Image Inhomogeneity Research Articles

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

Accurate construction of 3-D numerical breast models with anatomical information through MRI scans

The realistic numerical breast model is an important tool for verifying algorithms, improving design concepts, and exploring new technologies related to microwave breast cancer detection. Recently, several numerical breast models have been developed. However, these models do not include the real breast boundary and the chest region, which causes the electromagnetic wave propagation in the breast models to deviate from the actual situation. The proposed breast models in this paper overcome the significant deficiencies of the recent breast models in terms of structure completeness and authenticity. The model construction based on magnetic resonance imaging (MRI) scans is a multistep approach. Firstly, intensity inhomogeneity in MRI images is corrected by an improved nonparametric nonuniform intensity normalization (N4) algorithm. Then, a dual threshold and morphological transformation (DTMT) method is developed to segment the real breast region. Subsequently, a modified maximum inter-class variance (MICV) method is employed to automatically divide the breast region into the fat and the fibroglandular clusters with desired complexity. Finally, the dielectric properties of breast tissues are calculated by a piecewise weighted mapping method. The applicability and effectiveness of the proposed method are verified by constructing four breast models with varying tissue density, shape, and size. The constructed models in this study are significant to achieve reliable and precise effects for microwave breast cancer detection due to the accurate expression of the breast anatomical information and electromagnetic characteristics.

Float-laser device for measuring the water table and freshwater–saltwater interface in laboratory-scale experimental systems

Processes in coastal aquifers involving variable-density fluids are often investigated using laboratory sand tanks. However, experimental monitoring of the interface between liquids of different densities is challenging, typically requiring dye tracers and photographic analysis. These are susceptible to problems of tracer adsorption and image inhomogeneity, and experiments are often limited to two-dimensional cross-sections. In addition, the water-table position is an important factor, but its accurate measurement is often difficult. A device has been developed for sand-tank experiments to obtain high-frequency, high-precision measurements of both the freshwater–saltwater interface (inferred from density) and the water-table depth, using a float and a laser sensor in a monitoring well. The proposed device was tested using a two-column experiment, allowing for the estimation of measurement errors, which were generally less than 1 mm. The measurement device can be modified to allow for the monitoring of multiple salinity values, and is well suited to sand-box experiments where salinity dynamics are otherwise difficult to visualize.

Superpixel-based brain tumor segmentation in MR images using an extended local fuzzy active contour model

In this paper, to deal with poor boundaries in the presence of noise and heterogeneity of magnetic resonance (MR) images, a new region-based fuzzy active contour model based on techniques of curve evolution is introduced for the brain tumor segmentation. On the other hand, since brain MR images intrinsically contain significant amounts of dark areas such as cerebrospinal fluid, therefore for properly declining the heterogeneity of classes and better segmentation results, the proposed fuzzy energy-based function has been extended to consider three distinct regions; target, dark tissues with a dark background and the rest of the foreground. Moreover, due to the inevitable dependency of pixel-based models on the initial contour, artifact, and inhomogeneity of MR images, we have used superpixels as basic atomic units not only to reduce the sensitivity to the mentioned factors but also to reduce the computational cost of the algorithm. Results show that the proposed method outperforms the accuracy of the state-of-the-art models in both real and synthetic brain MR images.

Impurities detection in edible bird’s nest using optical segmentation and image fusion

The cleanliness of edible bird’s nest (EBN) is among the determinative factors for market acceptance. As it is meant for human consumption, EBN should be free of any impurities or matter which are foreign to it, such as bird feathers, egg fragments and droppings. However, natural variations in composition, density and thickness impose inconsistency to the level of translucency and colour of EBN, resulting in intensity inhomogeneity in EBN images that substantially reduce the accuracy of the segmentation and detection of impurities. Consequently, the segmentation and detection of impurities, which are essential to visual automation in the cleaning and inspection processes, remain unsolved. This work proposes a novel optical segmentation method to segment impurities from the EBN, in order to facilitate the detection of impurities. EBN images captured under two different lighting scenarios, namely, low-angle blue-diffused lighting and red-diffused backlighting, were used to prepare the fused image for background-EBN-impurities segmentation. The applicability of the method was demonstrated by comparing the detection results with those of human inspectors. With a simple thresholding operation performed on fused images, the impurities detection algorithm recorded a true positive/recall rate of 93.39%, a precision of 71.17% and a false-negative detection rate of 4.8%. Despite the high misclassification rate of 32.25%, the algorithm was able to detect more than 93% of the impurities, compared to human inspectors, who required a second examination on the EBNs.

A novel combined level set model for automatic MR image segmentation

Abstract Medical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities are eliminated by the local region intensity descriptor. The edge detector helps the level set model to locate the object boundaries more precisely. The proposed model was validated on synthesized images and magnetic resonance images of in vivo wrist bones. Comparing with the ground truth, the proposed method reached a Dice similarity coefficient of > 0.99 on all image tests, while the compared segmentation approaches failed the segmentations. The presented combined level set model can be used for the object segmentation in magnetic resonance images.

Temporal resolution doubling in fluorescence light-sheet microscopy via a hue-encoded shutter and regularization

Studying dynamic biological processes, such as heart development and function in zebrafish embryos, often relies on multi-channel fluorescence labeling to distinguish multiple anatomical features, yet also demands high frame rates to capture rapid cell motions. Although a recently proposed method for imaging dynamic samples in transmission or reflection allows to conveniently switch between color imaging or boosting the frame rate by use of spectrally-encoded, temporally-modulated illumination sequences and a hue-encoded shutter (hue-encode shutter method, HESM), the technique is not applicable directly in fluorescence microscopy, where the emitted light spectrum is mostly independent of the excitation wavelength. In this paper, we extend HESM by using samples labeled with multiple fluorophores, whose emission signal can either be used to distinguish multiple anatomical features when imaged in multi-channel mode or, if the fluorophores are co-localized in a dynamic tissue, to increase the frame rate via HESM. We detail the necessary steps to implement this method in a two-color light-sheet microscope to image the beating heart of a zebrafish embryo. Specifically, we propose an adapted laser modulation scheme for illumination, we identify caveats in choosing a suitable multi-color fluorophore labeling strategy, and derive an ℓ1-regularized reconstruction technique that is sufficiently robust to handle the low signal-to-noise ratio and labeling inhomogeneities in the fluorescence images at hand. Using the case of a beating heart in a zebrafish embryo, we experimentally show an increase in the frame rate by a factor two while preserving the ability to image static features labeled in distinct channels, thereby demonstrating the applicability of HESM to fluorescence. With a suitable illumination setup and fluorescent labeling, the method could generalize to other applications where flexibility between multiple channel and high-speed fluorescence imaging is desirable. For fluorophores that are not co-localized, the imaging system is similar to a conventional light sheet microscope.

CircleNet for Hip Landmark Detection

Landmark detection plays a critical role in diagnosis of Developmental Dysplasia of the Hip (DDH). Heatmap and anchor-based object detection techniques could obtain reasonable results. However, they have limitations in both robustness and precision given the complexities and inhomogeneity of hip X-ray images. In this paper, we propose a much simpler and more efficient framework called CircleNet to improve the accuracy of landmark detection by predicting landmark and corresponding radius. Using the CircleNet, we not only constrain the relationship between landmarks but also integrate landmark detection and object detection into an end-to-end framework. In order to capture the effective information of the long-range dependency of landmarks in the DDH image, here we propose a new context modeling framework, named the Local Non-Local (LNL) block. The LNL block has the benefits of both non-local block and lightweight computation. We construct a professional DDH dataset for the first time and evaluate our CircleNet on it. The dataset has the largest number of DDH X-ray images in the world to our knowledge. Our results show that the CircleNet can achieve the state-of-the-art results for landmark detection on the dataset with a large margin of 1.8 average pixels compared to current methods. The dataset and source code will be publicly available.

Research on GIS partial discharge light measuring method based on Fluorescent fiber sensing system

In order to accurately detect the partial discharge information inside GIS, this paper proposes a PD measurement method based on fluorescent fiber sensing system. Firstly, the typical physical model reflecting four typical insulation defects in GIS is perfected. The PD optical signal is obtained through the detection system, and the corresponding 3D map and its gray image are constructed. At the same time, for the simple fractal is not enough to accurately describe the characteristics of PD discharge mode, a multi-fractal spectrum algorithm for partial discharge gray image is proposed. Then, based on the extraction of each feature information in the multifractal spectrum, BP neural network based on improved conjugate gradient algorithm is selected as the classifier for feature recognition. The results show that the multifractal spectrum can effectively describe the degree of PD gray image inhomogeneity and its different levels of geometric structure, and can accurately identify the PD phenomenon caused by four typical insulation defects.

Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation

Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy.

Cancerous object detection using morphological region-based active contour in ultrasound images

Ultrasound is widely utilized in radiological screening to achieve early cancer investigations. Alongside this utilization, the need of object detection and segmentation techniques highly increases as crucial procedure of computer aided diagnosis tool. In addition, the industrial revolution 4.0 also demands automation and digitization in every technological development including computer aided diagnosis systems. This paper presents a morphological region-based active contour called MoRbAC to automatically detect the cancerous objects. Global level set active contour is initially employed to quick segment all objects in the whole image. Inhomogeneity in ultrasound images will lead to erroneous segmentation result. Hence, several morphology operations are taken to enhance it. The cancerous objects are then specifically detected by comparative calculation of area similarity. The proposed MoRbAC was tested in the real ultrasound images of breast and thyroid which validated by manual ground truth localization. Measurements of overlapping and disagreement regions are applied as quantitative indexes to demonstrate its performance. Average values of each validation has achieved i.e accuracy 98.58±0.89%, sensitivity 89.58±7.69%, specificity 99.58±0.11%, precision 95.58±2.77%, similarity 92.36±4.67%. In conclusion, the high performance of MoRbAC indicates its potential for practical applications in ultrasound computer aided diagnosis.

Hybrid SPF and KD Operator-Based Active Contour Model for Image Segmentation

Image segmentation is a crucial stage of image analysis systems because it detects and extracts regions of interest for further processing, such as image recognition and the image description. However, segmenting images is not always easy because segmentation accuracy depends significantly on image characteristics, such as color, texture, and intensity. Image inhomogeneity profoundly degrades the segmentation performance of segmentation models. This article contributes to image segmentation literature by presenting a hybrid Active Contour Model (ACM) based on a Signed Pressure Force (SPF) function parameterized with a Kernel Difference (KD) operator. An SPF function includes information from both the local and global regions, making the proposed model independent of the initial contour position. The proposed model uses an optimal KD operator parameterized with weight coefficients to capture weak and blurred boundaries of inhomogeneous objects in images. Combined global and local image statistics were computed and added to the proposed energy function to increase the proposed model's sensitivity. The segmentation time complexity of the proposed model was calculated and compared with previous state-of-the-art active contour methods. The results demonstrated the significant superiority of the proposed model over other methods. Furthermore, a quantitative analysis was performed using the mini-MIAS database. Despite the presence of complex inhomogeneity, the proposed model demonstrated the highest segmentation accuracy when compared to other methods.

Grading method of inhomogeneity of contrast-enhanced ultrasound for rectal tumors based on gray level co-occurrence matrix

Transrectal contrast-enhanced ultrasound (CEUS) is an important examination for rectal tumors. The inhomogeneity of the CEUS images has important clinical significance. However, there is no objective method to evaluate this index. In this study, a method based on gray-level co-occurrence matrix (GLCM) is proposed to extract texture features of images and grade these images according the inhomogeneity. Specific processes include compressing the gray level of the image, calculating the texture statistics of gray level co-occurrence matrix, combining feature selection and principal component analysis (PCA) for dimensionality reduction, and training and validating quadratic discriminant analysis (QDA). After ten cross-validation, the overall accuracy rate of machine classification was 87.01%, and the accuracy of each level was as follows: Grade Ⅰ 52.94%, Grade Ⅱ 96.48% and Grade Ⅲ 92.35% respectively. The proposed method has high accuracy in judging grade Ⅱ and Ⅲ images, which can help to identify the grade of inhomogeneity of contrast-enhanced ultrasound images of rectal tumors, and may be used to assist clinical doctors in judging the grade of inhomogeneity of contrast-enhanced ultrasound of rectal tumors.

On the Assessment of Image Inhomogeneity Using T2 Magnetic Resonance Imaging in Head Phantom for Radiotherapy Treatment Planning: Preliminary Study

The use of magnetic resonance imaging (MRI) for radiotherapy treatment planning (RTP) is increasing due to excellent tissue contrast and high-resolution images compared to other modalities. However, the issue of image inhomogeneity which affects tissue delineation and contrast during treatment planning needs to be resolved. This study is aimed to qualitatively observe image inhomogeneity in T2-weighted images and T2 measurement using head phantom. Agar hydrogel phantom of various concentrations were used to form inhomogeneous phantom for mimicking tissue variation in the brain in addition to American College of Radiology accredited phantom. T2 images from both phantoms were obtained using fast spin echo sequence with variation of echo time (TE) and repetition time. Image processing was processed to obtain signal intensity and image mapping and the effect of inhomogeneity was observed qualitatively. T2 values were obtained by fitting signal intensity against TE and T2-mapping images were obtained. T2-weighted and T2-mapping images from the experiment provided qualitative description on image inhomogeneity and contrast. The result showed that T2-mapping images offered more information on phantom properties and described image inhomogeneity and contrast better than T2-weighted images. T2 measurement provided an alternative method for better description of image contrast which could enhance localization and tissue delineation in MRI-based RTP.

Characterization of a multicore fiber image guide for nonlinear endoscopic imaging using two-photon fluorescence and second-harmonic generation.

.Multiphoton microscopy (MPM) has the capacity to record second-harmonic generation (SHG) and endogenous two-photon excitation fluorescence (2PEF) signals emitted from biological tissues. The development of fiber-based miniaturized endomicroscopes delivering pulses in the femtosecond range will allow the transfer of MPM to clinical endoscopy. We present real-time SHG and 2PEF ex vivo images using an endomicroscope, which totally complies with clinical endoscopy regulations. This system is based on the proximal scanning of a commercial multicore image guide (IG). For understanding the inhomogeneities of the recorded images, we quantitatively characterize the IG at the single-core level during nonlinear excitation. The obtained results suggest that these inhomogeneities originate from the variable core geometries that, therefore, exhibit variable nonlinear and dispersive properties. Finally, we propose a method based on modulation of dispersion precompensation to address the image inhomogeneity issue and, as a proof of concept, we demonstrate its capability to improve the nonlinear image quality.

Segmentation of sonar images with intensity inhomogeneity based on improved MRF

Sonar is one of the most important tools for underwater object detection and submarine topography reconstruction. To classify sonar images automatically and accurately is essential for the navigation and path planning of autonomous underwater vehicles (AUV). However, for the intensity inhomogeneity and speckle noise in sonar images, it is difficult to obtain segmentation results of high accurate rate. To address these issues, in this paper, we advocate a segmentation method incorporating simple linear iterative clustering (SLIC) and adaptive intensity constraint into Markov random field (MRF), to segment sonar images with intensity inhomogeneity into the object highlight, the object shadow and the background areas. The main procedures of the proposed work are as follows: first, SLIC is used to separate sonar images into homogeneous super pixels, and second the homogeneity patches, with a novel intensity constraint strategy, is utilized to optimize the segmentation result of MRF at each iteration. Experimental results reveal that the proposed method performs well and fast on real sonar images which have intensity inhomogeneity problem.

BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.

Segmentation of sonar imagery using convolutional neural networks and Markov random field

In this paper, we present a novel method incorporating convolutional neural networks (CNN) into Markov random field (MRF) to automatically segment side scan sonar (SSS) images into object-highlight, object-shadow and sea-bottom reverberation areas. As a widely used ocean survey sensor, SSS provides high-resolution maps of the seafloor. Automatically segmenting SSS in real time can assist the navigation and path-planning of autonomous underwater vehicles. However, for the speckle noise and intensity inhomogeneity in the SSS images, it is difficult to find a robust SSS segmentation method. These facts motivate us to explore efficient CNN architectures to solve these problems. For pixel-level SSS segmentation, to use the context information and the details around a central pixel simultaneously, the CNN with multi-scale inputs (MSCNN) is employed. Besides, to mitigate the impact of the class imbalance problem, two MSCNN training strategies are introduced, which are based on data augmentation and ensemble learning. Furthermore, to take into account the local dependencies of class labels, the results of MSCNN are used to initialize MRF to get the final segmentation maps. Experimental results on real SSS images reveal that the proposed segmentation method outperforms MRF, CNN and semantic segmentation methods such as fully convolutional network and Segnet in segmentation accuracy and generalization performance. Moreover, the efficiency of the proposed method is proved on retinal image dataset.

M‐WRSF model for medical image segmentation

In this Letter, multichannel weighted region-scalable fitting segmentation model (M-WRSF) is proposed for medical image segmentation. The authors have utilised a new edge detection function to improve the performance of image segmentation approaches that already exists on weak edges and the grey-scale inhomogeneity of some medical images. The M-WRSF model introduces a novel punishment item to improve numerical stability and augments time interval to boost iterative efficiency. The Gaussian kernel function is added on the basis of original model to enhance robustness. On the other side, the original medical images need to make de-noising operation before the multichannel active contour model segment them, and also level-set initial curve has no effect on the segmentation results. The benefits of the new M-WRSF model, specifically, the increased efficiency and usability, are verified in the simulation experiment.

Enhanced Homomorphic Unsharp Masking method for intensity inhomogeneity correction in brain MR images

ABSTRACTMRI is a powerful diagnostic tool providing excellent soft tissue contrast and high spatial resolution. However, imperfections arising in the radio frequency field and scanner-related intensity artefacts in MRI produce intensity inhomogeneity. These intensity variations pose major challenges for subsequent image processing and analysis techniques. Therefore, a robust means of correcting intensity inhomogeneity is essential for any accurate and efficient processing technique. Homomorphic filtering based techniques are commonly used for intensity correction in brain MR images, but the halo effect induced by these methods limit their performance in the presence of a tumour. To mitigate the halo effect, a novel Enhanced Homomorphic Unsharp Masking (EHUM) intensity correction algorithm is proposed in this paper. The proposed method first determines the ROI to overcome the halo effect between foreground and background regions. Then, the intensity correction is carried out using homomorphic filtering and linear grey scale mapping. The quantitative and qualitative evaluation results show the efficiency of the proposed method in overcoming the intensity inhomogeneity in both normal and abnormal brain MR images.