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4. People's Occupations During the Sangam Age

Author

Ramathilagam S

Subjects
Microbiology
Abstract

Material resource plays an important role in the development of a country. The Sangam literature is a testament to the existence of such a material resource society in ancient Tamil society. These texts show that plowing during the Sangam period enriched the country by augmenting water resources and conserving natural fertilizers. The Sangam books also show that the hand scale industry was boomed in the absence of machinery. The Sangam songs knew that during the Sangam period there was a salt resources equal to paddy cultivation and that the economic resources were increased by marine resources. The industrial attribute of ancient Tamils cooperation was individual attribute. Bartering is a testament to the fact that they are in possession of their own goods. The need of the individual is the need of the society in which he lives. Farmers who specialize in plowing to feed the world, and other workers who produce other necessities to our life, those who barter goods cultivated from their land to people in other lands, who import goods from other nation to their homeland, and they done business by sea and land the traders was improved by economics. Teachers imparted the wealth of education they received to others, and doctors serviced life-saving careers to improve the community through education and health. The purpose of this article is to highlight the fact that Sangam literature is a document that records the ancient Tamils' advancement in education, economy and health.

Published
2022

10. Modified Bias Field Fuzzy C-Means for Effective Segmentation of Brain MRI

Author

Kannan, S. R., Ramathilagam, S., Pandiyarajan, R., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gavrilova, Marina L., editor, and Tan, C. J. Kenneth, editor

Published
2010
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20. Novel segmentation algorithm in segmenting medical images

Author

Kannan, S.R., Sathya, A., Ramathilagam, S., and Devi, R.

Subjects
Algorithm, Medical imaging equipment -- Analysis, Algorithms -- Analysis, Image processing -- Analysis, Diagnostic imaging -- Analysis
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.07.036 Byline: S.R. Kannan (a)(b), A. Sathya (b), S. Ramathilagam (c)(d), R. Devi (b) Keywords: Fuzzy c-means; Clustering; Kernel function; Gaussian function; Image segmentation Abstract: The aim of this paper is to develop an effective fuzzy c-means (FCM) technique for segmentation of Magnetic Resonance Images (MRI) which is seriously affected by intensity inhomogeneities that are created by radio-frequency coils. The weighted bias field information is employed in this work to deal the intensity inhomogeneities during the segmentation of MRI. In order to segment the general shaped MRI dataset which is corrupted by intensity inhomogeneities and other artifacts, the effective objective function of fuzzy c-means is constructed by replacing the Euclidean distance with kernel-induced distance. In this paper, the initial cluster centers are assigned using the proposed center initialization algorithm for executing the effective FCM iteratively. To assess the performance of proposed method in comparison with other existed methods, experiments are performed on synthetic image, real breast and brain MRIs. The clustering results are validated using Silhouette accuracy index. The experimental results demonstrate that our proposed method is a promising technique for effective segmentation of medical images. Author Affiliation: (a) Department of Electrical Engineering, National Cheng Kung University, Tainan 70701, Taiwan (b) Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, India (c) Department of Engineering Science, National Cheng Kung University, Tainan 70701, Taiwan (d) Department of Mathematics, MVM Govt. College, Dindigul, India Article History: Received 22 September 2009; Revised 29 June 2010; Accepted 15 July 2010

Published
2010

38. Modified Bias Field Fuzzy C-Means for Effective Segmentation of Brain MRI.

Author

Kannan, S. R., Ramathilagam, S., and Pandiyarajan, R.

Abstract

In recent day, segmentation of brain Magnetic resonance Image (MRI) with bias field correction is challenging and unavoidable in high magnetic imaging. The brain MRI is affected by bias field that causes the undesired effect of quantitative image analysis. The removal of bias field distortion is useful in segmenting medical images for proper study of medical images. In this paper, we propose three new Fuzzy c-Means (FCM) algorithms namely Robust Gaussian based Weighted Bias Field FCM [RGBFCM], Spatial constraint Gaussian based bias field FCM [GBFCM_S], Novel Penalized Gaussian based Bias field FCM [NPGBFCM] in order to remove bias field distortion and to obtain well segmentation result. The proposed methods are capable to deal with the intensity in-homogeneities and noisy image effectively. Further, to reduce the number of iterations, the proposed algorithms initialize the centroid using dist-max initialization algorithm before the execution of algorithms iteratively. To show the performance of proposed methods, this paper applies them to segmentation of brain MRIs and compares the results of our proposed methods with other reported methods. The segmentation accuracy of proposed method is validated by using Silhouette method. The experimental results on real T1-T2 weighted and simulated brain MRIs show that our methods are superior in providing better segmentation results than standard fuzzy c-means based algorithms. [ABSTRACT FROM AUTHOR]

Published
2011
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40. Effective FCM noise clustering algorithms in medical images.

Author

Kannan, S. R., Devi, R., Ramathilagam, S., and Takezawa, K.

Subjects
*DIAGNOSTIC imaging, *ALGORITHMS, *FUZZY systems in medicine, *IMAGE segmentation, *EUCLIDEAN distance, *ROBUST control
Abstract

The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index. [ABSTRACT FROM AUTHOR]

Published
2013
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