Integrated spatial fuzzy clustering with variational level set method for MRI brain image segmentation

Duth, P Sudharshan; Vipuldas, C. A.; Saikrishnan, V. P.

doi:10.1109/iccsp.2017.8286650

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Nevertheless, as far as the level-set methods are concerned, speed is required in the acquisition as well as improved resolution of the imaging modalities [3,26,32,47]. With respect to the level-set function ( ) , a common representation involves defining the segmentation contour as well as the region on an image I(x) for the purpose of minimizing the following energy function [118]:…”

Section: Level-set Methodsmentioning

confidence: 99%

Biomedical Image Segmentation: A Survey

Alzahrani

Boufama

2021

SN COMPUT. SCI.

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Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D-medical images. The latter come from different modalities, such as Magnetic Resonance Imaging (MRI), X-Rays, Positron Emission Tomography (PET)/Single-Photon Emission Computed Tomography, Computed Tomography (CT), and Ultrasound (US). It is a key supporting technology for medical applications including diagnostics, planning, monitoring, and guidance. Hence, a large number of segmentation methods have been published in past decades. This paper presents a comprehensive review of the current medical segmentation techniques. In particular, we reviewed the most important medical segmentation methods that have been utilized for almost all types of medical images. We grouped these methods into categories and then compared, contrasted, and highlighted their main advantages and limitations.

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Section: Level-set Methodsmentioning

confidence: 99%

Biomedical Image Segmentation: A Survey

Alzahrani

Boufama

2021

SN COMPUT. SCI.

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“…The detection of limitations in segmentation and morphological operation allows the existence or absence of renal cyst and carcinoma to be diagnosed, which leads to the new formation of tumors in the kidney at an early stage and improves classi ication accuracy. (Duth et al, 2017) the method proposed follows Spatial Kernel Fuzzy C -Means (SKFCM) and Variational Level Set Method (VLSM) to minimize all of these imperfections. SKFCM is related to the standard Fuzzy C -Means algorithm making use of the Gaussian RBF kernel as a distance metric incorporating spatial information.…”

Section: Methodsmentioning

confidence: 99%

Kidney tumor detection and classification using image processing

Rohith

Simmi

Narasimha

2019

ijrps

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Kidney cancer is a disorder where the cells of the kidney become abnormal and develop into a tumor. It is also referred to as renal cell carcinoma. The detection of kidney tumor in the early stages is very important; it is said that different imaging techniques can help doctors to decide the cancer stage and determine the appropriate treatment method. The detection and diagnosis of kidney tumors are performed by scanning images of computed tomography. Most existing works in renal kidney cancer is detection and diagnosis of the existence or absence of tumor in the kidney and classify the tumor as harmless or malevolent. The fundamental goal of this paper is to propose a strategy that will characterize the type of cells present in the kidney tumor. This can be accomplished through methods like segmentation, feature extraction using GLCM and classi ication using multiSVM.

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“…The inhomogeneity of image intensity can have a great impact on image segmentation, and Kass et al proposed a widely used model for inhomogeneous density images, active contour model (ACM) [7]. Meanwhile, the field of medical image segmentation faces similar problem with inhomogeneous images, in order to solve this problem, the level set method (LSM) is mainly used to segment the target, [8][9][10][11]. Li et al proposed Distance Regularized Level Set Evolution model (DRLSE) [12], which facilitates the convergence of active contours by using length and region terms, and introduces distance regularzation terms to avoid periodic initialisation energy.…”

Section: Introductionmentioning

confidence: 99%

The Segmentation of Wind Turbine Defect Based on UAV Infrared Image

Wang,

Jiang,

Wen

et al. 2023

Proccedings of the 2023 International Conference on Quantitative InfraRed Thermography

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During the operation of wind power systems, problems such as damage may occur, in order to keep the system running normally and safely, regular maintenance of wind turbines is essential. Currently, the main treatment is to detect the blades by carrying an infrared camera on board a UAV. However, infrared images can suffer from significant inhomogeneities due to the inconsistent response of the detector array of the infrared camera, the inconsistent spectral response of the infrared detector to environmental variations and incoming background radiation, and the combined effect of the camera's optical lens. The inhomogeneity of an image can greatly affect the accuracy of image segmentation. To solve this problem, In this paper, we propose a method for rapidly segmenting inhomogeneous images. Specifically, we introduce global and local information of the image into the energy term. We also introduce a detail map as the reference map for the edge stopping function, which improves the robustness of the image to the initial contour and improves the segmentation accuracy of inhomogeneous images while speeding up the segmentation. Correction of bias field is also realized simultaneously. The results demonstrate that our method achieves average accuracy of 96% in the task of performing segmentation of wind turbine defects with the shortest running time. Notably, our segmentation efficiency surpasses that of mainstream level-set techniques.

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Integrated spatial fuzzy clustering with variational level set method for MRI brain image segmentation

Cited by 6 publications

References 5 publications

Biomedical Image Segmentation: A Survey

Biomedical Image Segmentation: A Survey

Kidney tumor detection and classification using image processing

The Segmentation of Wind Turbine Defect Based on UAV Infrared Image

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