Presence Of Intensity Inhomogeneity Research Articles

Bias field correction-based active contour model for region of interest extraction in digital image

The region-based Active Contour Model (ACM) is a widely known variational segmentation model for extracting or segmenting a digital image into numerous sections for further analysis. Distinguishing between global and specific segmentation models within this paradigm is possible. The global segmentation model is incapable of selectively segmenting the region of interest (ROI) from the input image, which leads to an over-segmented problem. A variety of models have been devised to address the task of selective segmentation, which involves the extraction of the boundary of a particular region of interest (ROI) inside a digital image. The Primal Dual Selective Segmentation (PDSS) model has been recently introduced and exhibits significant potential in terms of accuracy. Nevertheless, the presence of intensity inhomogeneity in digital images disrupts the precision and localisation of target regions of segmentation. Therefore, it is important to take into account bias field adjustment, also known as correction for intensity inhomogeneity. So, this study came up with a new selective segmentation model called the Selective Segmentation with Bias Field Correction (SSBF) model by combining the idea of the existing PDSS model with the level set-based bias field correction technique. To solve the proposed SSBF model, we first derived the Euler-Lagrange (EL) equation and solved it in MATLAB software. The Intersection over Union (IOU) coefficient, also known as the Dice (DSC) and Jaccard (JSC) similarity metrics, evaluated the proposed model's accuracy. Experimental results demonstrate that the JSC and DSC values of the proposed model were 13.4% and 7.2% higher, respectively, than the competing model.

An active contour model reinforced by convolutional neural network and texture description

Active contour models (ACMs)are popular and widely used for many image segmentationapplications and obtain promising results. However, these methods are unable to achieve the highest performance in the presence ofintensity inhomogeneity. To address this issue, this paper presents an ACM based on the combination of a convolutional neural network (CNN) and a texture descriptor approach.This study uses a CNN model to generate parameter maps more effectively for ACM. Compared to conventional global techniques, these parameter maps increase the speed of movement of the contour into the target. In this approach, the Local Word Directional Pattern (LWDP) is applied as the texture descriptor. LWDP is a texture descriptor that uses the angle between two gradients for exploring the texture structure inside the image. In the proposed method both the original image and the new image obtained bythe LWDP texture descriptor(encoded image) are provided as inputs to the CNN. The experimental outcomes show that the proposed strategy consistently outperforms thestate-of-the-art in accuracy and robustness for segmenting images with fuzzy boundaries and intensity inhomogeneity.

Quantifying surface morphology of manufactured activated carbon and the waste coffee grounds using the Getis-Ord-Gi* statistic and Ripley\u2019s K function

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

Modified silhouette based segmentation outperforming in the presence of intensity inhomogeneity in the hyperspectral images

In the real world the intensity inhomogeneity occurs presenting considerable challenges in the field of segmentation of images. In the arena of computer vision and processing of images, the method ...

Modified silhouette based segmentation outperforming in the presence of intensity inhomogeneity in the hyperspectral images

Modified silhouette based segmentation outperforming in the presence of intensity inhomogeneity in the hyperspectral images

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior.

Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.

3D unsupervised modified spatial fuzzy c-means method for segmentation of 3D brain MR image

This paper proposed a novel 3D unsupervised spatial fuzzy-based brain MRI volume segmentation technique in the presence of intensity inhomogeneity and noise. Instead of static masking, dynamic 3D masking has been proposed to measure the correlation among neighbors. The local membership function is defined based on the weighted correlation among neighbors. The local and global membership functions are combined to suppress the inhomogeneity and noise at the time of clustering. A weighted function is defined based on the 3D dynamic neighborhood to optimize the objective function in 3D space. In 2D slice-based MRI image segmentation techniques, the selection of the slice of interest is very important and it depends on the experience and skills of the expertise. As the proposed unsupervised method segments the 3D brain MRI volume as a whole, there is no need of such expertise. The detailed analysis of the results shows that the proposed unsupervised spatial fuzzy-based 3D brain MRI image segmentation technique is superior over the considered state-of-the-art methods in terms of cluster validation functions.

An Improved Active Contour Method for Medical Image Segmentation using Singular Value Decomposition

An image segmentation method uses dynamic shape models (ACM) executed by methods for level set systems have been successfully used as piece of picture division. essential idea of ACM is to undeniably address shapes zero level game plan of higher dimensional level set limit, & figure progression of frame through improvement of level set.since they always produce sub-regions with continuous boundaries. However, traditional edge-based active contour models have been applicable to only relatively simple images whose sub-regions are uniform without internal edges. A partial solution to the problem of internal edges is to partition an image based on the statistical approach i.e. Information of image intensity measured and active contour model based on level set and Singular Value Decomposition may be applied to improve the efficiency and accuracy in poor quality images. In this paper an Improved Active Contour Method for medical image segmentation is based on active appearance models, active shape models, level set,PCA and Linear Discriminant Analysis are analyzed with intent to produce an inhomogeneous environment using SVD for segmentation of real world images in the presence of intensity in-homogeneity and noise.

Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation

This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF .

Active contours driven by grayscale morphology fitting energy for fast image segmentation

An active contour model (ACM) based on grayscale morphology fitting energy for fast image segmentation in the presence of intensity inhomogeneity is proposed. The core idea of grayscale morphology fitting energy is using the grayscale erosion and dilation operations to fit the image intensities on the two sides of contours. By extracting local intensity information using morphological operators, the proposed model can effectively segment images with intensity inhomogeneity, and the computational cost is low because the grayscale morphology fitting functions do not need to be updated during the process of curve evolution. Experiments on synthetic and real images have shown that the proposed model can achieve accurate segmentation. In addition, it is more robust to the choice of initial contour and has a higher segmentation efficiency compared to traditional local fitting-based ACMs.

Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering

Mammography is the primary modality that helped in the early detection and diagnosis of women breast diseases. Further, the process of extracting the masses in mammogram represents a challenging task facing the radiologists, due to problems such as fuzzy or speculated borders, low contrast and the presence of intensity inhomogeneities. Aims to help the radiologists in the diagnosis of breast cancer, many approaches have been conducted to automatically segment the masses in mammograms. Towards this aim, in this paper, we present a new approach for extraction of tumors from region-of-interest (ROI) using the algorithm of Fuzzy C-Means (FCM) setting two clusters for semi-automated segmentation. The proposed method meant to select as input data the set of pixels that enable to get the meaningful information required to segment the masses with high accuracy. This could be accomplished through eliminating unnecessary pixels, which influence on this process through separating it outside of the input data using an optimal threshold given by monitoring the change of clusters rate during the process of threshold decrementing. The proposed methodology has successfully segmented the masses, with an average sensitivity of 82.02% and specificity of 98.23%.

Segmentation of MR Images of the Brain to Detect WM, GM, and CSF Tissues in the Presence of Noise and Intensity Inhomogeneity

ABSTRACTSegmentation of brain MR images for the detection of various healthy brain tissues such as white matter, gray matter, and cerebrospinal fluid is of immense interest to detect and to diagnose different brain-related disorders at the primitive level. MR image segmentation becomes a difficult task owing to the presence of intensity inhomogeneity (IIH), noise, partial volume effects, and intrinsic nature of the MR images. This paper proposes an efficient, region-based, energy minimization technique named as anisotropic multiplicative intrinsic component optimization (AMICO) to segment the brain image in the presence of IIH and noise and to detect different healthy brain tissues. The proposed algorithm utilizes a powerful anisotropic diffusion filter to denoise the image. The MICO algorithm segment the denoised image after correcting IIH. In the proposed technique, MR brain image is decomposed into two multiplicative and intrinsic components, such as the true image component and the bias field component. Brain tissue physical properties are represented by the component of true image and the IIH is characterized by the bias field component. Optimization of these two multiplicative and intrinsic components by employing the proposed effective energy minimization process, result in IIH correction and tissue segmentation simultaneously. The pursuance of the proposed technique is compared with some other existing techniques using the parameters, dice similarity coefficient, sensitivity, specificity, and segmentation accuracy. The results validated the excellent performance of the AMICO in detecting various brain tissues consisting of various levels of IIH and noise.

Convex‐relaxed active contour model based on localised kernel mapping

Intensity inhomogeneity is one of the major obstacles for intensity-based segmentation in many applications. The recently proposed kernel mapping (KM) method has exhibited excellent performance on segmenting various types of noisy images while it is not effective to handle intensity inhomogeneity. To overcome this drawback, this study presents a localised KM (LKM) method based on the fact that intensity inhomogeneity can be ignored in a local neighbourhood. The authors’ method first reconstructs the KM formulation of image segmentation in a neighbourhood of each pixel, and then such formulations for all pixels can be integrated together to derive the LKM energy functional. Minimisation of the energy functional is implemented by solving an equivalent convex-relaxed problem whose optimisation can be quickly achieved via the split Bregman method. Experimental results on two-phase segmentation and multiphase segmentation demonstrate competitive performance of the LKM method in the presence of intensity inhomogeneity and severe noise.

A level set method for multiple sclerosis lesion segmentation

In this paper, we present a level set method for multiple sclerosis (MS) lesion segmentation from FLAIR images in the presence of intensity inhomogeneities. We use a three-phase level set formulation of segmentation and bias field estimation to segment MS lesions and normal tissue region (including GM and WM) and CSF and the background from FLAIR images. To save computational load, we derive a two-phase formulation from the original multi-phase level set formulation to segment the MS lesions and normal tissue regions. The derived method inherits the desirable ability to precisely locate object boundaries of the original level set method, which simultaneously performs segmentation and estimation of the bias field to deal with intensity inhomogeneity. Experimental results demonstrate the advantages of our method over other state-of-the-art methods in terms of segmentation accuracy.

Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and ‘vesselness values’ from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

Regional active contours based on variational level sets and machine learning for image segmentation

Image segmentation is the problem of partitioning an image into different subsets, where each subset may have a different characterization in terms of color, intensity, texture, and/or other features. Segmentation is a fundamental component of image processing, and plays a significant role in computer vision, object recognition, and object tracking. Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution. Starting from an initial guess, the contour is evolved with the aim of approximating better and better the actual object boundary. Handling complex images in an efficient, effective, and robust way is a real challenge, especially in the presence of intensity inhomogeneity, overlap between the foreground/background intensity distributions, objects characterized by many different intensities, and/or additive noise. In this thesis, to deal with these challenges, we propose a number of image segmentation models relying on variational level set methods and specific kinds of neural networks, to handle complex images in both supervised and unsupervised ways. Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour models.

Automation of Segmentation of MRI Images in the Presence of Intensity Inhomogeneity by the Application of Probabilistic Neural Network

This paper presents a different approach by employing probabilistic neural network (PNN) with level set method for segmentation of intensity inhomogeneous magnetic resonance images (MRI). The input image is classified by PNN, and initialise the image with initial zero level set contours. A cascade approach is used in which after processing image by PNN, the output of PNN is fed into Level Set Method (LSM) Algorithm. The LSM is usedto perform the segmentation and to estimate bias field.

Fine feature retention of magnetic resonance images using intensity inhomogeneity weighted non-local filtering

Electronic noise in the presence of intensity inhomogeneity severely inhibits the robustness of many traditional filtering algorithms. We propose an adaptive filter that utilises the intensity inhomogeneity bias field often observed in magnetic resonance imaging as a map for directing the magnitude of smoothing. This adaptive filter allows for controlled filtering of images where high signal-to-noise (SNR) regions are smoothed less, while low SNR regions are smoothed more intensely. We validated our method with a phantom and MRIs of the lumbar spine. Results showed that the proposed algorithm improved filtering of noise in the presence of intensity inhomogeneity.

A Level Set Approach to Image Segmentation With Intensity Inhomogeneity.

It is often a difficult task to accurately segment images with intensity inhomogeneity, because most of representative algorithms are region-based that depend on intensity homogeneity of the interested object. In this paper, we present a novel level set method for image segmentation in the presence of intensity inhomogeneity. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances in which a sliding window is used to map the original image into another domain, where the intensity distribution of each object is still Gaussian but better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field with the original signal within the window. A maximum likelihood energy functional is then defined on the whole image region, which combines the bias field, the level set function, and the piecewise constant function approximating the true image signal. The proposed level set method can be directly applied to simultaneous segmentation and bias correction for 3 and 7T magnetic resonance images. Extensive evaluation on synthetic and real-images demonstrate the superiority of the proposed method over other representative algorithms.

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images.