A survey of thresholding techniques
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A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Wavelets for Computer Graphics: A Primer, Part 1
IEEE Computer Graphics and Applications
Wavelet based automatic thresholding for image segmentation
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Image Distance Using Hidden Markov Models
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
A Shape Descriptor Based on Circular Hidden Markov Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Segmentation of brain tumors in 4D MR images using the hidden Markov model
Computer Methods and Programs in Biomedicine
Semantic image segmentation with a multidimensional hidden markov model
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
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3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations.