Image Segmentation System Research Articles

Compound image segmentation of published biomedical figures.

Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. shatkay@udel.edu. Supplementary data are available online at Bioinformatics.

An embedded system for image segmentation and multimodal registration in noninvasive skin cancer screening.

We present a heterogeneous architecture for image registration and multimodal segmentation on an embedded system for noninvasive skin cancer screening. The architecture combines Otsu thresholding and the random walker algorithm to perform image segmentation, and features a hardware implementation of the Harris corner detection algorithm to perform region-of-interest detection and image registration. Running on a Xilinx XC7Z020 reconfigurable system-on-a-chip, our prototype computes the initial segmentation of a 400×400-pixel region of interest in the visible spectrum in 12.1 seconds, and registers infrared images against this region at 540 frames per second, while consuming 1.9W.

Cogent Confabulation based Expert System for Segmentation and Classification of Natural Landscape Images

Ever since there has been an increase in the number of automatic wildfire monitoring and surveillance systems in the last few years, natural landscape images have been of great importa ...

SegMo: Volumetric Image Segmentation System for X-rays CT Scanning of Mechanical Assemblies

Industrial X-ray CT scanning systems (XCT) have been used for obtaining 3D shapes of mechanical parts. In this study we are interested in scanning a whole assembly made of several parts made of different materials. It has several advantages such as reducing scanning labor compared to that needed for scanning each of the parts by skipping disassembly process and also by reducing scanning operations. However, such whole assembly scanning is not a common practice as it is difficult to extract the parts from the volumetric image of the whole assembly. This process is generally called segmentation. Currently, an operator needs to spend long time on manually segmenting the volumetric image. This paper proposes a new segmentation program SegMo. It is based on a cell decomposition of the volumetric image with Morse complex defined with a function of the norm of gradient of the CT values of the image. The Morse complex has cell boundaries including parts’ boundaries. By constructing LOD (level of details) hierarchy of the Morse complex, a user can select an appropriate level of the hierarchy where the boundary of the target part is well reproduced and thus can form a region of a part. A system based on the SegMo with GUI (graphical user interface) is developed on ZVoxer system of Zodiac corporation. It has been applied to various examples to show that the SegMo system can perform segmentation more quickly and with less effort than conventional methods. In this paper, we describe the algorithm of the SegMo and its experimental results.

Vehicle License Plate Image Segmentation System Using Cellular Neural Network Optimized by Adaptive Fuzzy and Neuro-Fuzzy Algorithms

Vehicle License Plate Image Segmentation System Using Cellular Neural Network Optimized by Adaptive Fuzzy and Neuro-Fuzzy Algorithms

Automatic and quantitative measurement of laryngeal video stroboscopic images.

The laryngeal video stroboscope is an important instrument for physicians to analyze abnormalities and diseases in the glottal area. Stroboscope has been widely used around the world. However, without quantized indices, physicians can only make subjective judgment on glottal images. We designed a new laser projection marking module and applied it onto the laryngeal video stroboscope to provide scale conversion reference parameters for glottal imaging and to convert the physiological parameters of glottis. Image processing technology was used to segment the important image regions of interest. Information of the glottis was quantified, and the vocal fold image segmentation system was completed to assist clinical diagnosis and increase accuracy. Regarding image processing, histogram equalization was used to enhance glottis image contrast. The center weighted median filters image noise while retaining the texture of the glottal image. Statistical threshold determination was used for automatic segmentation of a glottal image. As the glottis image contains saliva and light spots, which are classified as the noise of the image, noise was eliminated by erosion, expansion, disconnection, and closure techniques to highlight the vocal area. We also used image processing to automatically identify an image of vocal fold region in order to quantify information from the glottal image, such as glottal area, vocal fold perimeter, vocal fold length, glottal width, and vocal fold angle. The quantized glottis image database was created to assist physicians in diagnosing glottis diseases more objectively.

Two Segment Approach to Find Tumor Affected Area from Brain MRI

Two region graph cut image partitioning after mapping entire image of brain in brain MRI. It is seen that piecewise constant model of the graph cut formulation becomes applicable when the image data is transformed by a main function. The proposed function contains data term to evaluate the deviation of the transformed data within two segmentation region, from the piecewise constant model, and a smoothness boundary preserving regularization term. Using a common function, energy minimization typically consists of iterating image partitioning by graph cut iterations and evaluations of region parameters via fixed point computation. The method results in good segmentations and runs faster the graph cut methods. The segmentation of MRI image is challenging due to the high diversity in appearance of tissue among the patient. An automatic interactive brain MRI image segmentation system with the ability to adjust operator control is achieved through this method.

Hyperconic Multilayer Perceptron

This paper introduces the design of the hyperconic multilayer perceptron (HC-MLP). Complex non-linear decision regions for classification purposes are generated by quadratic hyper-surfaces spawned by the hyperconic neurons in the hidden layer (for instance, spheres, ellipsoids, paraboloids, hyperboloids and degenerate conics). In order to generate quadratic hyper-surfaces, the hyperconic neurons’ transfer function includes the estimation of a quadratic polynomial. The proper assignment of decision regions to classes is achieved in the output layer by using spheres to determine whether a point is inside or outside the spherical region. The particle swarm optimization algorithm is used for training the HC-MLP. The learning of the HC-MLP selects the best conic surface that separates the data set vectors. For illustration purposes, two experiments are conducted using two distributions of synthetic data in order to show the advantages of HC-MLP when the patterns between classes are contiguous. Furthermore a comparison to the traditional multilayer perceptron is carried out to evaluate the complexity (in terms of the number of estimated patterns) and classification accuracy. HC-MLP is the principal component to implement a diagnosis system to detect faults in an induction motor and to implement an image segmentation system. The performance of HC-MLP is compared to other leading algorithms by using 4 databases commonly used in related literature.

An Automated System for Atlas Based Multiple Organ Segmentation of Abdominal CT Images

In this paper, an efficient atlas based approach for multiple abdominal organ segmentation is presented. This automatic segmentation of different organs such as spine, kidneys, liver, aorta, spleen of abdominal image is based on allocation of spine as landmark. In current years several researches has been done for developing automatic segmentation techniques of abdominal CT images however still it is an incredibly challenging task to segment this efficiently and appropriately. This paper proposed a fully automatic system for abdominal image segmentation by marking spine as landmark to extract different organs using a fuzzy based system. The proposed technique uses the fact that multiple organs of abdominal images are situated at a particular distance and in particular range of angles from the spine and spine is the solitary organ which is frequent in the slices of CT image data set. In this paper we focused for the segmentation of liver, kidney, aorta, spine, spleen. This system is evaluated on the data of several patients (152 CT images which consist all such organs) and obtained significant results by comparing the computed results to the boundaries manually traced by experts.

The detections of retinopathy symptoms and tractional retinal detachment

Since the symptoms of the diabetic retinopathy, macular degeneration, and many other eye diseases are often associated with the bright spots, dark spots, and vascular abnormalities in retinal images, this study proposes a retinopathy image segmentation (system) to extract the bright spots, dark spots, and blood vessels from retinal images. Moreover, since a high proportion of diabetic patients suffer from the diabetic retinopathy with the most severe one to be the retinal detachment, the retinal image-based tractional retinal detachment diagnosis system is proposed, as well. Two genetic-based parameter detectors are given to provide the most appropriate values for the parameters used by the retinopathy image segmentation system and the tractional retinal detachment diagnosis system. The experimental results illustrate that the retinopathy image segmentation system and the tractional retinal detachment diagnosis system can provide expressive results in segmenting bright spots, dark spots, blood vessels, and in diagnosing tractional retinal detachment. Both systems are helpful for the ophthalmologists in diagnosing eye diseases.

Implementation of Biography Based Neural Clustering (BBNC) with Genetic Processing for tumor detection from medical images

Segmentation is a best method to divide the required region from the medical images. This research is based on segmentation of medical images (MRI, CT scans) based on the previous method known as pre-operative and post-recurrence tumor registration (PORTR) and proposed method biography based neural clustering (BBNC) with genetic processing for tumor segmentation. By using the new technique the extracted part can be view in 3D model and also can get the actual segmented tumor region. This new method will be helpful for diagnostics to find the tumor area as well as pixel difference in segmented part to define the tumor area accurately. While in the previous approach all the parameters have been used likewise, in which the registration method is used to transform the different sets of data into one coordinate system for segmentation of medical images. Registration basically is used to improve the signals to reduce the noise from the images. These techniques are better to find the tumor area from the MRI and CT scans, but after comparing them better results have been obtained in proposed technique. The proposed technique (BBNC) reduces the extracted region again into required and actual region of tumor with accuracy of area, time and pixel difference.

Implementation of Biography Based Neural Clustering (BBNC) with Genetic Processing for Tumor Detection from Medical Images

Implementation of Biography Based Neural Clustering (BBNC) with Genetic Processing for Tumor Detection from Medical Images This research paper presenting the segmentation of medical images (MRI, CT scans) based on the previous method pre operative and post recurrence brain tumor registration (PORTR) and proposed method biography based neural clustering (BBNC) with genetic processing for tumor segmentation. By using the new technique the extracted part can be view in 3D model and also can get the actual segmented tumor region. This new method will be helpful for diagnostics to find the tumor area as well as pixel difference in segmented part to define the tumor area accurately. While in the previous approach all the parameters have been used likewise, in which the registration method is used to transform the different sets of data into one coordinate system for segmentation of medical images. Registration basically is used to improve the signals to reduce the noise from the images. These techniques are better to find the tumor area from the MRI and CT scans, but after comparing them better results have been obtained in proposed technique. The proposed technique (BBNC) reduces the extracted region again into required and actual region of tumor with accuracy of area, time and pixel difference.

MAS based on a Fast and Robust FCM Algorithm for MR Brain Image Segmentation

In the aim of providing sophisticated applications and getting benefits from the advantageous properties of agents, designing agent-based and multi-agent systems has become an important issue that received further consideration from many application domains. Towards the same goal, this work gathered three different research fields; image segmentation, fuzzy clustering and multi-agent systems (MAS); and furnished a MAS for MR brain image segmentation that is based on a fast and robust FCM (FRFCM) algorithm. The proposed MAS was tested, as well as the sequential version of the FRFCM algorithm and the standard FCM, on simulated and real normal brains. The experimental results were valuable in both segmentation accuracy and running times point of views.

A Computer Aided Diagnosis (CAD) System for Segmentation and Analysis of Brain Magnetic Resonance Images

A Computer Aided Diagnosis (CAD) System for Segmentation and Analysis of Brain Magnetic Resonance Images

Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering

Image segmentation is a vital step in many imaging applications, such as medical images and computer vision. Image segmentation is considered as a challenging problem, so we need to develop an efficient, fast technique for medical image segmentation. In this paper, we propose a new system for a multi-resolution MRI brain image segmentation, which is based on a morphological pyramid with fuzzy C-mean (FCM) clustering. In the first stage, we use a wavelet multi-resolution to maintain spatial context between pixels. Secondly, we use the morphological pyramid to fuse the resulting multi-resolution images with the original image to increase sharpness and decrease noise in the processed image. Finally, we use FCM technique to segment the processed images. We compared our proposed system with some state of the art segmentation techniques on two different brain data sets. Experimental results showed that the proposed system improves the accuracy of the MRI brain image segmentation.

A segmentation system based on clustering method for pediatric DTI images

ABSTRACTA system is presented for the segmentation of white matter fiber tracts in pediatric diffusion tensor magnetic resonance imaging (DTI) images. DTI is an in vivo method to delineate the connectivity of white matter fiber tracts in human brain by fiber tractography. Fiber tractography is a promising method to visualize the whole bundles of fiber tracts. Fiber tractography is unable to provide a quantitative analysis and description of specific white matter fiber tracts. Obviously, segmenting and clustering the fiber tracts into anatomical bundles play an important role in fiber tracts analysis. Traditional manual segmentation method requires neuroanatomical expertise and significant time. It can not be a standardized and widely used method for segmentation of complicated fiber tracts in pediatric DTI images. Hence, an image segmentation system with an adaptive mean shift (AMS) clustering method is proposed to cluster fiber tracts into bundles automatically in this article. In the image segmentation system, fiber similarity measure based on Euclidean distance is used in the clustering method. Since the increase of children's mental illness in recent years, segmentation of pediatric DTI images by clustering methods is focused in our research. The effectiveness and robustness of adaptive mean shift clustering algorithm for segmentation of fiber tracts are also evaluated by error analysis experiments. In addition, the experiment results show that adaptive mean shift method used in our system is more efficient and effective than K‐means and Fuzzy C‐means (FCM) clustering methods for the segmentation of fiber tracts in real pediatric DTI images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 102–113, 2015

A robust multilevel segment description for multi-class object recognition

We present an attempt to improve the performance of multi-class image segmentation systems based on a multilevel description of segments. The multi-class image segmentation system used in this paper marks the segments in an image, describes the segments via multilevel feature vectors and passes the vectors to a multi-class object classifier. The focus of this paper is on the segment description section. We first propose a robust, scale-invariant texture feature set, named directional differences (DDs). This feature is designed by investigating the flaws of conventional texture features. The advantages of DDs are justified both analytically and experimentally. We have conducted several experiments on the performance of our multi-class image segmentation system to compare DDs with some well-known texture features. Experimental results show that DDs present about 8 % higher classification accuracy. Feature reduction experiments also show that in a combined feature space, DDs remain in the list of most effective features even for small feature vector sizes. To describe a segment fully, we introduce a multilevel strategy called different levels of feature extraction (DLFE) that enables the system to include the semantic relations and contextual information in the features. This information is very effective especially for highly occluded objects. DLFE concatenates the features related to different views of every segment. Experimental results that show more than 4 % improvement in multi-class image segmentation accuracy is achieved. Using the semantic information in the classifier section adds another 2 % improvement to the accuracy of the system.

Automated segmentation of concrete images into microstructures: A comparative study

Concrete is an important material in most of civil constructions. Many properties of concrete can be determined through analysis of concrete images. Image segmentation is the first step for the most of these analyses. An automated system for segmentation of concrete images into microstructures using texture analysis is proposed. The performance of five different classifiers has been evaluated and the results show that using an Artificial Neural Network classifier is the best choice for an automatic image segmentation of concrete.

Towards the taxonomic categorization and recognition of nanoparticle shapes

The shape of nanoparticles and nanomaterials is a fundamental characteristic that has been shown to influence a number of their properties and effects, particularly for nanomedical applications. The information related with this feature of nanoparticles and nanomaterials is, therefore, crucial to exploit and foster in existing and future research in this area. We have found that descriptions of morphological and spatial properties are consistently reported in the nanotechnology literature, and in general, these morphological properties can be observed and measured using various microscopy techniques. In this paper, we outline a taxonomy of nanoparticle shapes constructed according to nanotechnologists’ descriptions and formal geometric concepts that can be used to address the problem of nanomaterial categorization. We employ an image segmentation technique, belonging to the mathematical morphology field, which is capable of identifying shapes in images that can be used to (semi-) automatically annotate nanoparticle images. From the Clinical EditorThis team of authors outlines a taxonomy of nanoparticle shapes constructed according to nanotechnologists’ descriptions and formal geometric concepts enabling nanomaterial categorization. They also employ a mathematical morphology-based image segmentation system, capable of identifying shapes and can be utilized in semi-automated annotation of nanoparticle images.

Sonar Image Segmentation Using the Level Set Method without Re-Initialization

To solve the problem that many image segmentation methods cannot be applied to forward looking sonar image accurately, an improved level set segmentation method was proposed in this paper. Firstly, the level set evolution without re-initialization was introduced. Secondly the different characteristics of forward looking sonar image from the optical image were analyzed, and we got the factors affecting segmentation. Then, to overcome these negative effects, this paper did preprocessing by morphological top-hat and bottom-hat transformation, and carried on level set method without re-initialization to construct an improved level set sonar image segmentation system. Finally, our method was compared with the traditional level set method in computer experiments. Simulation results show that it is more adapted to forward looking sonar image segmentation.