Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization

2019 ◽  
Vol 7 (1) ◽  
pp. 119-143 ◽  
Author(s):  
Lilia Lazli ◽  
Mounir Boukadoum
Keyword(s):  
Automatic Segmentation ◽  
Volume Effect ◽  
Performance Comparison ◽  
Partial Volume ◽  
Functional Images ◽  
Possibilistic Clustering ◽  
Quantitative Results ◽  
Fuzzy Techniques ◽  
Brain Data ◽  
Fuzzy Genetic

Segmentation is a key step in brain imaging where clustering techniques are widely used, particularly the fuzzy approach which offers active and robust methods against noise and partial volume effect (PVE). To address those imperfections, this article suggests an automatic segmentation of brain tissues for magnetic resonance and functional images of Alzheimer's patients, based on an efficient and robust genetic-fuzzy-possibilistic clustering scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes. The proposed hybrid clustering process based on: 1) A fuzzy possibilistic c-means algorithm that models the degree of relationship between each voxel and a given tissue. 2) A fuzzy c-means algorithm to initialize the clusters centers, with subsequent optimization by a genetic algorithm. Each stage of the proposed clustering process is validated on real brain data and synthetic images of an Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom. A performance comparison is made with the usual fuzzy techniques. The visual and quantitative results obtained with the proposed approach using various signal-to-noise ratios prove its effectiveness to quantify the tissue volume of images of different modalities types in the presence of noise and PVE. The effectiveness in terms of computational rate is also demonstrated.

Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization

2021 ◽  
pp. 267-294
Author(s):  
Lilia Lazli ◽  
Mounir Boukadoum
Keyword(s):  
Automatic Segmentation ◽  
Volume Effect ◽  
Performance Comparison ◽  
Partial Volume ◽  
Functional Images ◽  
Possibilistic Clustering ◽  
Quantitative Results ◽  
Fuzzy Techniques ◽  
Brain Data ◽  
Fuzzy Genetic

Segmentation is a key step in brain imaging where clustering techniques are widely used, particularly the fuzzy approach which offers active and robust methods against noise and partial volume effect (PVE). To address those imperfections, this article suggests an automatic segmentation of brain tissues for magnetic resonance and functional images of Alzheimer's patients, based on an efficient and robust genetic-fuzzy-possibilistic clustering scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes. The proposed hybrid clustering process based on: 1) A fuzzy possibilistic c-means algorithm that models the degree of relationship between each voxel and a given tissue. 2) A fuzzy c-means algorithm to initialize the clusters centers, with subsequent optimization by a genetic algorithm. Each stage of the proposed clustering process is validated on real brain data and synthetic images of an Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom. A performance comparison is made with the usual fuzzy techniques. The visual and quantitative results obtained with the proposed approach using various signal-to-noise ratios prove its effectiveness to quantify the tissue volume of images of different modalities types in the presence of noise and PVE. The effectiveness in terms of computational rate is also demonstrated.

scholarly journals Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
K. Gayathri Devi ◽  
R. Radhakrishnan
Keyword(s):  
Neural Network ◽  
Automatic Segmentation ◽  
Side Wall ◽  
Volume Effect ◽  
Ct Images ◽  
Partial Volume Effect ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Partial Volume ◽  
Colon Segmentation

Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT) images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer.Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect.Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate.Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.

scholarly journals Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

2018 ◽  
Author(s):  
Reuben R Shamir ◽  
Yuval Duchin ◽  
Jinyoung Kim ◽  
Guillermo Sapiro ◽  
Noam Harel
Keyword(s):  
Subthalamic Nucleus ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Volume Effect ◽  
Measurement Tool ◽  
Dice Coefficient ◽  
Partial Volume ◽  
Partial Volume Effects ◽  
Segmentation Algorithms ◽  
Volume Effects

AbstractObjectiveOverlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structure’s size. Addressing these challenges is the objective of this work.MethodsWe extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC ≤1 and cDC = 1 if-and-only-if the structures’ overlap is complete, and; 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brain-stimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus.ResultsPartial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively.ConclusionThe cDC is well defined for probabilistic segmentation, less biased to structure’s size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.

scholarly journals Correction for the Partial Volume Effects (PVE) in Nuclear Medicine Imaging: A Post-Reconstruction Analytic Method

2021 ◽  
Vol 11 (14) ◽  
pp. 6460
Author(s):  
Fabio Di Martino ◽  
Patrizio Barca ◽  
Eleonora Bortoli ◽  
Alessia Giuliano ◽  
Duccio Volterrani
Keyword(s):  
Nuclear Medicine ◽  
Imaging System ◽  
Tomographic Reconstruction ◽  
Mathematical Expression ◽  
Reconstruction Algorithm ◽  
Volume Effect ◽  
Nuclear Medicine Imaging ◽  
Theoretical Derivation ◽  
Reconstruction Process ◽  
Partial Volume

Quantitative analyses in nuclear medicine are increasingly used, both for diagnostic and therapeutic purposes. The Partial Volume Effect (PVE) is the most important factor of loss of quantification in Nuclear Medicine, especially for evaluation in Region of Interest (ROI) smaller than the Full Width at Half Maximum (FWHM) of the PSF. The aim of this work is to present a new approach for the correction of PVE, using a post-reconstruction process starting from a mathematical expression, which only requires the knowledge of the FWHM of the final PSF of the imaging system used. After the presentation of the theoretical derivation, the experimental evaluation of this method is performed using a PET/CT hybrid system and acquiring the IEC NEMA phantom with six spherical “hot” ROIs (with diameters of 10, 13, 17, 22, 28, and 37 mm) and a homogeneous “colder” background. In order to evaluate the recovery of quantitative data, the effect of statistical noise (different acquisition times), tomographic reconstruction algorithm with and without time-of-flight (TOF) and different signal-to-background activity concentration ratio (3:1 and 10:1) was studied. The application of the corrective method allows recovering the loss of quantification due to PVE for all sizes of spheres acquired, with a final accuracy less than 17%, for lesion dimensions larger than two FWHM and for acquisition times equal to or greater than two minutes.

Partial volume effect of SPECT images in PRRT with 177Lu labelled somatostatin analogues: A practical solution

2019 ◽  
Vol 57 ◽  
pp. 153-159 ◽  
Author(s):  
Domenico Finocchiaro ◽  
Salvatore Berenato ◽  
Elisa Grassi ◽  
Valentina Bertolini ◽  
Gastone Castellani ◽  
...  
Keyword(s):  
Somatostatin Analogues ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Practical Solution ◽  
Partial Volume ◽  
Spect Images

Spectrum separation resolves partial-volume effect of MRSI as demonstrated on brain tumor scans

2008 ◽  
Vol 21 (10) ◽  
pp. 1030-1042 ◽  
Author(s):  
Yuzhuo Su ◽  
Sunitha B. Thakur ◽  
Karimi Sasan ◽  
Shuyan Du ◽  
Paul Sajda ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Partial Volume

Segmentation of the skull in MRI volumes using a deformable model and taking the partial volume effect into account

1999 ◽  
Author(s):  
Hilmi Rifai ◽  
Isabelle Bloch ◽  
Seth A. Hutchinson ◽  
Joe Wiart ◽  
Line Garnero
Keyword(s):  
Volume Effect ◽  
Partial Volume Effect ◽  
Deformable Model ◽  
Partial Volume

scholarly journals Ventricle Boundary in CT: Partial Volume Effect and Local Thresholds

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Ihar Volkau ◽  
Fiftarina Puspitasari ◽  
Wieslaw L. Nowinski
Keyword(s):  
Computed Tomography ◽  
Cerebrospinal Fluid ◽  
Grey Matter ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Partial Volume ◽  
Image Histogram ◽  
Global Threshold

We present a mathematical frame to carry out segmentation of cerebrospinal fluid (CSF) of ventricular region in computed tomography (CT) images in the presence of partial volume effect (PVE). First, the image histogram is fitted using the Gaussian mixture model (GMM). Analyzing the GMM, we find global threshold based on parameters of distributions for CSF, and for the combined white and grey matter (WGM). The parameters of distribution of PVE pixels on the boundary of ventricles are estimated by using a convolution operator. These parameters are used to calculate local thresholds for boundary pixels by the analysis of contribution of the neighbor pixels intensities into a PVE pixel. The method works even in the case of an almost unimodal histogram; it can be useful to analyze the parameters of PVE in the ground truth provided by the expert.

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