Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

Sumathi, R.; Mandadi, Venkatesulu

doi:10.4018/ijehmc.20210501.oa4

Cited by 5 publications

(6 citation statements)

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“…Figure 8 is the original image. Figure 9 shows the visualization results obtained by the proposed algorithm, CV, K-FCM [ 9 ], Ostu [ 30 ] and region growing algorithm [ 8 ] for brain tumor segmentation. The experimental results of the threshold algorithm were obtained by manually adjusting the threshold parameters several times.…”

Section: Resultsmentioning

confidence: 99%

“…Unsupervised segmentation does not require ground-truth images as a criterion to train the model. Although there are several general segmentation methods, such as histogram thresholding [ 7 ], region growing [ 8 ], CV, and statistical clustering [ 9 ], etc., they have failed to achieve good results in the domain of brain tumor identification. Wavelet-based methods are widely used to solve difficult and hot problems, and their effectiveness has been proven in many applications, including data compression [ 10 ], signal processing [ 11 ], image enhancement [ 12 ], image compression [ 13 ], image segmentation [ 14 ], pattern recognition [ 15 ], etc.…”

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Meng

Cattani

Villecco

2022

Entropy

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Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Meng

Cattani

Villecco

2022

Entropy

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“…Sethy et al [ 14 ] implemented a deep feature fusion technique to distinguish brain MR images using VGG-16, principal component analysis (PCA) and SVM. In [ 15 ], a new method for segmenting abnormal parts in multimodal images by combining the kernel possibilistic C-means (KPCM) clustering algorithm, particle swarm optimization (PSO), and morphological reconstruction filters is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding

Kamireddy,

Kandala,

Dhuli

et al. 2024

PLoS ONE

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Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.

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“… Mudda et al (2020) implemented an improved brain tumor classification framework based on the gray-level run-length matrix (GLRLM), center-symmetric local binary patterns (CS-LBP), and artificial neural network (ANN). Sumathi and Mandadi (2021) developed an automated segmentation approach using kernel-based probabilistic C-means (KPCM), particle swarm optimization (PSO) and morphological operations. Paul and Sivarani (2020) designed a CAD approach to detect MR-based brain tumors using fuzzy K-means clustering (FKM), gray-level co-occurrence matrix (GLCM), and a bag of visual word (BOVW) classifier.…”

Section: Introductionmentioning

confidence: 99%

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

Reddy

Batchu²,

Polinati³

et al. 2023

Front. Hum. Neurosci.

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IntroductionBrain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient’s life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm.MethodsThis paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB).ResultsThe presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models.DiscussionsBy analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.

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Towards Better Segmentation of Abnormal Part in Multimodal Images Using Kernel Possibilistic C Means Particle Swarm Optimization With Morphological Reconstruction Filters

Cited by 5 publications

References 34 publications

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

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