Volume-06 , Special Issue-04 , May - 2018 Go Back

The proposed work presents a fully automatic computer-aided diagnosis (CAD) system for magnetic resonance images (MRI) of brain tumor classification. Tumorous slices classification is one of the preprocess steps for brain tumor segmentation and visualization. The proposed work classifies each scan image of MRI volumes into normal or tumorous using block based feature extraction and random forest (RF) classifier. The given image has divided into 8 × 8 non overlapping blocks and extracted three Haralick features such as Energy, inverse difference moment (IDM) and directional moment (DM) from each block. These three extracted features of training blocks are helped to train the RF classifier. The MRI materials used are gathered from multimodal brain tumor segmentation (BraTS 2015) training dataset comprises 274 multisequence MR scans of glioma patients. The experimental results of proposed technique are validated using the measures sensitivity, specificity, accuracy, missed alarm (MA) and false alarm (FA). The average results of the proposed method reached upto 94% of sensitivity, 94% of specificity and 95% of accuracy in BraTS 2015. The error rates measures 1% of slices were missed to identify as tumor and 3% of slices spuriously detected as tumor. The performance of the proposed work was compared with eight existing methods. In summary, the results showed that the proposed method using RF classifier given effective classification for separating normal and tumorous slices from MR brain volumes.

Tumor detection, Random forest, Feature extraction, Classification, BraTS dataset

[1] D.L. Longo, “369 Seizures and Epilepsy Harrison`s principles of internal medicine”, 18th Ed., McGraw-Hill, New York, pp. 3258, 2012.
[2] J.L. Prince, J.M. Links, “Medical Imaging Signals and Systems”, Pearson Prentice Hall, 2nd Edition, 2014.
[3] J.A. Rodger, “Discovery of medical big data analytics: improving the prediction of traumatic brain injury survival rates by data mining patient informatics processing software hybrid hadoop hive”, Informatics in Medicine Unlocked, Vol. 1, pp. 17–26, 2015.
[4] T. Kalaiselvi, P Sriramakrishnan, “Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine”, International Journal of Imaging Systems Technology, pp.1–12, 2018. DOI: 10.1002/ima.22267
[5] P.M. Krishnammal, SS Raja, “Automated Brain Image classification using Neural Network Approach and Abnormality Analysis”, International Journal of Engineering and Technology (IJET), Vol. 7, No.3, pp.876-886, 2015.
[6] N. Rajalakshmi, V Lakshmi Prabha, “MRI brain image classification - a hybrid approach”, International Journal of Imaging Systems Technology, Vol. 25, No.3, pp. 226–244, 2015.
[7] R.J. Ramteke, Y. Khachane Monali, “Automatic medical image classification and abnormality detection using K-Nearest Neighbour”, International Journal of Advanced Computer Research, Vol. 2, No.4, pp. 190-196, 2012.
[8] Z. Kapas, L. Lefkovits, L. Szilagyi, “Automatic Detection and Segmentation of Brain Tumor Using Random Forest Approach”, In Modeling Decisions for Artificial Intelligence, pp. 301-312. Springer, Cham, 2016.
[9] M. Rezaei, H. Yang, C. Meinel, “Brain Abnormality Detection by Deep Convolutional Neural Network”, arXiv preprint arXiv:1708.05206. 2017 Aug 17.

[10] V.P. Gladis Pushpa Rathi, S. Palani, “Brain Tumor MRI Image Classification with Feature Selection and Extraction Using Linear Discriminant Analysis”, International Journal of Information Sciences and Techniques (IJIST), Vol.2, No.4, pp.131-146, 2012.
[11] D.R. Nayak, R. Dash, B. Majhi, “Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests”, Neurocomputing, Vol. 177, pp. 188-197, 2016.
[12] T. Gupta, P, Manocha, T.K. Gandhi, R.K. Gupta, B.K. Panigrahi, “Tumor Classification and Segmentation of MR Brain Images”, arXiv preprint arXiv:1710.11309, 2017.
[13] S.A. El-Dahshan, T. Hosny, A.M. Salem, “Hybrid intelligent techniques for MRI brain images classification”, Digital Signal Processing, Vol. 20, No. 2, pp. 433-441, 2010.
[14] M. Abdallah, M. Blonski, S. Wantz-Mezieres, Y. Gaudeau, L. Taillandier, J. Moureaux, “On the relevance of two manual tumor volume estimation methods for diffuse low-grade gliomas”, Healthcare Technology Letters, pp. 1-4, 2017.
[15] M. Robert, K. Haralick, K. Shanmugam, I. Dinstein, “Texture features for Image Classification”, IEEE Transactions on Systems and Cybernetics, Vol. 3, No. 6, pp. 610 - 621 1973.
[16] T. Ho, Kam, “Random Decision Forests”,. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, pp. 278–282, 1995.
[17] T. Kalaiselvi, S. Karthigai selvi, “A Novel Wavelet based Feature Selection to Classify Abnormal Images from T2-w Axial Head Scans”, Proceedings of National Conferences on New Horizons in Computational Intelligence and Information Systems (NHCIIS), India, pp. 140 -145, 2015.
[18] T. Kalaiselvi, P. Sriramakrishnan, “Brain Abnormality Detection from MRI of Human Head Scans using the Bilateral Symmetry Property and Histogram Similarity Measures”, The 20th International Computer Science and Engineering Conference, IEEE explore, Tailand, pp. 14 - 17 December 2016.
[19] R. Anitha, D. Siva Sundhara Raja, “Development of computer-aided approach for brain tumor detection using random forest classifier”, International Journal of Imaging Systems and Technology, pp.1–6, 2017. DOI: 10.1002/ima.22255

P. Sriramakrishnan, T. Kalaiselvi, P. Nagaraja and K. Mukila, "Tumorous Slices Classification from MRI Brain Volumes using Block based Features Extraction and Random Forest Classifier", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.191-196, 2018.

In recent years the computer vision field grown enormously and provides solutions to various other fields particularly like medical domain. Therefore many researchers contributed plenty of algorithms to support diagnosis. This proposed cinch bone fracture detection is a novel, easy, and effective algorithm for bone fracture using object counting in Leg bone or tibia. It automatically detects fracture & non-fracture in a leg bones.

Bone fracture, X-ray, Color images, Object Count, Connected Component

[1] Lum, V. L. F., Leow, W. K., Chen, Y., Howe, T. S., Png, M. A. , Combining classifiers for bone fracturedetection in x-ray images. In: Image Processing. ICIP. IEEEInternational Conference on, 1, p. I–1149. IEEE. 2005.
[2] Aishwariya, R., Geetha, M.Kalaiselvi. and Archana, M.“Computer- Aided Fracture Detection Of X-Ray Image”‖,
[3] Al-Khaffaf, H., Talib, A. Z., Salam, R. A, 19th International Conference on, p. 1–4. IEEE. (2008). Removing salt-and-pepper noise from binary images of engineering drawings. Pattern Recognition. ICPR.
[4] Digital images. IAENG International Journal of ComputerScience, 37(1).
[5] Caylak, E., 2010. The studies about phonological Deficittheory in children with developmentaldyslexia: Review. Am. J. Neurosci., 1: 1-12. DOI: 10.3844/ajnsp.2010.1.12
[6] Tanudeep Kaur , Anupam Garg. Bone Fraction Detection using Image Segmentation. International Journal of Engineering Trends and Technology (IJETT) – Volume 36 Number 2- June 2016.
[7] Vijaykumar, V., Vanathi, P., Kanagasabapathy P. (2010). Fast and efficient algorithm to remove gaussian noise removal.
[8] Chokkalingam, SP. and Komathy, K. ―Intelligent Assistive Methods for Diagnosis of Rheumatoid Arthritis Using Histogram Smoothing and Feature Extraction of Bone Images‖. Engineering and Technology International Journal of Computer Information Systems and Control Engineering, Vol. 8,Issue .5, pp.834-843,2014
[9] Liang, Jian, et al. "Fracture identification of X-ray image." Wavelet Analysis and Pattern Recognition (ICWAPR), 2010 International Conference on. IEEE, 2010.
[10] Jia,Y.,and Y.Jiang. "Active contour model with Shape constraints for bone fracture detection." Computer Graphics, Imaging and Visualisation, 2006 International Conference on. IEEE, 2006
[11] Mahmoud Al-Ayyoub, Ismail Hmeidi, Haya Rababah. Detecting Hand Bone Fractures in X-Ray Images. Journal of Multimedia Processing and Technologies Volume 4 Number 3 September 2013
[12] Sachin R.Mahajan, P.H.Zope,S.R.Suralkar. Review of AnEnhance Fracture Detection Algorithm Design Using XRaysImage Processing. International Journal of Innovative Research in Science, Engineering and Technology, Vol. 1, Issue 2, December 2012.
[13] Sharma, N. and Aggarwal, L.M. (2010) Automated Medical image segmentation techniques, J Med Phys, Vol.35, Pp.3-14.
[14] Swathika.B1, Anandhanarayanan. K, Baskaran B and Govindaraj R. Radius Bone Fracture Detection UsingMorphological Gradient Based Image Segmentation Technique
[15] Chan, K.P., Fu, A. W.C. Efficient time series matching by wavelets. Data Engineering. In: Proceedings, 15th International Conference on, p. 126– 133. IEEE. ,1999
[16] Anu T C, Mallikarjunaswamy M.S Rajesh Raman. Detection of Bone Fracture using Image Processing Methods. In International Journal of Computer Applications (0975 – 8887).

A. Shanthasheela, E. Nithya, "A Novel Cinch Automatic Bone Fracture Detection Algorithm", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.197-202, 2018.

Function Approximation is a major need in many areas such as Applied Mathematics, Computer Science, Engineering problems and so on. This paper proposed a solution for performing function approximation by using novel functional Chebyshev Neural Network with Backpropagation Algorithm. The advantage of Chebyshev Neural Network is very efficient for computation because of less complexity in modelling of the structure and produces the fast convergence rate and it is easy to implement circuit implementation compared to the standard Multilayer feed forward neural network. The proposed network consists of single input and a single output. The hidden layer is designed as taking the input of numerically transformable Chebyshev polynomial expansion of input. Backpropagation algorithm with Chebyshev Neural Network shows good behaviour in Nonlinear Function Approximation compared to multilayer feed forward neural network. The performance metric used in this paper to compare the realization capability of two networks for training and testing phase is Mean Square Error.

Function Approximation, Chebyshev Neural Network, Multilayer Perceptron, Backpropagation Algorithm

[1] Yan, S. P., et al. "CO2 concentration detection based on Chebyshev neural network and best approximation theory." Instrument Technique and Sensor 6 (2011): 107-110.
[2] Shrivastava, Animesh Kumar, and Shubhi Purwar. "State feedback and output feedback tracking control of discrete-time nonlinear system using Chebyshev neural networks." Power, Control and Embedded Systems (ICPCES), 2010 International Conference on. IEEE, 2010.
[3] Zou, An-Min, Krishna Dev Kumar, and Zeng-Guang Hou. "Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks." IEEE transactions on neural networks 21.9 (2010): 1457-1471.
[4] Mishra, Sudhansu Kumar, Ganpati Panda, and Sukadev Meher. "Chebyshev functional link artificial neural networks for denoising of image corrupted by salt and pepper noise." (2009).
[5] Patra, Jagdish Chandra, et al. "Identification of nonlinear dynamic systems using functional link artificial neural networks." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 29.2 (1999): 254-262.
[6] Wang, Lidan, Meitao Duan, and Shukai Duan. "Memristive chebyshev neural network and its applications in function approximation." Mathematical Problems in Engineering 2013 (2013).
[7] Purwar, Shubhi, Indra Narayan Kar, and Amar Nath Jha. "On-line system identification of complex systems using Chebyshev neural networks." Applied soft computing 7.1 (2007): 364-372.
[8] Akritas, P., I. Antoniou, and V. V. Ivanov. "Identification and prediction of discrete chaotic maps applying a Chebyshev neural network." Chaos, Solitons & Fractals 11.1-3 (2000): 337-344.
[9] Mall, Susmita, and Snehashish Chakraverty. "Numerical solution of nonlinear singular initial value problems of Emden–Fowler type using Chebyshev Neural Network method." Neurocomputing 149 (2015): 975-982.
[10] Zou, An-Min, Krishna Dev Kumar, and Zeng-Guang Hou. "Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks." IEEE transactions on neural networks 21.9 (2010): 1457-1471.
[11] Sornam, Madasamy, V. Vanitha, and T. G. Ashmitha. "Noise Removal using Chebyshev Functional Link Artificial Neural Network with Back propagation." International Journal of Advanced Research in Computer Science 8.5 (2017).

M. Sornam, V. Vanitha, "Application of Chebyshev Neural Network for Function Approximation", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.201-204, 2018.

The data collected from MST radar uses traditional and statistical analysis for inferring wind components from the spectral data. There are several algorithms available for dimensionality reduction on big data using PCA. These algorithms are non -parametric and often implemented on high dimensional datasets. It will be quite interesting to use these analytical algorithms in the context of MST radar dataset. The existing algorithms are very week in estimation of Doppler at low SNR conditions at higher altitudes. Thus PCA algorithm has been applied on the MST Radar data to find Power Spectrum (PS) and from Power Spectrum Doppler Frequency components are estimated. The components are Zonal (U), Meridional (V), Windspeed (W) are estimated from Doppler Frequency. The PCA derived wind data has to be qualified with wind information from GPS radio-sonde thereafter.

Principal Component Analysis, MST radar, GPS sonde, Wavelet-based denoising, cepstral, thresholding

[1] V.K. Anandan, “Spectral analysis of atmospheric signal using higher orders spectral estimation technique”, IEEE Transaction, Geosci. Remote Sens. 39 (9) (Sep.2001) 1890-1895
[2] T. Sreenivasulu Reddy, “MST radar signal processing using cepstral thresholding”, IEEE Transaction Geosci. Remote Sens. 48 (6) (Jun.2010) 2704-2710
[3] Thatiparthi Sreenivasulu Reddy, “MST radar signal processing using wavelet-based denoising”, IEEE Transaction Geosci. Remote Sens. Lett. 6 (4) (Oct.2009) 752-756
[4] P. Stoica, “Smoothed non parametric spectral estimation via Cepstral thresholding”, IEEE Signal Process. Mag.23(6) (Nov.2006) 34-45
[5] D.A. Hooper, “Signal and noise level estimation for narrow spectral width returns observed by the Indian MST radar”, Radio Sci. 34(4)(1999) 859-870

M.Anitha, J. Avanija , "Implementing PCA on MST Radar data for Wind Analysis", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.205-208, 2018.

In this paper, the work aimed a robust edge detection based on fuzzy technique for MRI brain image. Segmentation is the critical task in medical applications and also it is the most important task in medical image analysis. In brain image, segmentation is commonly used for analyse the brain changes and structure of the brain image and analyse the region of the brain image. Edge detection is the basic tool for segmentation. Edge detection is the finding the boundary of the particular image and edges occur on the boundary between the object and the background. Here, this paper segments the MRI image using fuzzy c-means thresholding. It covert the grey image to binary image and the result image applied fuzzy interface system and find edge of the particular object in the MRI Image. Experiments were done by using the MRI scan images.

Fuzzy logic, Fuzzy C-Means Thresholding, Fuzzy Edge detection, Fuzzy interface system, MRI head scans

[1] M.R Garey and D.S Johnson, “Computers and Intractability: A Guide to the Theory of NP-Completeness”. New York: W.H Freeman, 1979
[2] Er Kiranpreet Kaur, Er Vikram Mutenja ,Er Inderjeet Singh Gill,” Fuzzy Logic Based Image Edge Detection Algorithm in MATLAB”, International Journal of Computer Applications (0975 – 8887), Volume 1 – No. 22, 2010.
[3] Yasar Becerikli and Tayfun M. Karan, “A New Fuzzy Approach for Edge Detection”, Springer-Verlag Berlin Heidelberg, LNCS 3512, p 943 – 951, 2005.
[4] Du Gen-Yuan, MianoFang, Tian Sheng-Li,Guo Xi-Rong., “Remote Sensing Image Sequence Segmentation Based On The Modified Fuzzy C-Means”, Journal Of Software , Vol.5, No. 1, pp.28-35, 2009.
[5] Er Kiranpreet Kaur, Er Vikram Mutenja, Er Inderjeet Singh Gill, “Fuzzy Logic Based Image Edge Detection Algorithm in MATLAB”, International Journal of Computer Applications, Vol 1 – No. 22, 2010.
[6] Suryakant, Neetu Kushwaha, “Edge Detection using Fuzzy Logic in Matlab”, International Journal of Advanced Research in Computer Scienceand Software Engineering, Vol. 2, Issue 4, April 2012.
[7] Yau-Hwang Kuo, Chang-Shing Lee and Chao-Chin Liu, “A New Fuzzy Edge Detection Method for Image Enhancement”, IEEE,p 1069-1074 97.
[8] N. Senthilkumaran, R. Rajesh, "Edge Detection Techniques for Image Segmentation and A Survey of Soft Computing Approaches", International Journal of Recent Trends in Engineering, Vol. 1, No. 2, PP.250-254, May 2009.
[9] Hu L., Cheng H. D. and Zang M.” A high performance edge detector based on fuzzy inference rules”. An International Journal on Information Sciences, vol. 177,Nov 2007, no. 21, pp. 4768-4784.
[10] Tao, C. W. et al(1993), “A Fuzzy if-then approach to edge detection”, Proc. of 2nd IEEE intl.conf. on fuzzy systems, pp. 1356–1361.
[11] Li, W. (1997),” Recognizing white line markings for vision-guided vehicle navigation by fuzzy Reasoning”, Pattern Recognition Letters, 18: 771–780.
[12] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM Computing Surveys, vol.31, pp. 264-323,1999.
[13] J. Liu and M. Xu, "Kernelized fuzzy attribute C-means clustering algorithm," Fuzzy Sets and Systems, vol. 159, pp.2428-2445, 2008.
[14] A. B. Goktepe, S. Altun, and A. Sezer, "Soil clustering by fuzzy c-means algorithm," Advances in Engineering Software, vol. 36, pp. 691-698, 2005.

N. Senthilkumaran, C. Kirubakaran, N. Tamilmani, "Fuzzy Edge Detection Using Fuzzy C-Means Thresholding for MRI Brain Image", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.209-213, 2018.

Electroencephalogram (EEG) is used for detecting problems in the electrical activity of the brain associated with brain disorders. During acquisition of EEG signals various noises like electrocardiogram (ECG),electromyogram(EMG),electrooculogram(EOG)and power line interference etc. contaminates the signal, which makes the proper analysis of the signal difficult. Therefore, noise removal is an integral part of preprocessing step before signal analysis. In this paper, wavelet transform using different kind of filters like db2, db4, coif2, coif4, sym2 and sym4 is used to decompose the signal into low and high frequency components. Then, high frequency components have been thresholded at each level of decomposition. The denoised signal is reconstructed using the thresholded coefficients and the approximation coefficients. Thresholding methods such as minimaxi, Sure (Heuristic and rigorous) and Square-Root-Log are investigated to compute the threshold value. The coiflet filter at level 4 with minimax thresholding method performed better than other wavelet filters and thresholding methods in terms of Peak Signal-to-Noise Ratio (PSNR) value.

Electroencephalogram, Wavelet Transform, Threshold, Denoising, Peak Signal-to-Noise Ratio

[1] V. Singh, R. Sharma,“Performance Comparison of Denoising Methods of Electroencephalogram”International Journal of Engineering Research & Technology, Vol.3 Issue.9,2014
[2] M. Mamun, M. Al-Kadi, M. Marufuzzaman,“Effectiveness of Wavelet Denoising on Electroencephalogram Signals” Journal of Applied Research and Technology, Vol.11, pp.156-160, 2013
[3] S. Sudha, G. R. Suresh, R. Sukanesh,“Wavelet based image denoising using adaptive thresholding” In the proceedings of the 2007 International Conference on Computational Intelligence and Multimedia Applications (ICCIMA), pp.296–300, 2007
[4] A. K. Verma, N. Verma,“Performance analysis of wavelet thresholding methods in denoising of audio signals of some indian musical instruments” International Journal of Engineering Science Technology, vol.4, No.5, pp.2047–2052, 2012
[5] V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, K. Ramadoss,“Automatic identification and removal of ocular artifacts from EEG using wavelet transform”Measurement Science Review, Vol.6, No.4,pp.45-57,2006
[6] P. SenthilKumar, R. Arumuganathan, K. Sivakumar, C. Vimal,“Removal of ocular artifacts in the EEG through wavelet transform without using an EOG reference channel”Int. J. Open Problems Compt. Math., Vol.1, No.3,pp.188-200,2008
[7] N. K. Al-Qazzaz, S. Ali, S. A. Ahmad, M. S. Islam, and M. I. Ariff,“Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task”In the proceedings of 2014 IEEE Conference on Biomedical Engineering and Sciences, pp. 214–219, 2014
[8] TP. Jung, S. Makeig, C. Humpheries, T. Lee, M.J. Mckeown, V. Iragui, T.J. Sejnowski,“Removing electroencephalographic artifacts by blind source separation”Psychophysiology 37, pp.163–1782000
[9] S. Lahmiri, M. Boukadoum,“A Weighted Bio-signal Denoising Approach Using Empirical Mode Decomposition”The Korean Society of Medical & Biological Engineering and Springer, Vol.5, Issue.2, pp.131-139,2015
[10] K. Thangavel, K. Sasirekha,“Denoising Iris Image Using a Novel Wavelet Based Threshold” In the proceedings of Digital Connectivity – Social Impact. CSI pp. 57-69, 2016

Dipali Sinha, Thangavel K., "Performance Analysis of wavelet Thresholding for Denoising EEG Signal", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.214-218, 2018.

Land Surface Temperature (LST) is the skin temperature of the earth surface. And it is found that the value of LST will be high in settlement regions and low in area under green cover. The amount of green cover is reducing day by day which leads to higher LST. Hence in this paper LST was calculated for in and around Dindigul City. Satellite image provides information about thermal reflection. Satellite image processing is usually done in GIS and Digital Image Processing Software, whereas the output thus processed was compared with the result derived from Matlab software. From the analysis it is evident that Matlab can also be utilised for satellite image processing also. To calculate LST Split-Window Algorithm has been used. The output reveals that the LST estimated using both the software are nearly correlated. The slight difference is due to data type conversion in Matlab.

Land Surface Temperature, Geographical Information System, Digital Image Processing, Satellite image

[1] Zhao-Liang Li, Bo-Hui Tang, Hua Wu, Huazhong Ren, Guangjian Yan, Zhengming Wan, Isabel F. Trigo, José A. Sobrino, “Satellite-derived land surface temperature: Current status and perspectives”, Remote Sensing of Environment, Vol. 131, pp 14–37, 2013
[2] Jose A. Sobrino, Juan C. Jimenez-Munoz, Guillem Soria, Mireria Romagueram Luis Guanter, Jose Moreno, “Land Surface Emissivity Retrieval from Different VNIR and TIR Sensors”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, Issue 2,pp 316-327, 2008
[3] Sumit Khandelwal, Rohit Goyal, Nivedita Kaul, Aneesh Mathew, “Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India”, The Egyptian Journal of Remote Sensing and Space Science,2017
[4] Suzana Binti Abu Bakar, Biswajeet Pradhan, Usman Salihu Lay and Saleh Abdullahi, “Spatial assessment of land surface temperature and land use/land cover in Langkawi Island”, Earth and Environmental Science 37, 2016, 012064
[5] Swades Pal, Sk. Ziaul, “Detection of Land Use and Land Cover Change and Land Surface Temperature in English Bazar Urban Centre” The Egyptian Journal of Remote Sensing and Space Sciences, Vol. 20, pp 125–145, 2017
[6] Patricia Wanjiku Mwangi, Faith Njoki Karanja, Peter Kariuki Kamau, “Analysis of the Relationship between Land Surface Temperature and Vegetation and Built-Up Indices in Upper-Hill, Nairobi”, Journal of Geoscience and Environment Protection, Vol. 6, pp 1-16, 2018
[7] Rajeshwari.A, Mani N.D., “Estimation of Land Surface Temperature of Dindigul District using Landsat 8 Data”, International Journal of Research in Engineering and Technology, Vol. 3, Issue.5, pp 122-126, 2015

A. Rajeshwari, N.D. Mani, "Estimation of Land Surface Temperature using GIS and MATLAB", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.219-222, 2018.

Knowledge acquisition is an elementary stage of knowledge engineering. The process includes extracting the raw data from various sources that are later structured and organized in such a form that contribute in providing knowledge. Usually knowledge can be acquired from sources such as primer, manuals and simulation models but a highly elaborate acquisition comes from human experts. This paper projects the various modelling techniques and framework. The proposed Knowledge Acquisition Framework(KAF)emphasizes on the models like Common KADS, MIKE, PROTEGE and their concept of knowledge acquisition. This technology helps in the process of building and framing various application of knowledge engineering.

KADS, MIKE, PROTEGE, KAF, Umbrella Approaches, ESPRIT

[1] Rudi Studer, V. Richard Benjamins, and Dieter Fensel, “Knowledge Engineering: Principles and Methods” Data & Knowledge Engineering 25 (1998) 161-197, Elsevier.
[2] John H. Gennari, Mark A. Musen, Ray W. Fergerson, William E. Grosso, Monica Crubézy, Henrik Eriksson, Natalya F. Noy and Samson W. Tu, “The Evolution of Protégé: An Environment for Knowledge-Based Systems Development”, International Journal of Human-Computer Studies archive,Volume 58 Issue 1, January 2003 Pages 89 – 123.
[3] A. Abecker, S. Decker, K. Hinkelmann, and U. Reimer, Proc. “Workshop Knowledge-based Systems for Knowledge Management in Enterprises”, 21st Annual German Conference on AI (KI’97), Freiburg, 1997.
[4] Frank van Harmelen, “Formal Methods in Knowledge Engineering”, PhD Thesis, University of Amsterdam, 1995. The Knowledge Engineering Review, Vol. 10, No. 4, pp. 345-360, 1995
[5] J. Angele, S. Decker, R. Perkuhn, and R. Studer, “Modeling Problem-Solving Methods in NewKARL”, Proceedings of the 10th Knowledge Acquisition for Knowledge - Based Systems Workshop (KAW`96), Banff, Canada, November. Karlsruhe: 1996. 18 pp.
[6] J. Angele, D. Fensel, D. Landes and R. Studer, Developing “Knowledge-Based Systems with MIKE”, Journal of Automated Software Engineering, October 1998, Volume 5, Issue 4, pp 389–418
[7] Juergen Angele, Dieter Fensel, Rudi Studer, “Domain and Task Modeling in MIKE”, IS&O 1996: Domain Knowledge for Interactive System Design pp 149-163, Springer
[8] J. McDermott, “Preliminary steps toward a taxonomy of problem-solving methods”, pp.225-256 In Automating Knowledge Acquisition for Expert Systems. S. Marcus, (Ed.), Kluwer, Academic Publishers, Boston,1998.
[9] Guus Schreiber, Hans Akkermans, Anjo Anjewierden, Robert De Hoog, Nigel R. Shadbolt, Walter Van de Velde and B J. Wielinga, “Knowledge Engineering and Management: The CommonKADS Methodology”, MIT Press, 1999
[10] Edward H. Shortliffe, A. Carlisl e Scott, Miriam B. Bischoff, A. Bruce Campbell, Willia m Van Melle, Charlott e D. Jacobs, “ONCOCIN: An expert system for oncology protocol management”. International Joint Conference on Artificial Intelligence (IJCAI `81), Vancouver, CA, 876-881.
[11] B.J.Wielinga, A. Th. Schreiber, J. A. Breuker, “KADS: A modeling approach to knowledge engineering”, Knowledge Acquisition, Volume 4, Issue 1, March 1992, Pages 5-53
[12] Noy, N. F., Fergerson, R. W., and Musen, M. A., “The knowledge model of Protégé-2000: Combining interoperability and flexibility”, Second International Conference on Knowledge Engineer- ing and Knowledge Management (EKAW`2000), Juan-les-Pins, France, (2000).
[13] Rose Dieng, Olivier Corby, Sofiane Labidi, “Agent-based knowledge acquisition”, International Conference on Knowledge Engineering and Knowledge Management EKAW 1994: A Future for Knowledge Acquisition pp 63-82
[14] R. Dieng, O. Corby, and S. Labidi., “Expertise conflicts in knowledge acquisition”, In Proc. of the 8th KAW, vol. 2, pages 23.1–23.19, Banff, Canada, 1994.
[15] Gamble, P.R., Blackwell, J., “Knowledge Management: A State of the Art Guide”, Kogan Page, London, 2001

Thirunavukkarasu K., Abhilash Ashu, Digvijay Singh, Shahnawaz S. Khan, "Analysis of Modelling Frameworks for Knowledge Acquisition", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.223-227, 2018.

Securing data in today’s computing environment is an important aspect. Biometrics is one of the techniques which provide reliable security on the data in this insecure world. Currently, iris, face, finger print, palm have been employed in biometric authentication to authorize the person. Due to its unique behavior, biometric systems provide a good reliable and prominent environment. In recent research on biometric authenticity, it is proved that the vessel patterns of sclera are unique and it is applicable throughout the human lifetime. The sclera recognition consists of various stages, among which sclera segmentation and the feature extraction are the important stages as they decide the accuracy of the system. Feature extraction is to be done after segmentation and enhancement of the vessel patterns. This paper discusses proposal of robust method using canny based segmentation and Harris corner feature extraction techniques.

Harris corner edge detection, Biometrics, pattern recognition, sclera pattern matching, Pattern Enhancement, sclera segmentation

[1] Zhi Zhou, “A New Human Identification Method: Sclera Recognition”, IEEE transactions on systems, man, and cybernetics—part a: systems and humans, vol. 42, no. 3, may 2012.
[2] S. Alkassar, W. L. Woo, S. S. Dlay, J. A. Chambers, “Robust Sclera Recognition System With Novel Sclera Segmentation and Validation Techniques”, IEEE transactions on systems, man, and cybernetics: systems, 2168-2216 _c 2015 IEEE
[3] G. Annapoorani, R. Krishnamoorthi, P. Gifty jeya, S. Petchiammal@sudha, “Accurate and fast iris segmentation”, international journal of engineering science and technology
vol. 2(6), 2010, 1492-1499.
[4] Reza Derakhshani and Arun Ross, “a new biometric modality based on conjunctival Vasculature”,Appeared in Proc. of Artificial Neural Networks in Engineering (ANNIE), (St. Louis, USA), November 2006.
[5] Reza Derakhshani and Arun Ross, “A TextureBased Neural Network Classifier for Biometric Identification using Ocular Surface Vasculature”, Appeared in proc. Of International Joint Conference on Neural Network(IJCNN),Orlanda (USA),August 2007.
[6] S. Crihalmeanu, A. Ross, and R. Derakhshani, "Enhancement and Registration Schemes for Matching Conjunctival Vasculature," in Proceedings of the Third International Conference on Advances in Biometrics Alghero, Italy: Springer- Verlag, 2009.
[7] L. Flom and A. Safir, “Iris Recognition system”, U.S.Patent: 4,641,349, 1987.
[8] H. Davson, “Davson’s Physiology of the Eye”, MacMillan, London, 1990.
[9] L.G.Roberts, “Machine perception of 3-D solids”, Optical and Electro optical Information processing, 1965.
[10] J.M.S Prewitt, “Object enhancement and extraction”, Picture Processing and Psychopictorics , 1970.
[11] I.E. Sobel, “Camera models and machine perception”, Thesis, Stanford University, 1970.
[12] M.Hueckel, “An operator which locates edges in digital pictures”, Journal of the ACM, Vol.18, No.1, pp.113-125, 1971.
[13] A.Rosenfeld. The Max Roberts operator is Hueckel-type of edge detectors. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.3, No.1, pp.101-103, 1981.
[14] Marr, D. and Hildreth, E. C. Theory of edge detection. Proceedings of the Royal Society of London Series B: Biological Sciences, Vol.207, pp.187-217, 1980.
[15] A. Goshtasby and Hai-LunShyu, “Edge detection by Curve fitting”, Image and Vision Computing, Vol. 13, No.3, pp.169-177, 1995.
[16] J. Canny, “A computational approach for edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence”, Vol. 8, no.6, pp. 679-698, 1986.
[17] S.Y. Sarkar. and K.L Boyer, “Optimal infinite impulse response zero-crossing based edge detectors”, Computer Vision Graphics Image Processing: Image Understanding, Vol.54, No.9, pp.224-243, 1991.
[18] J.Shen and S. Castan, “An optimal linear operator for step edge detection”, Graph. Models Image Processing, Vol.54, No.1, pp.112-133, 1992.
[19] L. Ding, and A. Goshtasby, “ On the Canny edge detector”, Pattern Recognition, Vol.34, pp.721-725, 2001.
[20] R.R. Rakesh, ProbalChaudhuri and C.A.Murthy, “Thresholding in Edge Detection: A Statistical Approach”, IEEE Trans. on Image Processing, Vol.13, No.7, pp.927-936, 2004.
[21] D. Stern and L. Kurz, “Edge detection in correlated noise using Latin squares models”, Pattern Recognition, Vol.21, pp.119-129, 1988.
[22] P. Qie and S.M. Bhandarkar, “An edge detection Techniques using local smoothing and statistical hypothesis testing”, Pattern Recognition Letters, Vol.17, No.8, pp.849872, 1996.
[23] S.Z. Li, “Roof-Edge Preserving Image Smoothing Based on MRFs” IEEE Trans. On Image Processing, Vol.9, No.6, pp.11341138, 2000.
[24] W.E.L Grimson and T. Pavlidis, “Discontinuity detection for visual surface reconstruction”, Computer Vision, Graphics, Image Processing, Vol.30, pp.316-330, 1985.
[25] D. Lee, T. Pavlidis and K. Huang, “Edge detection through residual analysis”, Proc. IEEE Comput. Soc. Conf. Computer Vision and Pattern Recognition, pp.215-222, 1988.
[26] T. Pavlidis and D. Lee, “Residual analysis for feature extraction, in From Pixel to Features”, Proc. COST13 Workshop, pp.219-227, 1988.
[27] M.H. Chen, D. Lee, and T. Pavlidis, “Residual analysis for feature detection, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.13, pp.30-40, 1991.
[28] S. Zheng, Jian Liu and Jin Wen Tian, “A new efficient SVM-based edge detection method” Pattern Recognition Letters, Vol.25, pp.11431154, 2004.
[29] Dong-Su Kim, Wang-Heon Lee and In-So Kweon, “Automatic edge detection using 3 X 3 ideal binary pixel patterns and fuzzy-based edge thresholding”, Pattern Recogntion Letters, Vol.25, pp.101-106, 2004.
[30] J. Daugman, “High Confidence Visual Recognition of persons by a test of statistical independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No. 11, pp.1148-1161, 1993.
[31] J. Daugman, “The importance of being random: Statistical principles of iris recognition. Pattern Recognition”, Vol.36, No.2 , pp.279-291,2003.
[32] J. Daugman, “Demodulation by complex valued wavelets for stochastic pattern recognition,” International Journal on Wavelets, Multiresolution and Information Processing, Vol. 1, No. 1, pp. 1-17, 2003.
[33] J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No.1, pp. 21-30, 2004.
[34] Wildes, R.P., "Iris Recognition: An Emerging Biometric Technology", Proc. of the IEEE, Vol. 85, No.9, pp.1348-1363, 1997.
[35] Yong Zhu, Tieniu Tan, and Yunhong Wang, "Biometric Personal Identification Based on Iris Patterns", Proceedings of the 15th International Conference on Pattern Recognition, Vol. 2, pp.805 - 808, 2000.
[36] S. Lim, K.Lee, O.Byeon, and T. Kim, “Efficient Iris Recognition throughImprovement of Feature Vector and Classifier”, Journal of Electronics and Telecommunication Research Institute, Vol. 23, No. 2, pp. 61 – 70, 2001.

S Vijayalakshmi, Gokul Rajan V, "A Novel Approach for Human Identification using Sclera Recognition", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.228-235, 2018.

Magnetic Resonance Imaging of fetal brain facilitates to evaluate in-utero fetal brain development. Segmentation of fetal brain from MRI is a challenging task due to significant changes in terms of geometry as well as tissue morphology. In order to make ease of segmentation, fetal brain border is detected using chain code algorithm. Detected brain border is used further through in which query images will be extracted from fetal MRI database. This work will be extended with feature extraction and 3D modeling of fetal brain in a little while.

fetal brain,chain code,feature extraction

[1] Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, ” Digital Image Processing Using MATLAB”, 2nd edition,pp 440-488.
[2] Jahne, B., “Practical handbook on image processing for scientific and technical applications”, CRC Press, p.494,2004.
[3] Neeta Nain, Viajay Laxmi, Ankur Kumar Jain and Rakesh Agarwal,“Morphological Edge Detection and Corner Detection Algorithm Using Chain-Encoding”, IPCV’2006, pp 1-5,2008.
[4] Baji F, Mocanu M, "Chain Code Approach for Shape based Image Retrieval",Indian Journal of Science and Technology,vol.11,2018.
[5] Boodoo-Jahangeer, N.B. and Baichoo, "Face recognition using chain codes",Journal of Signal and Information Processing, vol.4,p.154,2013.
[6] Parmar DA., "Content MRI Brain Image Retrieval using Shape Descriptors and Relevance Vector Machine (RVM)",International Journal of Advance Research in Computer Science and Management Studies,vol.4,2016.
[7] Anjan Bikash Maity,Sandip Mandal,Ranjan Podder,"Edge Detection Using Morphological Method and Corner Detection Using Chain Code Algorithm",IJCSI International Journal of Computer Science Issues,vol.8, 2011.

S.P. Gayathri, K.Somasundaram, R.Siva Shankar, "Fetal Brain Border Detection from MRI using Chain Code Algorithm", International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.236-238, 2018.