Region Growing Image Segmentation Research Articles

Identification of Nodular Lymphocyte-Predominant Hodgkin Lymphoma Variant Morphology Using Artificial Intelligence

Background Lymphocyte predominant (LP) cells located outside B-cell-rich nodules are the hallmark of variant morphologic patterns of nodular lymphocyte predominant Hodgkin lymphoma (NLPHL). In comparison with typical NLPHL (patterns A and B), cases with variant morpholoy (patterns C, D, E and F) are associated with significantly worse clinical behavior. Nevertheless, assessment of tissue architecture may be challenging in limited biopsy specimens, which are increasingly common in routine clinical practice. We sought to address this gap by developing artificial intelligence-assisted modules to accurately recognize NLPHL and distinguish patterns A/B from variant patterns, namely the most common of those, pattern C. Material & Methods The study group included whole slide images (WSI) of hematoxylin and eosin (H&E) stains and CD20 immunohistochemistry stains from 10 NLPHL patients; cases emcompassed patterns A, B & C (A/B: 5 cases; C: 5 cases) as determined by hematopathology review. We designed modules to assess the spatial relationship between LP cells and B cell-rich areas by first seeking to detect LP cells using high-power field (HPF) magnification of H&E WSI (Figure A1). We split the WSI into tiles (8000 x 8000 pixels) to mitigate the computational burden of WSI analysis. For each tile, we deployed the pretrained HoVer-Net for nuclear segmentation. To distinguish LP cells from surrounding small lymphocytes, we analyzed nuclear size, intensity, and gray-level co-courrance matrix (GLCM) features (correlation and dissimilarity). For each feature, we identified the optimal threshold based on feature histogram of LP in comparison to the remainder surrounding cells. Nuclei meeting all feature criteria were labeled as LP cells and validated morphologically by hematopathologists. All tiles were then stitched to illustrate the spatial distribution of LP cells on WSI. Next we deployed the active contour (AC) technique, an iterative region-growing image segmentation algorithm, to identify B-cell areas from CD20 stains (Figure A2). To reliably define the intial contours for the AC, we abided by the following criteria: 1) Red (R) channel intensity larger than both the green (G) and blue (B) channels, with a ratio of 1.25; 2) Including pixels in the bottom 10% of the intensity histogram after removal of slide background and converting the RGB image into gray scale. We merged the contours from these two techniques and used them as initial seeds for AC segmentation, after which we took the final evolved contour as the boundaries of B-cell areas. We then overlayed CD20 on H&E WSI to assess the spatial relationship between the detected LP cells and B-cell areas. Since tissue sections on the CD20 and H&E slides represent neighboring slices, only translation, rotation, and slight scaling were surmised to occurr between slides in this pilot study set. Accordingly, we employed the affine registration paradigm and optimized mutual information between the transformed CD20 and fixed H&E images until convergence then warped the detected B-cell areas via the obtained deformation field (Figure A3). Results The concordant positioning between two stains after registration allowed us to compare the spatial relationship of LP cells to B-cell areas (including assessment of the number of LP cells inside (922) and outside (639) B-cell areas) (Figure A4). We then segmented the final image into quadrants to simulate needle core biopsies and confirmed the efficiency of our modules in detecting LP cells and analyzing their relationship to B-cell areas in limited tissue, in all quadrants (Figure A5). Our model was successfully applied on 100% of 10 NLPHL cases in this pilot group. After proof of concept was established in a full tissue biopsy sample (Figure A1-5), we applied the same steps to an actual physical needle core biopsy of an NLPHL-pattern C case (Figure B1-3) and were able to identify 7 out of 29 LP cells located outside B-cell areas, in congruence with the previously rendered diagnosis (Figure B4). Conclusion This technique, using H&E staining and CD20 immunohistochemistry only, is especially promising to work-up cases when ordering a routine panel of immunohistochemical stains is not feasible due to limited tissue that may be exhausted. Further validation using an independent cohort is warranted in the future. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Salient region growing based on Gaussian pyramid

For region growing image segmentation, seed selection and image noise are two major concerns causing negative segmentation performance. This paper proposes a regional growth algorithm based on the Gaussian pyramid (GPRG), which automatically selects seed points and optimizes the growth path. To achieve automatic seed selection, Gaussian difference pyramid generated by the Gaussian pyramid is required. The growing seed of the salient target in an image is acquired by searching for extremums in each image of the Gaussian difference pyramid. For growing path optimization, edge changing direction is updated according to different Gaussian difference pyramid images with sizes from small to large. The edge of region growing is determined by the edge of the salient target in an image. Segmentation experiments show that this algorithm is able to automatically select seeds and accurately segment the salient target in an image with and without image noise. Performances in terms of segmentation accuracy (SA), mean intersection-over-union (mIoU) and E-measure are superior compared with other methods.

Breast Tumor Detection in MR Images Based on Density

Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90% accuracy, 88% True Negative Fraction, 91% True Positive Fraction, 10% Misclassification Rate, 94% Precision and 86% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.

Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

Adaptive Region Growing Image Segmentation Algorithms for Breast MRI

Early detection and characterization of breast lesion are important for a better and effective treatment of breast cancer. In this paper, four different adaptive region growing image segmentation algorithms are compared. In fact, seed selection was a vital step in the success of region growing methods, so, better schemes for seed selection methods are proposed, namely, joint probabilistic seed selection (JPSS) and Generalised simulated annealing (GSA) based seed selection. The proposed region growing methods namely Fuzzy Region Growing (FRG) and Neutrosophic Region Growing (NRG) are integrated as JPSS-FRG and GSA-NRG frameworks. Another two methods are Scale Invariant Region growing (SiRG) and Fuzzy Neutrosophic Confidence Region growing (FNCRG). The results showed that FNCRG algorithm increases breast cancer detection rate on MRI breast images with the maximum of 93% is achieved. SiRG algorithm improves the true positive rate by 13% compared to existing methods. Further, GSA-NRG makes better segmentation accuracy by 9% and true positive rate by 12%. Also, JPSS-FRG algorithm enhances segmentation accuracy by 24% and improving the true positive rate by 27% compared to Region Growing-Cellular Neural Network (RG-CNN) and Seeded Region Growing-Particle swarm optimization (SRG-PSO) methods respectively.

A Novel Neutrosophic Method for Automatic Seed Point Selection in Thyroid Nodule Images.

The thyroid nodule is one of the endocrine issues caused by an irregular cell development. This rate of survival can be improved by earlier nodule detection. Accordingly, the accurate recognition of the nodule is of the utmost importance in providing powerful results in building the survival rate. The reduction in the accuracy of manual or semiautomatic segmentation methods for thyroid nodule detection is due to many factors, basically, the lack of experience of the sonographer and latency of operation. Most lesion regions in ultrasound images are homogeneous. Therefore, the value of entropy in these regions is high compared to its neighbours. Based on this criterion, a novel procedure for automatically selecting the seed point in thyroid nodule images is proposed. The proposed system consists of three components: neutrosophic image enhancement and speckle reduction to reduce speckle noise and automatic seed selection algorithm extracted from the centre of candidate block in ultrasound thyroid images based on the principle that most of its Higher Order Spectra Entropies (HOSE) from Radon Transform (RT) at different angles are within the range between average and maximum entropies, and the region growing image segmentation is applied with the constant threshold. The performance of proposed automatic segmentation method is compared with other methods in terms of calculating, True Positive (TP) value (96.44 ± 3.01%), False Positive (FP) value (3.55 ± 1.45%), Dice Coefficient (DC) value (92.24 ± 6.47%), Similarity Index (SI) (80.57 ± 1.06%), and Hausdroff Distance (HD) (0.42 ± 0.24 pixels). The proposed system can be considered as an added value to the malignancy diagnosis in thyroid nodule by an endocrinologist.

Adaptive strategy for superpixel-based region-growing image segmentation

This work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained over-segmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we firstly introduce a robust adaptive multi-scale superpixel similarity in which region comparisons are made both at content and common border level. Secondly, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adpative merging criterion to ensure that best region aggregations are given highest priorities. This allows to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach.

A Tool Assessing Optimal Multi-Scale Image Segmentation

Image segmentation to create representative objects by region growing image segmentation techniques such as multi resolution segmentation (MRS) is mostly done through interactive selection of scale parameters and is still a subject of great research interest in object-based image analysis. In this study, we developed an optimum scale parameter selector (OSPS) tool for objective determination of multiple optimal scales in an image by MRS using eCognition software. The ready to use OSPS tool consists of three modules and determines optimum scales in an image by combining intrasegment variance and intersegment spatial autocorrelation. The tool was tested using WorldView-2 and Resourcesat-2 LISS-IV Mx images having different spectral and spatial resolutions in two areas to find optimal objects for ground features such as water bodies, trees, buildings, road, agricultural fields and landslides. Quality of the objects created for these features using scale parameters obtained from the OSPS tool was evaluated quantitatively using segmentation goodness metrics. Results show that OSPS tool is able determine optimum scale parameters for creation of representative objects from high resolution satellite images by MRS method.

SYNCHRONIC CORONAL HOLE MAPPING USING MULTI-INSTRUMENT EUV IMAGES: DATA PREPARATION AND DETECTION METHOD

ABSTRACT We describe a method for the automatic mapping of coronal holes (CHs) using simultaneous multi-instrument EUV imaging data. Synchronized EUV images from STEREO/EUVI A and B 195 Å and SDO/AIA 193 Å are preprocessed, including point-spread function deconvolution and the application of data-derived intensity corrections which account for center-to-limb variations (limb brightening) and inter-instrument intensity normalization. We systematically derive a robust limb-brightening correction that takes advantage of unbiased long-term averages of data and respects the physical nature of the problem. The new preprocessing greatly assists in CH detection, allowing for the use of a simplified variable-connectivity, two-threshold region-growing image segmentation algorithm to obtain consistent detection results. We generate synchronic EUV and CH maps, and show a preliminary analysis of CH evolution. Several data and code products are made available to the community (www.predsci.com/chd): for the period of this study (2010 June 10 to 2014 August 18), we provide synchronic EUV and CH map data at 6 hr cadence, data-derived limb-brightening corrections for STEREO/EUVI A and B 195 Å and SDO/AIA 193 Å, and inter-instrument correction factors to equate their intensities. We also provide the CH image segmentation code module (ezseg) implemented in both FORTRAN-OpenMP and GPU-accelerated C-CUDA. A complete implementation of our CH detection pipeline in the form of a ready-to-use MATLAB driver script euv2chm utilizing ezseg is also made available.

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Attenuation correction with region growing method used in the positron emission mammography imaging system**Supported by Knowledge Innovation Project of The Chinese Academy of Sciences (KJCX2-EW-N06)

The Positron Emission Mammography imaging system (PEMi) provides a novel nuclear diagnosis method dedicated for breast imaging. With a better resolution than whole body PET, PEMi can detect millimeter-sized breast tumors. To address the requirement of semi-quantitative analysis with a radiotracer concentration map of the breast, a new attenuation correction method based on a three-dimensional seeded region growing image segmentation (3DSRG-AC) method has been developed. The method gives a 3D connected region as the segmentation result instead of image slices. The continuity property of the segmentation result makes this new method free of activity variation of breast tissues. The threshold value chosen is the key process for the segmentation method. The first valley in the grey level histogram of the reconstruction image is set as the lower threshold, which works well in clinical application. Results show that attenuation correction for PEMi improves the image quality and the quantitative accuracy of radioactivity distribution determination. Attenuation correction also improves the probability of detecting small and early breast tumors.

Automated mapping of burned areas in semi-arid ecosystems using modis time-series imagery

Abstract. Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Remote sensing has become the most widely used tool to detect fire affected areas over large tracts of land (e.g., ecosystem, regional and global levels). Standard satellite burned area and active fire products derived from the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour l’Observation de la Terre (SPOT) are available to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applications. Consequently, we propose a novel semi-automated algorithm for identification and mapping of burned areas at regional scale. The semi-arid Monte shrublands, a biome covering 240,000km2 in the western part of Argentina, and exposed to seasonal bushfires was selected as the test area. The algorithm uses a set of the normalized burned ratio index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. Additionally, the performance of the adaptive algorithm was assessed against standard global fire products derived from MODIS Aqua and Terra satellites, total burned area (MCD45A1), the active fire algorithm (MOD14); and the L3JRC SPOT VEGETATION 1km GLOBCARBON products. The correlation between the size of burned areas detected by the global fire products and independently-derived Landsat reference data ranged from R2=0.01–0.28, while our algorithm performed showed a stronger correlation coefficient (R2=0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

A Region-Growing Segmentation Algorithm for GPUs

This letter proposes a parallel version for graphics processing units (GPU) of a region-growing image segmentation algorithm widely used by the geographic object-based image analysis (GEOBIA) community. Initially, all image pixels are considered as seeds or primitive segments. Fine-grained parallel threads assigned to individual pixels merge adjacent segments iteratively always ensuring to minimize the overall heterogeneity increase. Besides spectral features the merging criterion considers morphological features that can be efficiently computed in the underlying GPU architecture. Two alternatives using different merging criteria are proposed and tested. An experimental analysis upon five different test images has shown that the parallel algorithm may run up to 19 times faster than its sequential counterpart.

Quantification of double-layer Ni/YSZ fuel cell anodes from focused ion beam tomography data

Three-dimensional microstructure reconstructions of Ni–yttria-stabilized zirconia (Ni/YSZ) anodes are presented, all of which are based on focused ion beam tomography data.The reconstruction procedure, starting from a series of 2D scanning electron micrographs, is investigated step by step and potential sources of error are identified. The distinction between Ni phase, YSZ phase and pore phase is solved by an advanced algorithm, which belongs to the region-growing image segmentation methods. This improves the accuracy of automated grayscale segmentation especially for images with low contrast, which is characteristic of both solid phases in Ni/YSZ anodes.Critical microstructure parameters like material fractions, surface areas, particle size and distribution of Ni, YSZ, and pore phase, as well as phase connectivity and triple-phase boundary density, are evaluated and discussed.In this contribution, two types of high-performance Ni/YSZ anodes – differing in thickness of both the anode functional layer and the anode substrate – are reconstructed and compared to each other. For the first time, the anode functional layer adjacent to the thin film electrolyte is separately quantified. The presented methods are qualified to quantitatively compare different anode microstructures and relate the result to their electrochemical performance.

Detecting, Grouping, and Structure Inference for Invariant Repetitive Patterns in Images

The efficient and robust extraction of invariant patterns from an image is a long-standing problem in computer vision. Invariant structures are often related to repetitive or near-repetitive patterns. The perception of repetitive patterns in an image is strongly linked to the visual interpretation and composition of textures. Repetitive patterns are products of both repetitive structures as well as repetitive reflections or color patterns. In other words, patterns that exhibit near-stationary behavior provide rich information about objects, their shapes, and their texture in an image. In this paper, we propose a new algorithm for repetitive pattern detection and grouping. The algorithm follows the classical region growing image segmentation scheme. It utilizes a mean-shift-like dynamic to group local image patches into clusters. It exploits a continuous joint alignment to: 1) match similar patches, and 2) refine the subspace grouping. We also propose an algorithm for inferring the composition structure of the repetitive patterns. The inference algorithm constructs a data-driven structural completion field, which merges the detected repetitive patterns into specific global geometric structures. The result of higher level grouping for image patterns can be used to infer the geometry of objects and estimate the general layout of a crowded scene.

A stochastic gravitational approach to feature based color image segmentation

In this paper, a novel image segmentation algorithm based on the theory of gravity is presented, which is called as “stochastic feature based gravitational image segmentation algorithm (SGISA)”. The proposed SGISA uses color, texture, and spatial information to partition the image into homogenous and semi-compact segments. The proposed method benefits from the advantages of both clustering and region growing image segmentation techniques. The SGISA is equipped with a new operator called “escape” that is inspired by the concept of escape velocity in physics. Moreover, motivated by heuristic search algorithms, we incorporate a stochastic characteristic with the SGISA, which gives algorithm the ability to search the image for finding the fittest regions (pixels) that are suitable for merging. Several experiments on various standard images as well as Berkley standard image database are reported. Results are compared with a well-known clustering based segmentation method, C-means, a gravitational based clustering method (SGC), and the well-known mean-shift method. The results are reported using unsupervised criteria and pre-ground-truthed measures. The obtained results confirm the effectiveness of the proposed method in color image segmentation.

A Hybrid Region Growing Algorithm for Medical Image Segmentation

In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.

Line-Based Region Growing Image Segmentation for Mobile Device Applications

Line-Based Region Growing Image Segmentation for Mobile Device Applications

Optimal region growing segmentation and its effect on classification accuracy

Image segmentation is a preliminary and critical step in object-based image classification. Its proper evaluation ensures that the best segmentation is used in image classification. In this article, image segmentations with nine different parameter settings were carried out with a multi-spectral Landsat imagery and the segmentation results were evaluated with an objective function that aims at maximizing homogeneity within segments and separability between neighbouring segments. The segmented images were classified into eight land-cover classes and the classifications were evaluated with independent ground data comprising 600 randomly distributed points. The accuracy assessment results presented similar distribution as that of the objective function values, that is segmentations with the highest objective function values also resulted in the highest classification accuracies. This result shows that image segmentation has a direct effect on the classification accuracy; the objective function not only worked on a single band image as proved by (Espindola, G.M., Camara, G., Reis, I.A., Bins, L.S. and Monteiro, A.M., 2006, Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27, pp. 3035–3040.) but also on multi-spectral imagery as tested in this, and is indeed an effective way to determine the optimal segmentation parameters. McNemar's test (z 2 = 10.27) shows that with the optimal segmentation, object-based classification achieved accuracy significantly higher than that of the pixel-based classification, with 99% significance level.