An automated method of segmentation for tumor detection by neutrosophic sets and morphological operations using MR images

Author(s):  
Gursangeet Kaur ◽  
Hardeep Kaur
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.


Author(s):  
Kok Ren Choy ◽  
Sanghun Sin ◽  
Yubing Tong ◽  
Jayaram K. Udupa ◽  
Dirk M. Luchtenburg ◽  
...  

Novel biomarkers of upper airway biomechanics may improve diagnosis of Obstructive Sleep Apnea Syndrome (OSAS). Upper airway effective compliance (EC), the slope of cross-sectional area versus pressure estimated using computational fluid dynamics (CFD), correlates with apnea-hypopnea index (AHI) and critical closing pressure (Pcrit). The study objectives are to develop a fast, simplified method for estimating EC using dynamic MRI and physiological measurements, and to explore the hypothesis that OSAS severity correlates with mechanical compliance during wakefulness and sleep. Five obese children with OSAS and five obese control subjects age 12-17 underwent anterior rhinomanometry, polysomnography and dynamic MRI with synchronized airflow measurement during wakefulness and sleep. Airway cross-section in retropalatal and retroglossal section images was segmented using a novel semi-automated method that uses optimized singular-value decomposition (SVD) image filtering and k-means clustering combined with morphological operations. Pressure was estimated using rhinomanometry Rohrer coefficients and flow rate, and EC calculated from the area-pressure slope during five normal breaths. Correlations between apnea-hypopnea index (AHI), EC, and cross-sectional area (CSA) change were calculated using Spearman rank correlation. The semi-automated method efficiently segmented the airway with average Dice Coefficient above 89% compared to expert manual segmentation. AHI correlated positively with EC at the retroglossal site during sleep (rs=0.74, p=0.014), and with change of EC from wake to sleep at the retroglossal site (rs=0.77, p=0.01). CSA change alone did not correlate significantly with AHI. EC, a mechanical biomarker which includes both CSA change and pressure variation, is a potential diagnostic biomarker for studying and managing OSAS.


2011 ◽  
Vol 47 (10) ◽  
pp. 3849-3852 ◽  
Author(s):  
Lei Guo ◽  
Lei Zhao ◽  
Youxi Wu ◽  
Ying Li ◽  
Guizhi Xu ◽  
...  
Keyword(s):  

Author(s):  
Priya Verma et.al., Priya Verma et.al., ◽  

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