DSpace Community:
https://dyuthi.cusat.ac.in:443/jspui/handle/purl/442
2024-03-29T15:30:10ZAutomatic Optic Disc Boundary Extraction from Color Fundus Images
https://dyuthi.cusat.ac.in:443/jspui/handle/purl/4580
Title: Automatic Optic Disc Boundary Extraction from Color Fundus Images
Authors: Tessamma, Thomas; Thresiamma, Devasia; Poulose Jacob,K
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, 20142014-01-01T00:00:00ZAutomatic Detection and Classification of Glioma Tumors using Statistical Features
https://dyuthi.cusat.ac.in:443/jspui/handle/purl/4579
Title: Automatic Detection and Classification of Glioma Tumors using Statistical Features
Authors: Tessamma, Thomas; Ananda Resmi, S
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. 08-142014-01-01T00:00:00ZImage Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain
https://dyuthi.cusat.ac.in:443/jspui/handle/purl/4578
Title: Image Denoising Using Sure-Based Adaptive Thresholding In Directionlet Domain
Authors: Tessamma, Thomas; Sethunadh, R
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 20122012-12-01T00:00:00ZEvaluation Of A Novel Method Of Real Time Computer Assisted Spine Surgery
https://dyuthi.cusat.ac.in:443/jspui/handle/purl/4577
Title: Evaluation Of A Novel Method Of Real Time Computer Assisted Spine Surgery
Authors: Tessamma, Thomas; Nobert, Thomas Pallath; Suresh, S
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 20142014-02-01T00:00:00Z