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Tessamma, Thomas; Thresiamma, Devasia; Poulose Jacob,K (2014)[+][-]
Abstract: Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases Description: (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 5, No. 7, 2014 URI: http://dyuthi.cusat.ac.in/purl/4580 Files in this item: 1
Files Size Automatic Optic ... om Color Fundus Images.pdf (602.5Kb) -
Tessamma, Thomas; Ananda Resmi, S (IJETCAS, 2014)[+][-]
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 Files in this item: 1
Files Size Automatic Detec ... g Statistical Features.pdf (413.4Kb) -
Tessamma, Thomas; Sethunadh, R (December , 2012)[+][-]
Abstract: The standard separable two dimensional wavelet transform has achieved a great success in image denoising applications due to its sparse representation of images. However it fails to capture efficiently the anisotropic geometric structures like edges and contours in images as they intersect too many wavelet basis functions and lead to a non-sparse representation. In this paper a novel de-noising scheme based on multi directional and anisotropic wavelet transform called directionlet is presented. The image denoising in wavelet domain has been extended to the directionlet domain to make the image features to concentrate on fewer coefficients so that more effective thresholding is possible. The image is first segmented and the dominant direction of each segment is identified to make a directional map. Then according to the directional map, the directionlet transform is taken along the dominant direction of the selected segment. The decomposed images with directional energy are used for scale dependent subband adaptive optimal threshold computation based on SURE risk. This threshold is then applied to the sub-bands except the LLL subband. The threshold corrected sub-bands with the unprocessed first sub-band (LLL) are given as input to the inverse directionlet algorithm for getting the de-noised image. Experimental results show that the proposed method outperforms the standard wavelet-based denoising methods in terms of numeric and visual quality Description: Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.6, December 2012 URI: http://dyuthi.cusat.ac.in/purl/4578 Files in this item: 1
Files Size Image Denoising ... In Directionlet Domain.pdf (523.7Kb) -
Tessamma, Thomas; Nobert, Thomas Pallath; Suresh, S (February , 2014)[+][-]
Abstract: In this paper the effectiveness of a novel method of computer assisted pedicle screw insertion was studied using testing of hypothesis procedure with a sample size of 48. Pattern recognition based on geometric features of markers on the drill has been performed on real time optical video obtained from orthogonally placed CCD cameras. The study reveals the exactness of the calculated position of the drill using navigation based on CT image of the vertebra and real time optical video of the drill. The significance value is 0.424 at 95% confidence level which indicates good precision with a standard mean error of only 0.00724. The virtual vision method is less hazardous to both patient and the surgeon Description: International Journal of Scientific and Research Publications, Volume 4, Issue 2, February 2014 URI: http://dyuthi.cusat.ac.in/purl/4577 Files in this item: 1
Files Size Evaluation Of A Novel Method Of........pdf (516.8Kb) -
Tessamma, Thomas; Praveen, N (2012)[+][-]
Abstract: Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification Description: (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 11, 2012 URI: http://dyuthi.cusat.ac.in/purl/4576 Files in this item: 1
Files Size Multifinger Fea ... erprint Identification.pdf (828.8Kb)
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