A Comparative Analysis of MRI Brain Tumor Segmentation Technique

Lakra, Anubha

doi:10.5120/ijca2015905922

Cited by 4 publications

(4 citation statements)

References 23 publications

(24 reference statements)

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“…The 22 features with AMSOM-FKM used by the proposed algorithm were the overcoming factor for all other techniques. The average accuracy of KMFCM [20] was 98%, SOM-FKM [19] was 94%, and the proposed was high at 99.8%. The MRI images of different data compared with different algorithms implemented by researchers formed in one tumor region are clearly illustrated in Table 4.…”

Section: Results and Analysismentioning

confidence: 95%

Section: Results and Analysismentioning

confidence: 99%

“…The significant quality parameters that determine the precision and efficiency of the proposed algorithm are presented as statistical measures, such as the mean square error, and others were evaluated using Equations ( 10)- (19). These statistical measures were briefly defined in [47][48][49][50][51][52][53].…”

Section: Performance Parametersmentioning

confidence: 99%

“…It introduced various techniques of classification. Research [ 19 ] reported the automated morphological identification and differentiation of non-enhancing tumors from stable brain tumors by localization processes. It implemented an automatic CNN segmentation method that considered all local features, an input image, and global area features—developed a fully automated brain tissue detection system using fluid-attenuated investment recovery image MRI images.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering

Dalal,

Lilhore,

Manoharan

et al. 2023

Sensors

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Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

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Section: Results and Analysismentioning

confidence: 95%

Section: Results and Analysismentioning

confidence: 99%