Finite element modeling of haptic thermography: A novel approach for brain tumor detection during minimally invasive neurosurgery

Sadeghi-Goughari, Moslem; Mojra, Afsaneh

doi:10.1016/j.jtherbio.2015.08.011

Cited by 7 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main objective of this was to improvebrain tumor classification by utilizing this support scheme for secure connection of networks in medical centers. In 2015,Goughari and Mojra [26] utilized the technique named "haptic thermography" whichwas coupled with an artificial tactile sensing method for searching the tumor presence with normal tissues relative to eminent temperature. This technique's resultant outcome wasprovenwith appropriate temperature distribution.…”

Section: Related Workbased On Brain Tumor Classificationmentioning

confidence: 99%

“…3. Others Genetic algorithm [1] Fisher Criteria [2] Kernel clustering [3] Classification forest [5] Graph cut algorithm [8] Multiclass Classification [10] Extremely randomized tree-based Classification [17] Quantitative metric-based classification [18] Two-tier classification approach [20] Linear discriminant analysis (LDA) [22] Multiclass classification [23] Fuzzy logic-based hybrid kernel [24] Intraoperative Thermal Imaging (ITI) [26] Intensity-Curvature Measurement [28] Ensemble classifier [29] Local independent projection-based classification (LIPC) [32] Adaboost classifier [36] Fisher's rank-reduced version of LDA FLDA) [37] Region Growing Threshold [38] Prior voxel-based classification [39] Soft computing strategies [41] MethPed classifier [42] SNN [45] LP-iDOPE, [46] Luciferase NanoLuc [48] MR spectroscopy (MRS) [49] MP-KDD algorithm [50] Hybrid graphene-copper UWB array sensor [51] ICA [53] ICA [54] Multivariate correspondence factorial analysis [55] Intracranial glioma model [56] Diffuse Reflectance Spectroscopy Instrument [58] Adaptive dictionary learning [59] Radial basis function [60] Supervised classification [61] MRS classifier [62] Naive Bayes (NB) classifier [64] Brain tumour initiating cell (BTIC) marker …”

Section: Segmentation Algorithm Analysismentioning

confidence: 99%

“…Some of the other performance measures that are used in this reviewed work are classification rate, normalized density, frequency, lifetime, viability, and so on. Oth ers Others Watershed Segmentation [1] Kernel sparse Coding [3] Fuzzy C means [4] Spectral clustering [5] Cognition based Modified Level Set Segmentation [6] FCM based multilevel image segmentation [8] Super-pixel based segmentation [10] DWT [12] SOM clustering [13] BMM-LA [14] Wavelet-based texture segmentation [15] M-ACA-Based Tumour Segmentation [16] Super-pixel segmentation [17] Quantitative metric cum classifier [18] K-means algorithm [20] Independent component analysis (ICA) [21] Self-Organizing Map [26] Wavelet decomposition and modified FCM (MFCM) [29] Super-pixel over-segmentation algorithm [30] Graph-based seeded segmentation [32] Content-based active contour [34] Graph cuts segmentation [35] PTPSA fractal-MultiFD [36] Weighted aggregation [39] Wavelet decomposition and MFCM clustering [43] FMRIB automated segmentation tool (FAST) [44] SNN [45] Threshold Based Region Optimization [50] Adaptive global threshold [65] [23]…”

Section: Performance Analysismentioning

confidence: 99%

See 2 more Smart Citations

A Compact Review on State-of-the-Art Brain Tumor Classification Models

Amit Uttarkar

2024

View full text Add to dashboard Cite

The brain tumor is normally caused by the occurrence of abnormal cells in the brain region. Malignant or cancerous and benign tumors are the two major types in the brain tumor classification. Brain tumor classification is the process of differentiating various stages of tumors like grading of gliomas as well as primary gliomas from metastases. The diagnosis of a brain tumor was made by the study of MR images. Some of the notable brain tumor classification techniques are knowledge-based techniques, support vector machine classifiers (SVM), and neural network classifiers. This survey intends to provide a review of65 paperson the topic of brain tumor classification. Mainly, the review comes with two major aspects: the analysis of classification algorithms and the analysis of segmentation algorithms. At first, aclear literature review is made in terms of various brain tumor classification models. Subsequently, the analysis is made under the performance measure especially the accuracy rate is analyzed from all the reviewed papers. Further analysis is made regarding the used dataset, image modalities, and the used optimization concept as well. All the analytical results are explained in terms of tabulation and diagrammatic graphical representation. Finally, the clear problem statement is described showingthe different challenges faced in the classification process and the future direction that is to be made.

show abstract

Section: Related Workbased On Brain Tumor Classificationmentioning

confidence: 99%

Section: Segmentation Algorithm Analysismentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Compact Review on State-of-the-Art Brain Tumor Classification Models

Amit Uttarkar

2024

View full text Add to dashboard Cite

show abstract

“…Aside from iMRI, there are reports of intraoperative imaging utilizing CT and ultrasound in attempts to acquire real‐time data on brain tumours affected by brain shift. Despite all advances in image‐guided neurosurgery, intraoperative imaging techniques such as iMRI and iCT are classified as invasive methods since patients are exposed to the harmful impacts of magnetic fields and x‐rays . Furthermore, they are expensive and time‐consuming because each scan takes up to 20 min .…”

Section: Introductionmentioning

confidence: 99%

An experimental and numerical study on tactile neuroimaging: A novel minimally invasive technique for intraoperative brain imaging

Sadeghi-Goughari

Qian

Jeon

et al. 2018

Robotics Computer Surgery

Self Cite

View full text Add to dashboard Cite

BackgroundThe success of tumor neurosurgery is highly dependent on the ability to accurately localize the operative target, which may be shifted during the operation. Performing an intraoperative brain imaging is crucial in minimally invasive neurosurgery to detect the effect of brain shift on the tumor's location, and to maximize the efficiency of tumor resection. MethodThe major objective of this research is to introduce the tactile neuroimaging as a novel minimally invasive technique for intraoperative brain imaging. To investigate the feasibility of the proposed method, an experimental and numerical study was first performed on silicone phantoms mimicking the brain tissue with a tumor. Then the study was extended to a clinical model with the meningioma tumor. ResultsThe stress distribution on the brain surface has high potential to intraoperatively localize the tumor. ConclusionResults suggest that tactile neuroimaging can be used to provide a non-invasive, and real-time intraoperative data on tumor's features.

show abstract

“…A temperature difference up to 3.3°C was recorded by Kateb et al for a cancerous tumor. In 2 different studies, Sadeghi Goughari et al introduced a new thermography technique for detection of brain tumors, which is an aggregation of tissue cells. In their studies, a brain tumor was considered to have a 3.3°C temperature rise relative to the normal surrounding tissue.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for early evaluation of orthodontic process by a numerical thermomechanical analysis

Heidary

Mojra

Shirazi

et al. 2017

Numer Methods Biomed Eng

View full text Add to dashboard Cite

The main objective of this paper is to propose a novel method that provides an opportunity to evaluate an orthodontic process at early phase of the treatment. This was accomplished by finding out a correlation between the applied orthodontic force and thermal variations in the tooth structure. To this end, geometry of the human tooth surrounded by the connective soft tissue called the periodontal ligament and the bone was constructed by employing dental CT scan images of a specific case. The periodontal ligament was modeled by finite strain viscoelastic model through a nonlinear stress-strain relation (hyperelasticity) and nonlinear stress-time relation (viscoelasticity). The tooth structure was loaded by a lateral force with 15 different quantities applied to 20 different locations, along the midedge of the tooth crown. The resultant compressive stress in the periodontal ligament was considered as the cause of elevated cell activity that was modeled by a transient heat flux in the thermal analysis. The heat flux value was estimated by conducting an experiment on a pair of rats. The numerical results showed that by applying an orthodontic force to the tooth structure, a significant temperature rise was observed. By measuring the temperature rise, the orthodontic process can be evaluated. KEYWORDS finite element method, mechanical stress, orthodontic loading, thermal analysis, viscohyperelastic behavior

show abstract

Finite element modeling of haptic thermography: A novel approach for brain tumor detection during minimally invasive neurosurgery

Cited by 7 publications

References 34 publications

A Compact Review on State-of-the-Art Brain Tumor Classification Models

A Compact Review on State-of-the-Art Brain Tumor Classification Models

An experimental and numerical study on tactile neuroimaging: A novel minimally invasive technique for intraoperative brain imaging

A novel approach for early evaluation of orthodontic process by a numerical thermomechanical analysis

Contact Info

Product

Resources

About