DWT-PCA Image Fusion Technique to Improve Segmentation Accuracy in Brain Tumor Analysis

Rajinikanth, V.; Satapathy, Suresh Chandra; Dey, Nilanjan; Rajangam, Vijayarajan

doi:10.1007/978-981-10-7329-8_46

Cited by 54 publications

(18 citation statements)

References 9 publications

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“…Recent works in the literature confirm that heuristic algorithm supported tri-level thresholding is widely adopted to pre-process grayscales and RGB medical images [1][2][3]. The works of Rajinikanth et al [22] and Shree et al [42] confirm that SE offers enhanced image quality compared to other pre-processing schemes. Previous research by Rajinikanth and Satapathy [8], Dey et al [43] and Satapathy and Naik [44], confirms that SGO is very simple in implementation and offers better result compared to the existing heuristic approaches in the literature.…”

Section: Pre-processingmentioning

confidence: 98%

“…This work also provides a detailed comparative assessment on various tumor segmentation procedures existing in the literature. Rajinikanth et al proposed an image fusion approach to enhance the outcome during the MRI examination [22]. In their work, the BRATS dataset is considered for the examination and the fusion approach is considered to attain images like Flair+T1, Flair+T2, Flair+T1C and Flair+T2+T1C.…”

Section: Related Earlier Workmentioning

confidence: 99%

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Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey

Rajinikanth

Shi

et al. 2019

Biocybernetics and Biomedical Engineering

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Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images.

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Section: Pre-processingmentioning

confidence: 98%

Section: Related Earlier Workmentioning

confidence: 99%

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey

Rajinikanth

Shi

et al. 2019

Biocybernetics and Biomedical Engineering

Self Cite

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“…In the medical field, PCA has been applied in the fusion of MRI, CT, PET and US [41][42][43]. PCA can also combine with decomposition methods, such as IHS, the pyramid method, Discrete wavelet transform [44], the Curvelet transform, Contourlet transform [45] and Non-Subsampled Contourlet transform [31,[46][47][48][49][50][51][52][53][54][55][56].…”

Section: Principal Component Analysismentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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“…We used the MPSO technique on each 2D slice composing the 3D image, ensuring accuracy and minimizing time as much as possible. To successfully compare the proposed technique with state‐of‐the‐art brain tumor segmentation techniques, we evaluate our method on the data from Medical Image Computing and Computer Assisted Interventions (MICCAI) 2013 Multimodal Brain Tumor Segmentation (BRATS) challenge . This dataset accommodates both real patient data and simulated data.…”

Section: Introductionmentioning

confidence: 99%

3D brain tumor segmentation in MRI images based on a modified PSO technique

Gtifa

Hamdaoui

Sakly

2019

Int J Imaging Syst Tech

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Three‐dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional‐order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1‐C modality.

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DWT-PCA Image Fusion Technique to Improve Segmentation Accuracy in Brain Tumor Analysis

Cited by 54 publications

References 9 publications

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

3D brain tumor segmentation in MRI images based on a modified PSO technique

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