Abstract
In digital image processing, image segmentation is the key methodology which is to be used frequently. In digital image processing, noise reduction and enhancement techniques are plays as a vital role. Brain is major and major organ of the human body which is to be controlled by the nervous system. In this paper, we proposed a brain tumor image enhancement technique with the help of the ICA-LDA (independent component analysis-linear discriminate analysis algorithm with ARHE (adaptive region based histogram enhancement) model. Image fusion technique is to apply for combination of the two or more input image. In this paper, the weighted average technique is to be used for image fusion techniques. The noise reduction and enhancement techniques are to be applied in preprocessing stage. The adaptive median filter is to be used for preprocessing stage. The ARHE (adaptive region based histogram enhancement) model is to be used for enhancement present in the preprocessing stage. The feature extraction and the feature optimization have to be utilized with the ICA (independent component analysis). The LDA (linear discriminate analysis) is to be used for the classification techniques. Using this classifier which is to separate the abnormal and normal stages. When the brain tumor is denoted as abnormal case then the morphological based segmentation is to be done. The simulation and result shows the analysis of various parameters such as specificity, sensitivity, positive predictive value, negative predictive value, accuracy, precision, and recall.
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16 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04185-1
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04185-1
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Saravanan, S., Karthigaivel, R. & Magudeeswaran, V. RETRACTED ARTICLE: A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model. J Ambient Intell Human Comput 12, 4727–4735 (2021). https://doi.org/10.1007/s12652-020-01875-6
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DOI: https://doi.org/10.1007/s12652-020-01875-6