scholarly journals A novel combined level set model for automatic MR image segmentation

2020 ◽  
Vol 6 (3) ◽  
pp. 20-23
Author(s):  
Jianzhang Li ◽  
Sven Nebelung ◽  
Björn Rath ◽  
Markus Tingart ◽  
Jörg Eschweiler
Keyword(s):  
Magnetic Resonance ◽  
Level Set Method ◽  
Level Set ◽  
Object Segmentation ◽  
Magnetic Resonance Images ◽  
Local Region ◽  
Dice Similarity Coefficient ◽  
Edge Detector ◽  
Level Set Model

AbstractMedical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities are eliminated by the local region intensity descriptor. The edge detector helps the level set model to locate the object boundaries more precisely. The proposed model was validated on synthesized images and magnetic resonance images of in vivo wrist bones. Comparing with the ground truth, the proposed method reached a Dice similarity coefficient of > 0.99 on all image tests, while the compared segmentation approaches failed the segmentations. The presented combined level set model can be used for the object segmentation in magnetic resonance images.

Inhomogeneous Brain Magnetic Resonance Images Segmentation Using a Novel Double Level Set Method

2020 ◽  
Vol 10 (10) ◽  
pp. 2452-2458
Author(s):  
Jianhua Song ◽  
Shuqin Li
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Level Set Method ◽  
Level Set ◽  
Magnetic Resonance Images ◽  
Heaviside Function ◽  
Mr Image ◽  
Level Set Function ◽  
Clustering Model ◽  
Mr Image Segmentation

Magnetic resonance (MR) image segmentation plays an important role in the clinical diagnosis and pathological analysis of brain diseases, and has become a focus in the field of medical image processing. However, MR image segmentation is also a complex task because it is easily corrupted by inhomogeneous intensity and noise during the process of imaging. In this paper, we use double level set function to replace single level set of the data energy fitting model and propose a model based on Legendre polynomial and Heaviside function, which is used to segment brain magnetic resonance images. The double level set method (DLSM) can extract simultaneously the white matter (WM) and gray matter (GM) of brain tissue and ensure the robustness of level set initialization. Moreover, the bias field caused by intensity inhomogeneity is represented by a set of smooth basis functions, which can satisfy its property of slow variety. Finally, compared with the local intensity clustering model and multiplicative intrinsic component optimization model, both visual and objective results can prove the superior of the proposed DLSM model, and the computational speed is faster.

Does the Modified Arrhenius Model Reliably Predict Area of Tissue Ablation After Magnetic Resonance-Guided Laser Interstitial Thermal Therapy for Pediatric Lesional Epilepsy?

2021 ◽  
Author(s):  
Kelsey D Cobourn ◽  
Imazul Qadir ◽  
Islam Fayed ◽  
Hepzibha Alexander ◽  
Chima O Oluigbo
Keyword(s):  
Magnetic Resonance ◽  
Linear Regression ◽  
Magnetic Resonance Images ◽  
Thermal Therapy ◽  
Invasive Technique ◽  
Hypothalamic Hamartoma ◽  
Arrhenius Model ◽  
Laser Interstitial Thermal Therapy ◽  
Accuracy And Precision

Abstract BACKGROUND Commercial magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) systems utilize a generalized Arrhenius model to estimate the area of tissue damage based on the power and time of ablation. However, the reliability of these estimates in Vivo remains unclear. OBJECTIVE To determine the accuracy and precision of the thermal damage estimate (TDE) calculated by commercially available MRgLITT systems using the generalized Arrhenius model. METHODS A single-center retrospective review of pediatric patients undergoing MRgLITT for lesional epilepsy was performed. The area of each lesion was measured on both TDE and intraoperative postablation, postcontrast T1 magnetic resonance images using ImageJ. Lesions requiring multiple ablations were excluded. The strength of the correlation between TDE and postlesioning measurements was assessed via linear regression. RESULTS A total of 32 lesions were identified in 19 patients. After exclusion, 13 pairs were available for analysis. Linear regression demonstrated a strong correlation between estimated and actual ablation areas (R2 = .97, P < .00001). The TDE underestimated the area of ablation by an average of 3.92% overall (standard error (SE) = 4.57%), but this varied depending on the type of pathologic tissue involved. TDE accuracy and precision were highest in tubers (n = 3), with average underestimation of 2.33% (SE = 0.33%). TDE underestimated the lesioning of the single hypothalamic hamartoma in our series by 52%. In periventricular nodular heterotopias, TDE overestimated ablation areas by an average of 13% (n = 2). CONCLUSION TDE reliability is variably consistent across tissue types, particularly in smaller or periventricular lesions. Further investigation is needed to understand the accuracy of this emerging minimally invasive technique.

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