An effective technique for three dimensional reconstruction of craniofacial structure from the contours of CT slices is presented. Preprocessing is done to suppress the noise and distortion introduced during image acquisition. Discrete Curvelet transform is applied to extract the contours of individual slices. The edge points are stacked to obtain the 3D point cloud and it is Delaunay triangulated for reconstruction. The edge detection performance is compared with Laplacian, Sobel and Canny edge detectors. The final reconstructed 3D structure is compared with those edge-detector-based reconstruction (with the same set of 2D slices) on the basis of number of triangular indices and time complexity. Copyright © 2012 Inderscience Enterprises Ltd.

Menaka R

Electronics

School of Electronics Engineering

Chennai Campus

Karthik R

Chellamuthu C

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Tomographic 3D cranio-facial reconstruction using curvelet transform

International Journal of Biomedical Engineering and Technology

This work introduces an efficient approach for brain tumour detection using curvelet transform-based statistical features combined with GLCM (Grey Level Cooccurrence Matrix) texture features. The detection of the brain tumour is considered as a challenging problem, due to the irregularity of the highly varying structure of the tumour cells. The major contribution of the proposed work resides in the selection of significant features from both spatial and frequency domains for training the system. It combines the curvelet transform-based statistical features in the frequency domain with the GLCM texture features in the spatial domain. The proposed method applies skullstripping as the pre-processing step to extract the brain portion from the MRI slice. This pre-processed image is subjected to watershed transform-based segmentation process to extract the necessary region of interest. From the extracted region of interest, frequency and spatial domain-based features are extracted. Finally, the classification model is developed using support vector machine. Experiments reveal that the proposed classifier is good in terms of accuracy. Copyright © 2015 Inderscience Enterprises Ltd.

A comprehensive framework for classification of brain tumour images using SVM and curvelet transform

International Journal of Computer Applications

Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform

2010 Sixth International Conference on Natural Computation

Curvelet feature extraction for face recognition and facial expression recognition

2010 International Conference on Intelligent and Advanced Systems

Curvelet based feature extraction method for breast cancer diagnosis in digital mammogram

2009 16th IEEE International Conference on Image Processing (ICIP)

Extraction of retinal blood vessels by curvelet transform

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Automatic detection of small bowel tumors in capsule endoscopy based on color curvelet covariance statistical texture descriptors

Journal	International Journal of Biomedical Engineering and Technology
Publisher	Inderscience Publishers
ISSN	1752-6418
Open Access	0