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| Abstract: | The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing |
| Description: | International Journal of Emerging Technologies in Computational and Applied Sciences, 7(1), December 2013- February, 2014, pp. 08-14 |
| URI: | http://dyuthi.cusat.ac.in/purl/4579 |
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| Automatic Detec ... g Statistical Features.pdf | (413.4Kb) |
| Abstract: | The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated |
| Description: | India Conference (INDICON), 2012 Annual IEEE |
| URI: | http://dyuthi.cusat.ac.in/purl/4318 |
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| The Effect of S ... in Malayalam Language.pdf | (833.4Kb) |
| Abstract: | This paper presents a writer identification scheme for Malayalam documents. As the accomplishment rate of a scheme is highly dependent on the features extracted from the documents, the process of feature selection and extraction is highly relevant. The paper describes a set of novel features exclusively for Malayalam language. The features were studied in detail which resulted in a comparative study of all the features. The features are fused to form the feature vector or knowledge vector. This knowledge vector is then used in all the phases of the writer identification scheme. The scheme has been tested on a test bed of 280 writers of which 50 writers having only one page, 215 writers with at least 2 pages and 15 writers with at least 4 pages. To perform a comparative evaluation of the scheme the test is conducted using WD-LBP method also. A recognition rate of around 95% was obtained for the proposed approach |
| Description: | IMACST: VOLUME 2 NUMBER 1 MAY 2011 |
| URI: | http://dyuthi.cusat.ac.in/purl/4312 |
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| Identifying Dec ... Writings in Malayalam.pdf | (535.8Kb) |
| Abstract: | On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting recognition of western, Chinese and Japanese scripts, but there are very few related to the recognition of Indic script such as Malayalam. This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using K-NN algorithm. It would help in recognizing Malayalam text entered using pen-like devices. A novel feature extraction method, a combination of time domain features and dynamic representation of writing direction along with its curvature is used for recognizing Malayalam characters. This writer independent system gives an excellent accuracy of 98.125% with recognition time of 15-30 milliseconds |
| Description: | 2010 First International Conference on Integrated Intelligent Computing |
| URI: | http://dyuthi.cusat.ac.in/purl/4095 |
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| k-NN based On-L ... cterrecognition system.pdf | (577.8Kb) |
| Abstract: | This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using Kohonen network. It would help in recognizing Malayalam text entered using pen-like devices. It will be more natural and efficient way for users to enter text using a pen than keyboard and mouse. To identify the difference between similar characters in Malayalam a novel feature extraction method has been adopted-a combination of context bitmap and normalized (x, y) coordinates. The system reported an accuracy of 88.75% which is writer independent with a recognition time of 15-32 milliseconds |
| Description: | 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009) |
| URI: | http://dyuthi.cusat.ac.in/purl/4314 |
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| On-Line Handwri ... using Kohonen Networks.pdf | (379.0Kb) |
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