for segmentation. By using the advantage of each method the segmentation results can be improved. REFERENCES [1] A comparative performance evaluation of various approaches for liver segmentation from SPIR images; Evgin Göçeri, Mehmet Zübeyir Ünlü and Oğuz Dicle; Available at: http://online.journals.tubitak.gov.tr/ www.ijeee-apm.com
openAcceptedDocument.htm?fileID=290786&no=63245 , pp. 144 [2] Cerebrovascular Disease: Revised Imaging Guidelines from the American College of Radiology; Available at: http://www.eradimaging.com/ site/article.cfm?ID=779. [3] Evaluation of Image Segmentation; Simon K. Warfield, Ph.D.; Computational Radiology Laboratory Harvard Medical School; Available at: http://www2.imm.dtu.dk/projects/sparse/iceland-warfield-evalsegmentation.pdf , pp. 1-46 [4] Morphological Image Processing Approach On The Detection Of Tumor And Cancer Cells; Ms. M. Parisa Beham, Ms.A.B.Gurulakshmi; IEEE 2012 [5] Comprehensive Applying Watershed Algorithm in Segmentation of CT Brain Images; ZHU Bing-li, Xiong Jiang, Tan Xiao-ling; 2011 IEEE, pp. 81-83 [6] Intracranial Hemorrhage Annotation for CT Brain Images; Tong Hau Lee, Mohammad Faizal Ahmad Fauzi , Su-Cheng Haw; Proceeding of the International Conference on Advanced Science, Engineering and Information Technology 2011, pp. 689-693 [7] A novel intuitionistic fuzzy approach for tumour/hemorrhage detection in medical images; Tamalika Chaira and Sneh Anand; Journal of Scientific & Industrial Research Vol. 70, June 2011, pp. 427-434 [8] Hematoma volume detection and estimation from CT images; V. SĂCELEANU, R. BRAD, A. BARGLAZAN, M. PEREANU; AMT, vol. II, nr. 3, 2011, pp. 298-301 [9] Qualitative and Quantitative Comparisons of Haemorrhage Intracranial Segmentation in CT Brain Images; W. Mimi Diyana W. Zaki; Tencon 2011, pp. 367- 373 [10] An Algorithm for Automatic Segmentation of Spontaneous Cerebral Hemorrhages; R. Rodríguez Morales; Claib 2011, pp. 1-4 [11] Comparative Study of Adaptive Network-Based Fuzzy Inference System (ANFIS), k-Nearest Neighbors (k-NN) and Fuzzy c-Means (FCM) for Brain Abnormalities Segmentation; Noor Elaiza Abdul Khalid, Shafaf Ibrahim, Mazani Manaf; INTERNATIONAL JOURNAL OF COMPUTERS Issue 4, Volume 5, 2011, pp. 513- 524 [12] Medical Image Segmentation Based on Contourlet Transform and Watershed Algorithm; Hongying LIU, Yi LIU, Qian LI, Hongyan LIU, Yongan TONG; 2011 IEEE, pp. 224-227 [13] A Novel Anatomical Structure Segmentation Method of CT Head Images; Xiaojun Zang, Jian Yang, Dongdong Weng, Vue Liu, Yongtian Wang; The 2010 IEEE/ICME International Conference on Complex Medical Engineering July 13-15,2010, Gold Coast, Australia, pp. 316-320 [14] A novel method of CT brain images segmentation; Xiaojun Zang, Yongtian Wang, Jian Yang, Yue Liu; 2010 International Conference of Medical Image Analysis and Clinical Application (MIACA), pp. 109-112 [15] Multi-dimensional Data Analysis of Intracerebral Hemorrhage from CT Images; Jianmin Dong, Fangxia Shi; 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010), pp. 406-409 [16] Fuzzy expert system for edema segmentation; Sven Lencaric; INTERNATIONAL JOURNAL OF COMPUTERS Issue 3, Volume 5, 2010, pp. 311- 317 [17] A Fast and Noise-Adaptive Rough-Fuzzy Hybrid Algorithm for Medical Image Segmentation; Arpit Srivastava, Abhinav Asati, Mahua Bhattacharya; 2010 IEEE International Conference on Bioinformatics and Biomedicine, pp. 416-421 International Journal of Electrical & Electronics Engineering
9
AUTHORS First Author– Abhishek Thakur: M. Tech. in Electronics and Communication Engineering from Punjab Technical University, MBA in Information Technology from Symbiosis Pune, M.H. Bachelor in Engineering (B.E.- Electronics) from Shivaji University Kolhapur, M.H. Five years of work experience in teaching and one year of work experience in industry. Area of interest: Digital Image and Speech Processing, Antenna Design and Wireless Communication. International Publication: 7, National Conferences and Publication: 6, Book Published: 4 (Microprocessor and Assembly Language Programming, Microprocessor and Microcontroller, Digital Communication and Wireless Communication). Working with Indo Global College of Engineering Abhipur, Mohali, P.B. since 2011. Email: abhithakur25@gmail.com Second Author – Rajesh Kumar is working as Associate Professor at Indo Global College of Engineering, Mohali, Punjab. He is pursuing Ph.D from NIT, Hamirpur, H.P. and has completed his M.Tech from GNE, Ludhiana, India. He completed his B.Tech from HCTM, Kaithal, India. He has 11 years of academic experience. He has authored many research papers in reputed International Journals, International and National conferences. His areas of interest are VLSI, Microelectronics and Image & Speech Processing.
International Journal of Electrical & Electronics Engineering
10
Third Author – Amandeep Batth: M. Tech. in Electronics and Communication Engineering from Punjab Technical University, MBA in Human Resource Management from Punjab Technical University , Bachelor in Technology (B-Tech.) from Punjab Technical University . Six years of work experience in teaching. Area of interest: Antenna Design and Wireless Communication. International Publication: 1, National Conferences and Publication: 4. Working with Indo Global College of Engineering Abhipur, Mohali, P.B. since 2008. Email: amandeep_batth@rediffmail.com Fourth Author – Jitender Sharma: M. Tech. in Electronics and Communication Engineering from Mullana University, Ambala, Bachelor in Technology (B-Tech.)from Punjab Technical University . Five years of work experience in teaching. Area of interest:, Antenna Design and Wireless Communication. International Publication: 1 National Conferences and Publication:6 and Wireless Communication). Working with Indo Global college since 2008. E-mail: er_jitender2007@yahoo.co.in
www.ijeee-apm.com