Developed Segmentation Method Research Articles

Structural Performance and Nesting Efficiency of segmented, point-supported, slabs for co-designed timber architecture

Climate change requires the economical use of all raw materials, including renewable construction materials such as timber. The aim of this study is to understand the interdependencies between structural and nesting performance to define the segmentation method that uses the least raw material. To do this, six segmentation methods are compared for 16 floor plans. Based on these simulations the amount of raw material needed per project is estimated for two timber building systems: one solid and the other hollow. Two of the developed segmentation methods are competitive with the commonly used regular segmentation method in terms of their raw material use. The overall performance however is dependent on the column layout, the boundary shape of the floor plan, and the chosen building system.

Large-scale 3D object segmentation employing conditional angular clustering technique

This paper aims to develop an automatic 3D object segmentation method for the large-scale point clouds. Given a range image, the preprocessing is first applied to get the optimal 3D point cloud. A [Formula: see text]-nearest neighbor is built, and a segmentation algorithm based on the conditional angular clustering technique is used to segment the objects from the point cloud. The algorithm is tested on the real point cloud datasets. The experiment results demonstrated that the developed segmentation method can be used to localize the object with the relative uncertainty of 0.27%.

Texture segmentation method for computer-assisted dermatologic diagnostic system

The wide spread of dermatological diseases is an important medical and social problem. Doctors note the constant growth of psoriasis among people of all ages. The psoriasis disease symptoms are similar to the symptoms of such diseases as eczema, atopic dermatitis and medicinal disease. Therefore, there is a high probability of an error in the disease diagnosis, which prevents the full treatment and prevention of the disease. Dermatological diagnostic systems are decision-making support systems for dermatologists when establishing a diagnosis and assessing the severity of the disease course. The development of new image processing methods for dermatological diagnostic systems is an important task, which allow to increase the accuracy of the diagnostic decision. In this work, the segmentation method of psoriasis images for systems of medical dermatological diagnostics based on a vector-difference approach to improve the quality of segmentation was developed. The vector-difference approach allows to calculate a certain texture feature of the image as a vector transformation of various texture features by linear algebra methods. Psoriasis disease images can be described by texture (spectral, statistical, spectral-statistical) and color, so it is proposed to take into account textural and color characteristics of images for image segmentation. The color models that are most often used in segmentation methods of psoriasis disease images were analyzed. Based on the analysis, the Hue-Saturation-Intensity color model was chosen.It is proposed to use spectral, statistical and spectral-statistical texture models and color characteristics of image pixels to represent psoriasis disease images. The developed segmentation method includes the following stages: image pre-processing; identification; vector-difference transformation; threshold processing. At the pre-processing stage, homomorphic filtering was applied to psoriasis disease images. The result of the identification stage is a set of features calculated by the textural and color characteristics for image objects. The vector-difference transformation converts the texture image into intensity. Threshold processing is performed with a global threshold. Experimental research of the proposed segmentation method of psoriasis disease images was performed. As a result of the experimental research, it was found that the probability of correct identification of psoriasis disease area on average is 0.97, the probability of a false alarm is about 0.05.

Hyperia: A novel methodology of developing anthropomorphic breast phantoms for X-ray imaging modalities - Part I: Concept and initial findings.

To introduce a novel methodology for developing anthropomorphic breast phantoms for use in X-ray-based imaging modalities. "Hyperization" is a quasi-stippling mapping operation in which regions of varying grayscale values in a 2D image are transformed into regions of varying holes on a surface. The holes can be cut or engraved on the sheet of paper using a high-resolution laser cutter/engraver. In hyperization, the main parameters are the size and the distance between the holes. Here, we introduce the concept and chronicle the development and characterization of a proof-of-concept prototype. In this study, we hypothesized that a resulting "Hyperia" phantom would be a realistic representative of a patient's breast tissue: it would exhibit similar X-ray properties and show textural complexities. We used breast computed tomography (bCT) images of real patients as the input models. Using a previously developed segmentation method, the input CT images were segmented into different tissue classes (skin, adipose, and fibroglandular). The segmented images were then "Hyperized". A series of Monte Carlo simulations were conducted to find the optimal hyperization parameters. Different laser cutter/engraver systems and substrate materials were explored to find a viable option for developing an entire Hyperia breast phantom. The resulting phantom was imaged on a prototype breast CT system, and the resulting images were evaluated based on physical properties and similarity to the original patient data. The simulation results indicate close similarities - both in the distribution of different tissue types and the resulting CT numbers - between the patient bCT image and the bCT of the Hyperia phantom, regardless of the breast size and density: the Pearson correlation coefficient (ρ) ranged from 0.88 in a BIRADS A breast to 0.94 in BIRADS C and D breasts (ρ of 1.00 suggests perfect structural similarity), and the volumetric mean squared error ranged from 0.0033 (in BIRADS D breast) to 0.0059 (in BIRADS A), suggesting good agreement between the resulting CT numbers. For fabricating the slices, the office paper was found to be an optimal substrate material, with the Hyperization parameters of (α, β) = (0.200mm, 0.400mm). A novel phantom can be used for X-ray-based breast cancer imaging systems. The main advantage is that only one material is used for creating a contrast between different tissue types in an image.

Multi-level thresholding segmentation for pathological images: Optimal performance design of a new modified differential evolution

The effective analytical processing of pathological images is crucial in promoting the development of medical diagnostics. Based on this matter, in this research, a multi-level thresholding segmentation (MLTS) method based on modified different evolution (MDE) is proposed. The MDE is the primary benefit offered by the suggested MLTS technique, which is a novel proposed evolutionary algorithm in this article with significant convergence accuracy and the capability to leap out of the local optimum (LO). This optimizer came into being mostly as a result of the incorporation of the movement mechanisms of white holes, black holes, and wormholes into various evolutions. Thus, the developed MLTS approach may provide high-quality segmentation results and is less susceptible to segmentation process stagnation. To validate the efficacy of the presented approaches, first, the performance of MDE is validated using 30 benchmark functions, and then the proposed segmentation method is empirically compared with other comparable methods using standard pictures. On the basis of breast cancer and skin cancer pathology images, the developed segmentation method is compared to other competing methods and experimentally validated in further detail. By analyzing experimental data, the key compensations of MDE are proven, and it is experimentally shown that the unique MDE-based MLTS approach can achieve good performance in terms of many performance assessment indices. Consequently, the proposed method may offer an efficient segmentation procedure for pathological medical images.

Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.

Purpose:To fully exploit the benefits of magnetic resonance imaging (MRI) for radiotherapy, it is desirable to develop segmentation methods to delineate patients' MRI images fast and accurately. The purpose of this work is to develop a semi-automatic method to segment organs and tumor within the brain on standard T1- and T2-weighted MRI images.Methods and Materials:Twelve brain cancer patients were retrospectively included in this study, and a simple rigid registration was used to align all the images to the same spatial coordinates. Regions of interest were created for organs and tumor segmentations. The K-nearest neighbor (KNN) classification algorithm was used to characterize the knowledge of previous segmentations using 15 image features (T1 and T2 image intensity, 4 Gabor filtered images, 6 image gradients, and 3 Cartesian coordinates), and the trained models were used to predict organ and tumor contours. Dice similarity coefficient (DSC), normalized surface dice, sensitivity, specificity, and Hausdorff distance were used to evaluate the performance of segmentations.Results:Our semi-automatic segmentations matched with the ground truths closely. The mean DSC value was between 0.49 (optical chiasm) and 0.89 (right eye) for organ segmentations and was 0.87 for tumor segmentation. Overall performance of our method is comparable or superior to the previous work, and the accuracy of our semi-automatic segmentation is generally better for large volume objects.Conclusion:The proposed KNN method can accurately segment organs and tumor using standard brain MRI images, provides fast and accurate image processing and planning tools, and paves the way for clinical implementation of MRI-guided radiotherapy and adaptive radiotherapy.

Breast cancer segmentation based on modified Gaussian mean shift algorithm for infrared thermal images

ABSTRACT Accurate segmentation of infrared thermal images is a challenging issue in the breast cancer detection. This paper presents and evaluates novel segmentation method for breast cancer detection using infrared thermal images. The developed segmentation method uses developed segmentation algorithm that is hybrid of Gaussian Mean Shift (GMS) and roulette wheel selection approach. In the first stage of the developed segmentation method, the redundant portions of the infrared thermal image are removed and then, the infrared thermal image is divided into five and six clusters by applying the developed segmentation algorithm. The segmented infrared thermal image is created by multiplying the selected best image of each cluster. This image is used to diagnosis the normal breast from abnormal breast. The proposed method is developed using MATLAB 2017 and infrared thermal images of 64 patients. The results show that the average Dice similarity coefficient, Jaccard index and Hausdorff distance in the proposed segmentation method are 91.81%, 84.86%, and 4.87, respectively. The comparison results demonstrate that using the proposed segmentation method can improve the performance of the Computer-Aided-Detection (CAD) system compared to the CAD systems that use the Mean Shift (MS) and Fuzzy C-Means (FCM) segmentation algorithms.

Color object segmentation and tracking using flexible statistical model and level-set

This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with a post processing step implemented with a variational active contour. Flexible learning method of a finite mixture of bounded generalized Gaussian distributions using the Minimum Message Length (MML) principle is developed to cope with the complexity of color images modeling. We deal here simultaneously with the issues of data-model fitting, determining automatically the optimal number of classes and selecting relevant features. Indeed, a feature selection step based on MML is implemented to eliminate uninformative features and therefore improving the algorithm’s performance. For model’s parameters estimation, the maximum likelihood (ML) was investigated and conducted via expectation maximization (EM) algorithm. The obtained object boundaries in the first step are tracked on each frame of a given sequence using a geometric level-set approach. The implementation has the advantage to help in improving the computational efficiency in high-dimensional spaces. We demonstrate the effectiveness of the developed segmentation method through several experiments. Obtained results reveal that our approach is able to achieve higher precision as compared to several other methods for color image segmentation and object tracking.

Deep learning-based liver segmentation for fusion-guided intervention.

Tumors often have different imaging properties, and there is no single imaging modality that can visualize all tumors. In CT-guided needle placement procedures, image fusion (e.g. with MRI, PET, or contrast CT) is often used as image guidance when the tumor is not directly visible in CT. In order to achieve image fusion, interventional CT image needs to be registered to an imaging modality, in which the tumor is visible. However, multi-modality image registration is a very challenging problem. In this work, we develop a deep learning-based liver segmentation algorithm and use the segmented surfaces to assist image fusion with the applications in guided needle placement procedures for diagnosing and treating liver tumors. The developed segmentation method integrates multi-scale input and multi-scale output features in one single network for context information abstraction. The automatic segmentation results are used to register an interventional CT with a diagnostic image. The registration helps visualize the target and guide the interventional operation. The segmentation results demonstrated that the developed segmentation method is highly accurate with Dice of 96.1% on 70 CT scans provided by LiTS challenge. The segmentation algorithm is then applied to a set of images acquired for liver tumor intervention for surface-based image fusion. The effectiveness of the proposed methods is demonstrated through a number of clinical cases. Our study shows that deep learning-based image segmentation can obtain useful results to help image fusion for interventional guidance. Such a technique may lead to a number of other potential applications.

Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features.

Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.

Morphological and Otsu’s Thresholding-Based Retinal Blood Vessel Segmentation for Detection of Retinopathy

Retinal blood vessel segmentation is crucial as it is the earliest process in measuring various indicators of retinopathy sign such as arterial-venous nicking, and focal arteriolar and generalized arteriolar narrowing. The segmentation can be clinically used if its accuracy is close to 100%. In this study, a new method of segmentation is developed for extraction of retinal blood vessel. In this paper, we present a new automated method to extract blood vessels in retinal fundus images. The proposed method comprises of two main parts and a few subcomponents which include pre-processing and segmentation. The main focus for the segmentation part is two morphological reconstructions which are the morphological reconstructions followed by the morphological top-hat transform. Then the technique to classify the vessel pixels and background pixels is Otsu’s Thresholding. The image database used in this study is the High Resolution Fundus Image Database (HRFID). The developed segmentation method accuracies are 95.17%, 92.06% and 94.71% when tested on dataset of healthy, diabetic retinopathy (DR) and glaucoma patients respectively. Overall, the performance of the proposed method is comparable with existing methods with overall accuracies were more than 90 % for all three different categories: healthy, DR and glaucoma.

Influence of evaporation rate on cracks’ formation of a drying drop of whole blood

The influence of the drying rate on pattern formation of a dried drop of whole blood is investigated. A range of relative humidity (RH) between 12.0% and 66.5% is studied. Drops of whole blood of same volume are gently deposited on microscope glass substrates. A top-view camera operates at a typical frame rate of 10 frames per second, which is sufficient to monitor the drying regime of the drop (deposition, gelation, and fracturation). Also, the evolution of the morphological structure of patterns made up of cracks and their arrangement in the corona region is studied. For this purpose, a manual segmentation is used as a reference method for the validation of our automatic developed segmentation method. This automatic segmentation method enables automatic extraction and morphological characterization of patterns. We show that the evaporation rate influences structural distribution of plaques, and consequently, the final plaques diameter at the end of the evaporation process. At low drying rate (high RH), plaques diameter show good agreement between automatically and manually segmented plaques with a slight difference. Also, automatic segmentation overestimates angular deviation. Our experimental results also show that the length of the corona region remains constant whatever is the RH levels.

Automated segmentation of atherosclerotic histology based on pattern classification

Background: Histology sections provide accurate information on atherosclerotic plaque composition, and are used in various applications. To our knowledge, no automated systems for plaque component segmentation in histology sections currently exist. Materials and Methods: We perform pixel-wise classification of fibrous, lipid, and necrotic tissue in Elastica Von Gieson-stained histology sections, using features based on color channel intensity and local image texture and structure. We compare an approach where we train on independent data to an approach where we train on one or two sections per specimen in order to segment the remaining sections. We evaluate the results on segmentation accuracy in histology, and we use the obtained histology segmentations to train plaque component classification methods in ex vivo Magnetic resonance imaging (MRI) and in vivo MRI and computed tomography (CT). Results: In leave-one-specimen-out experiments on 176 histology slices of 13 plaques, a pixel-wise accuracy of 75.7 ± 6.8% was obtained. This increased to 77.6 ± 6.5% when two manually annotated slices of the specimen to be segmented were used for training. Rank correlations of relative component volumes with manually annotated volumes were high in this situation (P = 0.82-0.98). Using the obtained histology segmentations to train plaque component classification methods in ex vivo MRI and in vivo MRI and CT resulted in similar image segmentations for training on the automated histology segmentations as for training on a fully manual ground truth. The size of the lipid-rich necrotic core was significantly smaller when training on fully automated histology segmentations than when manually annotated histology sections were used. This difference was reduced and not statistically significant when one or two slices per section were manually annotated for histology segmentation. Conclusions: Good histology segmentations can be obtained by automated segmentation, which show good correlations with ground truth volumes. In addition, these can be used to develop segmentation methods in other imaging modalities. Accuracy increases when one or two sections of the same specimen are used for training, which requires a limited amount of user interaction in practice.

3D human posture segmentation by spectral clustering with surface normal constraint

In this paper, we propose a new algorithm for partitioning human posture represented by 3D point clouds sampled from the surface of human body. The algorithm is formed as a constrained extension of the recently developed segmentation method, spectral clustering (SC). Two folds of merits are offered by the algorithm: (1) as a nonlinear method, it is able to deal with the situation that data (point cloud) are sampled from a manifold (the surface of human body) rather than the embedded entire 3D space; (2) by using constraints, it facilitates the integration of multiple similarities for human posture partitioning, and it also helps to reduce the limitations of spectral clustering. We show that the constrained spectral clustering (CSC) still can be solved by generalized eigen-decomposition. Experimental results confirm the effectiveness of the proposed algorithm.

One-Class Support Vector Machines for Aerial Images Segmentation

Interpretation of aerial images is an important task in various applications. Image segmentation can be viewed as the essential step for extracting information from aerial images. Among many developed segmentation methods, the technique of clustering has been extensively investigated and used. However, determining the number of clusters in an image is inherently a difficult problem, especially when a priori information on the aerial image is unavailable. This study proposes a support vector machine approach for clustering aerial images. Three cluster validity indices, distance-based index, Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative measures of the quality of clustering results. Comparisons on the effectiveness of these indices and various parameters settings on the proposed methods are conducted. Experimental results are provided to illustrate the feasibility of the proposed approach. Keywords—Aerial imaging, image segmentation, machine learning, support vector machine, cluster validity index

A new segmentation method for point cloud data

In the process of generating a surface model from point cloud data, a segmentation that extracts the edges and partitions the three-dimensional (3D) point data is necessary and plays an important role in fitting surface patches and applying the scan data to the manufacturing process. Many researchers have tried to develop segmentation methods by fitting curves or surfaces in order to extract geometric information, such as edges and smooth regions, from the scan data. However, the surface- or curve-fitting tasks take a long time and it is also difficult to extract the exact edge points because the scan data consist of discrete points and the edge points are not always included in these data. In this research, a new method for segmenting the point cloud data is proposed. The proposed algorithm uses the octree-based 3D-grid method to handle a large amount of unordered sets of point data. The final 3D-grids are constructed through a refinement process and iterative subdivisioning of cells using the normal values of points. This 3D-grid method enables us to extract edge-neighborhood points while considering the geometric shape of a part. The proposed method is applied to two quadric models and the results are discussed.

Applicability of semi-automatic segmentation for volumetric analysis of brain lesions

This project involves the development of a fast semi-automatic segmentation procedure to make an accurate volumetric estimation of brain lesions. This method has been applied in the segmentation of demyelination plaques in Multiple Sclerosis (MS) and right cerebral hemispheric infarctions in patients with neglect. The developed segmentation method includes several image processing techniques, such as image enhancement, amplitude segmentation, and region growing. The entire program operates on a PC-based computer and applies graphical user interfaces. Twenty three patients with MS and 43 patients with right cerebral hemisphere infarctions were studied on a 0.5 T MRI unit. The MS plaques and cerebral infarctions were thereafter segmented. The volumetric accuracy of the program was demonstrated by segmenting Magnetic Resonance (MR) images of fluid filled syringes. The relative error of the total volume measurement based on the MR images of syringes was 1.5%. Also the repeatability test was carried out as inter- and intra-observer study in which MS plaques of six randomly selected patients were segmented. These tests indicated 7% variability in the inter-observer study and 4% variability in the intra-observer study. Average time used to segment and calculate the total plaque volumes for one patient was 10 min. This simple segmentation method can be utilized in the quantitation of anatomical structures, such as air cells in the sinonasal and temporal bone area, as well as in different pathological conditions, such as brain tumours, intracerebral haematomas and bony destructions.