3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm
Abstract
1. Introduction
1.1. Problem Statement and Motivation
1.2. Contribution and Methodology
2. Related Work
3. Materials and Methods
3.1. Preprocessing Phase
3.2. Two Steps Dragonfly-Based Clustering Phase
Algorithm 1: Two-step Dragonfly Clustering. |
Input: dataset contains MRI brain images Output: Best solution of final cluster center () Begin Initialization phase Initialize the position of dragonfly population Xi (i = 1 2, ..., n). Initialize step vectors Δ Xi For /* is the total number of food sources (number of clusters) */ Initialize the food source within the boundary of given dataset in random order; Evaluate the better potions of food sources by applying the k-means algorithm / *Algorithm 2*/ Send the dragonflies to the food sources; / * Computed centers */ End For Dragonfly algorithm Phase Iteration = 0; Do While (the end condition is not satisfied) For i = 1:n Calculate the fitness of each dragonfly Update the food source and enemy Update w, s, a, c, f, and e Calculate S, A, C, F, and E using Equations (4) to (8) Update neighboring radius If (a dragonfly has at least one neighboring dragonfly) Update step vector (ΔX) using Equation (9) Update position vector X using Equation (10) Else Update position vector using Equation (11) End if Check and correct the new positions based on the boundaries of variables End For For Compute the probability. /* Calculate the probability for each one */ End For For If (rand ( ) < ) /* denotes the probability associated with food source */ Calculate the new fitness of the new food source using Equation (14); Select the best food source by using a greedy selection between the old and new food source; Else ; End If End For End While Output: Final clusters‘ centers. End |
Algorithm 2: K-means clustering [42]. |
Input: . // the number of clusters; dataset contains MRI brain images (2D slices). Begin Arbitrary choose objects from as the initial cluster centers; Repeat - (re) group the most similar objects into a cluster, based on the Euclidian distance between the object and the cluster centroid (mean); - Update the cluster centroid, i.e., calculate the mean value of the objects for each cluster. Until no change. |
3.3. Level Set Segmentation
Algorithm 3: Level set segmentation. |
1: Insert initial contour points using two-step DA clustering output (ROI indexes). 2: Construct a signed distance function. 3: Calculate feature image using Gaussian filter and gradient. 4: Obtain the curve’s narrow band. 5: Obtain curvature and use gradient descent to minimize energy. 6: Evolve the curve. 7: Repeat step number two and stop after obtaining the segmented region. |
4. Experimental Results
4.1. Experiment 1: Comparison with Existing Methods
4.2. Experiment 2: Model Accuracy with and without k-Means
4.3. Experiment 3: Role of DA to Reduce Level Set Iteration
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Methods | Accuracy | Recall | Precision |
---|---|---|---|
Proposed Model (Two-step DA, Level Set) | 98.20 | 95.13 | 93.21 |
Symmetry Analysis, Level Set [21] | 93.63 | 89.10 | 90.45 |
Fuzzy C-Means [22] | 85.7 | 87.6 | 72.3 |
Rough Fuzzy C-Means [22] | 91.50 | 90 | 92 |
K-means, Level Set [24] | 89.30 | 92.7 | 75.8 |
Random Forest [49] | 85.60 | 91.85 | 78.3 |
Support Vector Machine (SVM) [50] | 94.25 | 92.15 | 91.21 |
DNN Methods | Accuracy | Recall | Precision |
---|---|---|---|
Proposed Model (Two-step DA, Level Set) | 98.15 | 95.40 | 93.57 |
Two-pathway CNN [36] | 96.24 | 89.67 | 82.56 |
DNN, level set [26] | 91.58 | 96.40 | 93.23 |
Nature-Inspired Metaheuristic | Accuracy | Recall | Precision |
---|---|---|---|
DA, Level Set | 98.15 | 95.40 | 93.57 |
ABC, Level Set | 95.90 | 92.13 | 91.40 |
PSO, Level Set | 93.58 | 92.40 | 89.23 |
CF, Level Set | 96.85 | 94.32 | 92.55 |
Methods | Accuracy | Mean | Standard Deviation |
---|---|---|---|
k-means, DA and level set | 98.10 | 95.67 | 0.02 |
DA, level set | 85.67 | 82.56 | 0.04 |
Methods | Patient No.1 | Patient No.2 | Patient No.3 | Patient No.4 | Patient No.5 |
---|---|---|---|---|---|
Level set with DA clustering | 15 | 18 | 16 | 15 | 20 |
Level set without DA clustering | 252 | 330 | 371 | 266 | 407 |
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Khalil, H.A.; Darwish, S.; Ibrahim, Y.M.; Hassan, O.F. 3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm. Symmetry 2020, 12, 1256. https://doi.org/10.3390/sym12081256
Khalil HA, Darwish S, Ibrahim YM, Hassan OF. 3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm. Symmetry. 2020; 12(8):1256. https://doi.org/10.3390/sym12081256
Chicago/Turabian StyleKhalil, Hassan A., Saad Darwish, Yasmine M. Ibrahim, and Osama F. Hassan. 2020. "3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm" Symmetry 12, no. 8: 1256. https://doi.org/10.3390/sym12081256
APA StyleKhalil, H. A., Darwish, S., Ibrahim, Y. M., & Hassan, O. F. (2020). 3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm. Symmetry, 12(8), 1256. https://doi.org/10.3390/sym12081256