Brain tumor segmentation with radius contraction and expansion based initial contour detection for active contour model

2020 ◽  
Vol 79 (33-34) ◽  
pp. 23793-23819
Author(s):  
C. Jaspin Jeba Sheela ◽  
G. Suganthi
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Detection ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Contour Model ◽  
Contraction And Expansion

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Sensor Review ◽  
2019 ◽  
Vol 39 (4) ◽  
pp. 473-487 ◽  
Author(s):  
Ayalapogu Ratna Raju ◽  
Suresh Pabboju ◽  
Ramisetty Rajeswara Rao
Keyword(s):  
Brain Tumor ◽  
Sensitivity And Specificity ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Content Type ◽  
Contour Model ◽  
The Brain ◽  
Mri Image

Purpose Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Design/methodology/approach The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training. Findings The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Originality/value This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

A novel content-based active contour model for brain tumor segmentation

2012 ◽  
Vol 30 (5) ◽  
pp. 694-715 ◽  
Author(s):  
Jainy Sachdeva ◽  
Vinod Kumar ◽  
Indra Gupta ◽  
Niranjan Khandelwal ◽  
Chirag Kamal Ahuja
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Active Contour Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Contour Model

Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation

2017 ◽  
Vol 220 ◽  
pp. 84-97 ◽  
Author(s):  
Elisee Ilunga-Mbuyamba ◽  
Juan Gabriel Avina–Cervantes ◽  
Arturo Garcia–Perez ◽  
Rene de Jesus Romero–Troncoso ◽  
Hugo Aguirre–Ramos ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Active Contour Model ◽  
Background Intensity ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Contour Model ◽  
Mr Brain

Automatic MRI Brain Tumor Segmentation System Based on Localizing Active Contour Models

2011 ◽  
Vol 219-220 ◽  
pp. 1342-1346 ◽  
Author(s):  
Ying Wang ◽  
Zhi Xian Lin ◽  
Jian Guo Cao ◽  
Mao Qing Li
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models

In this paper, an automatic segmentation system was developed for MRI brain tumor. Local region-based active contour models were suitable for heterogeneous features of brain MRI image. But the models are sensitive to initial contour, which generally requires manual setting. An automatic MRI brain tumor segmentation system were developed based on localized contour models, which can identify tumor-dominant slice, set initial contour automatically and segment tumor’s contours from all MRI slices autonomously. K-means clustering and grayscale analysis were combined to identify tumor-dominant slice. Multi-threshold algorithm with the aid of erosion and dilation operators was adopted to obtain an initial contour for the tumor-dominant slice. The segmentation contour from the local active contour models was applied as initial contours of two-side neighboring slices. MRI brain tumor data were applied to validate the automatic segmentation system.

An Efficient MRI Brain Tumor Segmentation by the Fusion of Active Contour Model and Self-Organizing-Map

2019 ◽  
Vol 40 ◽  
pp. 79-91 ◽  
Author(s):  
G. Sandhya ◽  
Giri Babu Kande ◽  
Savithri T. Satya
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Active Contour Model ◽  
Parameter Tuning ◽  
Tumor Segmentation ◽  
Self Organizing Map ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods ◽  
The Brain

Accurate detection of tumors in brain MR images is very important for the medical image analysis and interpretation. Tumors which are detected and treated in the early stage gives better long-term survival than those detected lately. This paper proposes a combined method of Self-Organizing –Map (SOM) and Active Contour Model (ACM) for the effective segmentation of the brain tumor from MR images. ACMs are energy-based image segmentation methods and they treat the segmentation as an optimization problem. The optimization function is formulated in terms of appropriate parameters and is designed such that the minimum value of its correspondence to a contour which is a near approximation of the real object boundary. The traditional ACMs depend on pixel intensity as well as very susceptible to parameter tuning and it turns out to be a challenge for these ACMs to deal the image objects of distinct intensities. Conversely, Neural Networks (NNs) are very effective in dealing inhomogeneities but usually results in noise due to the misclassification of pixels. Additionally, NNs deal the segmentation problems without objective function. Hence we proposed a framework for the brain tumor segmentation which integrates SOM with ACM and is termed as SOMACM. This works by exactly integrating the global information derived from the weights or prototypes of the trained SOM neurons to aid choosing whether to shrink or enlarge the present contour during the optimization process and is performed in an iterative way. The proposed method can deal with the images of complex intensity distributions, even in the presence of noise. Exploratory outcomes demonstrate the high accuracy in the segmentation results of SOMACM on different tumor images, compared to the ACM as well as the general SOM segmentation methods. Furthermore, the proposed framework is not highly sensitive to parameter tuning.

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