Segmentation and Analysis of Brain Tumor Using Tsallis Entropy and Regularised Level Set

2017 ◽  
pp. 313-321 ◽  
Author(s):  
V. Rajinikanth ◽  
Steven Lawrence Fernandes ◽  
Bharath Bhushan ◽  
Harisha ◽  
Nayak Ramesh Sunder
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Tsallis Entropy

A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

2021 ◽  
Vol 352 ◽  
pp. 109091
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi ◽  
Saeed Mozaffari
Keyword(s):  
Brain Tumor ◽  
Level Set Method ◽  
Level Set ◽  
Bias Correction ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Brain ◽  
Domain Transformation

A framework combining DNN and level-set method to segment brain tumor in multi-modalities MR image

2019 ◽  
Vol 23 (19) ◽  
pp. 9237-9251 ◽  
Author(s):  
Pinle Qin ◽  
Jinjing Zhang ◽  
Jianchao Zeng ◽  
Haiyan Liu ◽  
Yuhao Cui
Keyword(s):  
Brain Tumor ◽  
Level Set Method ◽  
Level Set ◽  
Mr Image

scholarly journals Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Abdelmajid Bousselham ◽  
Omar Bouattane ◽  
Mohamed Youssfi ◽  
Abdelhadi Raihani
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
False Negative ◽  
Ground Truth ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Bioheat Equation ◽  
Brain Tumor Segmentation ◽  
Temperature Changes ◽  
Negative Results

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

Level set method with automatic selective local statistics for brain tumor segmentation in MR images

2013 ◽  
Vol 37 (7-8) ◽  
pp. 522-537 ◽  
Author(s):  
Kiran Thapaliya ◽  
Jae-Young Pyun ◽  
Chun-Su Park ◽  
Goo-Rak Kwon
Keyword(s):  
Brain Tumor ◽  
Level Set Method ◽  
Level Set ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Local Statistics

scholarly journals BRAIN TUMOR SEGMENTATION BASED ON U-NET WITH IMAGE DRIVEN LEVEL SET LOSS

2021 ◽  
Vol 59 (5) ◽  
Author(s):  
Truong Van Pham ◽  
Thao Thi Tran
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Loss Function ◽  
High Performance ◽  
Deep Neural Networks ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Coefficient ◽  
Brain Tumor Segmentation ◽  
Mri Scans

This paper presents an approach for brain tumor segmentation based on deep neural networks. The paper proposes to utilize U-Net as an architecture of the approach to capture the fine and soars information from input images. Especially, to train the network, instead of using commonly used cross-entropy loss, dice loss or both, in this study, we propose to employ a new loss function including Level set loss and Dice loss function. The level set loss is inspired from Mumford-Shah functional for unsupervised task. Meanwhile, the Dice loss function measures the similarity between the predicted mask and desired mask. The proposed approach is then applied to segment brain tumor from MRI images as well as evaluated and compared with other approaches on a dataset of nearly 4000 brain MRI scans. Experiment results show that the proposed approach achieves high performance in terms of Dice coefficient and Intersection over Union (IoU) scores.

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