Fusion of SPECT and MRI images using integer wavelet transform in combination with curvelet transform

Kavitha, C.T.; Chellamuthu, C.

doi:10.1179/1743131x14y.0000000092

Cited by 10 publications

(2 citation statements)

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“…Both benign and malignant tumors have distinct shapes and structures. The proposed scheme considers three different features, namely statistical features, shape-based features, and GLCM features, by reviewing previous research papers studies [3,13,20,25]. The statistical features are the mean, median, maximum, minimum, standard deviation, and variance.…”

Section: Feature Extractionmentioning

confidence: 99%

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

Rajeena¹,

Sivakumar²

2023

Intelligent Automation &Amp; Soft Computing

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An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person's health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in recent years. This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images. The proposed approach starts with preprocessing the images to reduce the noise. Then, fusion rules are applied to get the fused image, and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region. Finally, a machine learning classifier classified the brain images into benign and malignant tumors. Computing statistical measures evaluate the classification potential of the proposed scheme. Experimental outcomes are provided, and the Enhanced Flower Pollination Algorithm (EFPA) system shows that it outperforms other brain tumor classification methods considered for comparison.

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Section: Feature Extractionmentioning

confidence: 99%

Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

Rajeena¹,

Sivakumar²

2023

Intelligent Automation &Amp; Soft Computing

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“…( Jodoin et al, 2015 ; Hansen et al, 2017 ; Zhang J. et al, 2017 ). It is necessary to fuse different modes of medical images into more informative images based on fusion algorithms, in order to provide doctors with more reliable information during clinical diagnosis ( Kavitha and Chellamuthu, 2014 ; Zeng et al, 2014 ). At present, medical image fusion has been considered in many aspects, such as the localization of brain diseases, the detection of glioma, the diagnosis of AD (Alzheimer’s disease), etc.…”

Section: Introductionmentioning

confidence: 99%

A New Pulse Coupled Neural Network (PCNN) for Brain Medical Image Fusion Empowered by Shuffled Frog Leaping Algorithm

et al. 2019

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Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly.

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