International Journal of Innovative Science and Modern Engineering(TM)
Exploring Innovation| ISSN:2319–6386(Online)| Reg. No.:68121/BPL/CE/12| Published by BEIESP| Impact Factor:3.86
Home
Aims & Scope
Instructions for Authors
Call for Papers
Editorial Board
Archive
Conferences
Download
Fee
Ethics & Policies
FAQ
Contact
Volume-4 Issue-11: Published on June 15, 2017
03
Volume-4 Issue-11: Published on June 15, 2017

 Download Abstract Book

S. No

Volume-4 Issue-11, June 2017, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Prasanna.S, Durgadevi.S, Senthilkumar.S

Paper Title:

An Efficient Salient Region Detection on Underwater Image with Grabcut Algorithm

Abstract:  Saliency detection aims at automatically estimating visually salient object regions in an image, saliency segmentation and foreground extraction are two important applications of this. However, it is a challenge for underwater images to estimate salient regions by saliency detection methods because of the low-contrast and poor quality. In this paper, we address this problem by combining the detected object regions rather than the whole image, where Sobel edge detector and Active contour is used for proposing candidate regions. We extensively evaluated our method on underwater images, and experimental results show that the performances of saliency detection and segmentation are improved. These saliency segmentation masks are further used to extract the foreground objects of an image.

Keywords:
  Saliency segmentation, foreground extraction, fish localization, underwater image.


References:

1.       Cheng, Ming, et al. "Global contrast based salient region detection." Pattern Analysis and Machine Intelligence, IEEE Transactions on 37.3 (2015): 569-582               
2.       M.Clerc, “The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm   Optimization,” In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1951-1957, 1999.  (conference style)

3.       Goferman, Stas, Lihi Zelnik-Manor, and Aiyellet Tal. "Context-aware saliency detection." Pattern Analysis and Machine Intelligence, IEEE Transactions on34.10 (2012): 1915-1926.

4.       Rother, Carsten, Vladimir Kolmogorov, and Andrew Blake. "Grabcut: Interactive foreground extraction  using  iterated graph cuts."ACM Transactions on Graphics (TOG)       23.3 (2004): 309-314.

5.       Chen, Tao, et al. "Sketch2Photo: internet image montage." ACM Transactions on Graphics (TOG) 28.5 (2009): 124

6.       Avidan, Shai, and Ariel Shamir. "Seam carving for content-aware image resizing." ACM Transactions on graphics (TOG). Vol. 26. No. 3. ACM, 2007.

7.       Christopoulos, Charilaos, Athanassios Skodras, and       TouradjEbrahimi. "The JPEG2000 still image coding system: an overview. "  Consumer Electronics, IEEE Transactions on 46.4 (2000): 1103-1127.

8.       Achanta, Ravi, et al. "Frequency-tuned salient region  detection." Computer vision and pattern recognition, 2009. cvpr 2009. IEEE conference on. IEEE, 2009.

9.       Itti, Laurent, Christof Koch, and Ernst Niebur. "A model of saliency- based visual attention for rapid scene analysis." IEEE Transactions on Pattern Analysis & Machine Intelligence 11 (1998): 1254-1259.

10.    Zhai, Yun, and Mubarak Shah. "Visual attention detection in video sequences using spatiotemporal cues."Proceedings of the 14th annual ACM international conference on Multimedia. ACM, 2006.

11.    Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A  spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007.

12.    Cheng, Ming-Ming, et al. "Efficient salient region detection with soft image abstraction." Proceedings of the IEEE International Conference on Computer Vision. 2013.

13.    Caselles, f. Catté (catte), t. Coll, f. Dibos, "a geometric model for active contours in image processing", numer. Math., vol. 66, pp. 1-31, 1993.

14.    Y. Hu, X. Xie, W.-Y. Ma, L.-T. Chia, and D. Rajan. Salient region detection using weighted feature maps based on the human visual attention model.  Pacific Rim Conference on Multimedia, 2004.

15.    Z. Wang and B. Li. A two-stage approach to saliency detection in images. IEEE Conference on Acoustics, Speech and Signal Processing, 2008.


1-5

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html








































2.

Authors:

V. Parvathavarthini, S. M. Ramesh, M. Irshad Ahamed

Paper Title:

Detection of Prostate Cancer with MAPS Feature Model using Region Growing Algorithm

Abstract: In this paper we present a new method for automated and quantitative grading of prostate cancer. A total of 102 graph-based, morphological, and textural features are extracted from each tissue patch in order to quantify the arrangement of structures within digitized images of prostate cancer. A support vector machine (SVM) is used to classify the prostate into benign or malignant based on four appearance features extracted from registered images. Moreover, in this paper we introduce a new approach to generate level of  cancer, that illustrate the propagation of diffusion in prostate tissues based on the analysis of the MAPS of the change of the gray level values of prostate voxel using  (GGMRF) image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Experimental results on 28 clinical diffusion weighted MRI data sets yield promising results.

Keywords: classifiers; C Timages; MAPS; Prostate cancer.


References:

1.       American Cancer Society, "Cancer Facts and Figures 2012”
2.       T. Hacklander, C. Scharwachter, R. Golz, and H. Mertens, "Value of diffusion-weighted imaging for diagnosing vertebral metastases due to prostate cancer in comparison to other primary tumors," RoFo, vol. 178, no. 4, pp. 416-424, 2006

3.       R. Shimofusa, H. Fujimoto, H. Akamata et aI., "Diffusion weighted imaging of prostate cancer," 1. of Comp. Assi. Tomography, vol. 29, no. 2, pp. 149-153, 2005.

4.       K. K. Yu and H. Hricak, "Imaging prostate cancer", Radiologic vol. 38, no. 1, pp. 59-85, 2000.

5.       S. Ikonen, P. Karkkainen, L. Kivisaari et aI., "Endorectal magnetic resonance imaging of prostatic cancer: comparison between fat-suppressed T2-weighted fast spin echo and threedimensional dual-echo, steady-state sequences," Eur. Radiology, vol. 11, no. 2, pp. 236-241, 2001.

6.       T Tamada,TSone,Y Jo, et al. "Prostate cancer: relationships between postbiopsy hemorrhage and tumor detectability at MR diagnosis." Radiology, vol. 248. pp. 531-539, 2008.

7.       K Yoshirnitsu, K Kiyoshima, H lrie, et al. "Usefulness of apparent diffusion coefficient map in diagnosing prostate carcinoma: correlation with stepwise histopathology," I MagnReson Imaging, vol. 27, pp.132-141, 2008.

8.       NihadMesanovic, HarisHuseinagic, Matija Males, ,MislavGrgic, Emir Skejic, MuamerSmajlovic ”Automatic CT Image Segmentation of the Lungs with Region Growing Algorithm”

9.       SayaniNandy, Nikita Pandey “ A Novel Approach of Cancerous Cells Detection from Lungs CT Scan Images’’ International Journal of Advanced Research in Computer Science and Software Engineering Volume 2, Issue 8, August 2012

10.    Prof. Samir Kumar Bandyopadhyay “Edge Detection From Ct Images Of Lung’’ International Journal Of Engineering Science & Advanced Technology Volume - 2, Issue - 1, 34 – 37

11.    FatmTaher, NaoufelWerghi and Hussain Al-Ahmad “Extraction of Sputum Cells using Thresholding Techniques for Lung Cancer Detection” 2012 International Conference on Innovations in Information Technology QinghuaJi,Ronggang Shi “A Noval Method of Image Segmentation Using Watershed Transformation”2011

12.    International Conference on Computer Science and Network Technology

13.    M. A. Haider, T. H. van der Kwast, J. Tanguay, A. J. Evans, A.-T. Hashmi, G. Lockwood, and J. Trachtenberg, “Combined T2-weighted and diffusion-weighted MRI for localization of prostate cancer.” AJR. American journal of roentgenology, vol. 189, no. 2, pp. 323–8, Aug. 2007

14.    Madabhushi, M. D. Feldman, D. N. Metaxas, J. Tomaszeweski, and D. Chute, “Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI.” IEEE transactions on medical imaging, vol. 24, no. 12, pp. 1611–25, Dec. 2005.

15.    R. Amelard, A. Wong, and D. A. Clausi, “Extracting Enhancing Skin Lesion Classification,” in 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, 2012, pp. 4458 – 4461.

16.    D. L. Pham, C. Xu, and J. L. Prince, “A survey of current methods in medical image segmentation,” Ann. Rev. Biomed. Eng.,Vol. 2, pp. 315_37, 1998.

17.    Olivera, J. Jordi Freixeneta, and J. Joan Mart_ia, “A review of automatic mass detection and segmentation in mammographic images,” Med. Image Anal., Vol. 14, no. 2, pp. 87_110, 2010.

6-10

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

3.

Authors:

Janhavi Suchet Vakil

Paper Title:

A Survey Paper on Identifying Candidate Features in Opinion Mining using Intrinsic and Extrinsic Domain Relevance

Abstract:  Opinion feature extraction is the process of obtaining candidate features from the existing set of features identified from reviews and opinions. We study few techniques and propose a novel method to identify candidate features using different pattern mining approaches and extract relevant information using a set of syntactic rules. Using Dependency Parsing (DP) we can extract Parts of Speech (POS). The POS can be used to extract candidate features using syntactic rules and thus obtain candidate features. According to previous studies candidate features that are less generic and more domain-specific are then confirmed as opinion features. Previous experimental results on two real evaluation domains show that this approach may surpass several other well-established methods for identifying opinion characteristics.

Keywords:  opinion mining. Sentiment analysis, Intrinsic, Extrinsic, Domain Relevance, Stanford NLP


References:
1.       Zhen Hai, Kuiyu Chang, Jung-Jae Kim, and Christopher C. Yang, “Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 3, March 2014
2.       B.  Liu,  “Sentiment  Analysis  and  Opinion  Mining,”  Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-167, May 2012.

3.       Hatzivassiloglou and j.m. Wiebe, “effects of adjective orientation and gradability on sentence subjectivity,” proc. 18th conf. Computational linguistics, pp. 299-305, 2000.

4.       B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts,” Proc. 42nd Ann. Meeting on Assoc. for Computational Linguistics, 2004.

5.       R.  Mcdonald,  K.  Hannan,  T.  Neylon,  M.  Wells,  and  J.  Reynar, “Structured Models for Fine-to-Coarse Sentiment Analysis,” Proc. 45th Ann. Meeting of the Assoc. of Computational Linguistics, pp. 432- 439, 2007.

6.       D. Bollegala, D. Weir, and J. Carroll, “Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus,” IEEE Trans. Knowledge and Data Eng., vol. 25, no. 8, pp. 1719-1731, Aug. 2013.

7.       Yessenalina and C. Cardie, “Compositional Matrix-Space Models for Sentiment Analysis,” Proc. Conf. Empirical Methods in Natural Language Processing, pp. 172-182, 2011.

8.       A.L. Maas, R.E. Daly, P.T. Pham, D. Huang, A.Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis,” Proc. 49th Ann. Meeting of the Assoc. for Computational Linguistics: Human Language Technologies, pp. 142-     150,2011. 

9.       S.-M. Kim and E. Hovy, “Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text,” Proc. ACL/COLING Workshop Sentiment and Subjectivity in Text, 2006.

10.    T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis,” Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354, 2005.

11.    Quillian, M. Ross (1966) Semantic Memory, PhD dissertation, Carnegie Institute of Technology (now CMU). Abridged version in Minsky (1968) pp. 227-270.
12.    Schank, Roger C., & Larry G. Tesler (1969) A conceptual parser for natural language, Proc. IJCAI-69, 569-578.
13.    Woods, William A. (1975) What’s in a link: foundations for semantic networks, in D. G. Bobrow & A. Collins, eds. (1975) Representation and Understanding, New York: Academic Press, pp. 35-82.

14.    Chomsky, N., "A transformational approach to syntax", Proceedings of the 1958 University of Texas Symposium on Syntax


11-15

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html



















































































4.

Authors:

A. Monica, D. Devarajan, L. Ramachandran

Paper Title:

Diffusion Tensor MRI of Human Heart using Wavelet Based Approach

Abstract: Diffusion tensor MRI of human heart is a promising but challenging technique in the medical field. This work proposed the wavelet based image fusion using MRI images and three-dimensional of the diffusion tensor imaging. Medical image fusion is the idea to improve the image content by fusing images taken from different imaging tools like Magnetic Resonance Imaging (MRI) and three dimensional Diffusion Tensor Imaging (DTI). Then the ten short axis slices images are taken entire heart using multiple shifted trigger delay under free breathing conditions. Then post processing scheme are used the algorithm based on wavelet based image fusion (WIF) method. Diffusion tensor Magnetic resonance image (MRI) image are fused and better it has been compared with existing method. The proposed of Wavelets are decompose the image into different sub-band images.

Keywords:   Diffusion tensor MRI, Wavelet transform, Image fusion, Haar wavelet based fusion


References:
1.       Shrivsubramani Krishnamoorthy, K P Soman,― Implementation and Comparative Study of Image Fusion Algorithms‖ .International Journal of Computer Applications (0975 – 8887) Volume 9– No.2, November 2010
2.       Maheswari.S1, Dr.Reeba Korah2 ―Survey on Image Fusion Algorithm‖ International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 4, Issue 1, January-February 2015 ISSN 2278- 6856

3.       Sweta K. Shah, Prof. D.U. Shah ―Comparative A Review on Image Fusion Techniques,‖ in International Journal of Innovative Research in Science, Engineering and Technology , Vol. 2, Issue 3, March 2014.

4.       Deepali Sale1, Dr. Madhuri Joshi2, Varsha Patil3 Pallavi Sonare4 ,Chaya Jadhav5 ― Image Fusion For Medical Image Retrieval‖ International Journal of Computational Engineering Research Vol, 03 Issue, 8 August2013, Issn 2250-3005

5.       Suruchi Goyal, Rupinder Wahla, ―A Review on Image Fusion‖ International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization), Vol. 3, Issue 8, August 2015, ISSN (Online): 2320-9801,ISSN (Print): 2320-9798.

6.       Ashishgoud Purushotham G. Usha Rani Samiha Naik ―:Image Fusion Using DWT & PCA‖ International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 4, 2015 ISSN: 2277 128X

7.       Deron Rodrigues, Hasan Ali Virani, Shajahan Kutty,”Mulimodal Image Fusion Techniques for medical images using wavelets”, International Journal of Research in Advent Technology, Vol.2, No.3, March 2014.

8.       P. James, B. V. Dasarathy,”Medical image fusion:A survey of the state of the art”, Information Fusion, 2014.

9.       Yueyang Guan, Anna Wang, Haijing Sun "The Application of Wavelet Transform to Multi-modality Medical Image Fusion", IEEE Int. Conf. on Networking, Sensing and Control (ICNSC '06), Florida, pp 270-274.

10.    Aganj I, Lenglet C, Yacoub E, Sapiro G, and Harel N, “A 3D wavelet fusion approach for the reconstruction of isotropic-resolution MR images from orthogonal anisotropic-resolution scans.” Magn Reson Med., vol. 67, no. 4, pp. 1167–1172, 2012.

11.    Basser PJ, and Pajevic S, “Statistical artifacts in Diffusion Tensor MRI (DT-MRI) caused by background noise.” Magn Reson Med., vol. 44, no. 1, pp. 41–50, 2000.

12.    Yang Y, Park D, Huang S, and Rao N, “Medical image fusion via an effective wavelet-based approach.” EURASIP J Advances in Signal Processing, vol. 1, pp. 1-13, 2010.

13.    Miss.Yogita Jawale, Mrs.A.G.Andurkar.,”Implementation of image fusion techniques using wavelet transform”,International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 3, March 2013

14.    Huang Xishan Chen Zhe. A wavelet –based scene image fusion algorithm[C]. Proceedings of IEEE TENCON 2002. Piscateway USA IEEE Press 2002:602-605.

15.    Mallat, S. (1989), "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp. 674–693.

16.    Matlab user manual – Image processing toolbox, MathWorks, Natick, 1999.


16-19

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html