Homogeneity Criterion Research Articles

TU-AB-303-03: CBCT-Based On-Site Simulation, Planning, and Delivery (OSPD) for Whole Brain Radiotherapy

Purpose: To demonstrate the feasibility of a CBCT-based on-site simulation, planning, and delivery (OSPD) for whole brain radiotherapy, in which all steps from imaging, planning to treatment delivery are performed at the treatment unit in one appointment time slot. This work serves as the proof of concept for future OSPD single fraction radiation therapy. Methods: An integrated on-site imaging, planning and delivery workflow was developed and tested for whole brain radiotherapy. An automated two-opposed-oblique-beam plan is created by utilizing the treatment planning system scripting and simple field-in-field IMRT. The IMRT plan is designed with maximum 8 control points to cover the target volume consisting of the brain to C1/C2 of the spinal cord, with dose homogeneity criteria from −5% to +7% of the prescription dose. Due to inaccuracy of reconstructed Hounsfield unit numbers in CBCT images, the dose distribution is calculated with non-heterogeneity correction introducing only clinically insignificant dose discrepancy. A coherent and synchronized workflow was designed for a team of attending physician, physicist, therapists, and dosimetrist to work closely with the ability to quickly modify, approve, and implement the treatment. Results: Thirty-one patients have been treated with this OSPD treatment, without compromising the plan quality compared to our regular clinically used parallel apposed 2D plans. The average time for these procedures are 48.02 ±11.55 minutes from the time patient entered the treatment room until s/he exited, and 35.09 ±10.35 minutes from starting CBCT until last beam delivered. This time duration is comparable to the net time when individual tasks are summed up during our regular CT- based whole brain planning and delivery. Conclusions: The OSPD whole brain treatment has been tested to be clinically feasible. The next step is to further improve the efficiency and to streamline the workflow. Other disease sites will be also tested with this new technology.

Unsupervised Quantification of Under- and Over-Segmentation for Object-Based Remote Sensing Image Analysis

Object-based image analysis (OBIA) has been widely adopted as a common paradigm to deal with very high-resolution remote sensing images. Nevertheless, OBIA methods strongly depend on the results of image segmentation. Many segmentation quality metrics have been proposed. Supervised metrics give accurate quality estimation but require a ground-truth segmentation as reference. Unsupervised metrics only make use of intrinsic image and segment properties; yet most of them strongly depend on the application and do not deal well with the variability of objects in remote sensing images. Furthermore, the few metrics developed in a remote sensing context mainly focus on global evaluation. In this paper, we propose a novel unsupervised metric, which evaluates local quality (per segment) by analyzing segment neighborhood, thus quantifying under- and over-segmentation given a certain homogeneity criterion. Additionally, we propose two variants of this metric, for estimating global quality of remote sensing image segmentation by the aggregation of local quality scores. Finally, we analyze the behavior of the proposed metrics and validate their applicability for finding segmentation results having good tradeoff between both kinds of errors.

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

This paper proposes an adaptive algorithm for clustering cumulative probability distribution functions (c.p.d.f.) of a continuous random variable, observed in different populations, into the minimum homogeneous clusters, making no parametric assumptions about the c.p.d.f.’s. The distance function for clustering c.p.d.f.’s that is proposed is based on the Kolmogorov–Smirnov two sample statistic. This test is able to detect differences in position, dispersion or shape of the c.p.d.f.’s. In our context, this statistic allows us to cluster the recorded data with a homogeneity criterion based on the whole distribution of each data set, and to decide whether it is necessary to add more clusters or not. In this sense, the proposed algorithm is adaptive as it automatically increases the number of clusters only as necessary; therefore, there is no need to fix in advance the number of clusters. The output of the algorithm are the common c.p.d.f. of all observed data in the cluster (the centroid) and, for each cluster, the Kolmogorov–Smirnov statistic between the centroid and the most distant c.p.d.f. The proposed algorithm has been used for a large data set of solar global irradiation spectra distributions. The results obtained enable to reduce all the information of more than 270,000 c.p.d.f.’s in only 6 different clusters that correspond to 6 different c.p.d.f.’s.

Interactive segmentation and visualization of DTI data using a hierarchical watershed representation.

Magnetic resonance diffusion tensor imaging (DTI) measures diffusion of water molecules and is used to characterize orientation of white matter fibers and connectivity of neurological structures. Segmentation and visualization of DT images is challenging, because of low data quality and complexity of anatomical structures. In this paper, we propose an interactive segmentation approach, based on a hierarchical representation of the input DT image through a tree structure. The tree is obtained by successively merging watershed regions, based on the morphological waterfall approach, hence the name watershed tree. Region merging is done according to a combined similarity and homogeneity criterion. We introduce filters that work on the proposed tree representation, and that enable region-based attribute filtering of DTI data. Linked views between the visualizations of the simplified DT image and the tree enable a user to visually explore both data and tree at interactive rates. The coupling of filtering, semiautomatic segmentation by labeling nodes in the tree, and various interaction mechanisms support the segmentation task. Our method is robust against noise, which we demonstrate on synthetic and real DTI data.

Image Compression Using Modified Huffman Coding

Image compression using modified Huffman coding can be obtained by dividing the original grey level image into unoverlapped blocks and setting homogeneity criterion to test each block individually depending on a threshold value and the well-known techniques of Quad-tree decomposition. By taking threshold value as 0.2, Huffman coding for encoding and decoding of the image are the techniques which have been applied for the compression of satellite imageries. The results indicate that for a Lena image, CR is 9.79 and PSNR value is 24.96, Satellite image values are 12.69 and 25.01, respectively. The noise used is salt and pepper, and it is filtered by wellknown technique of Wiener filter. The results are presented and discussed in this paper.

Vicarious calibration of S-NPP/VIIRS day–night band using deep convective clouds

The Suomi National Polar-orbiting Partnership (S-NPP) satellite was successfully launched in 28 October 2011. The day–night band (DNB) is mounted on the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NPP to greatly improve upon the night time imaging capabilities. In order to cover the wide dynamic radiation range, the Low Gain Stage (LGS), Median Gain Stage (MGS) and High Gain Stage (HGS) are employed to view the earth at daytime, twilight and nighttime respectively. Especially, the radiance measured at night is relatively low, so the radiometric calibration is too difficult to calculate directly. To independently evaluate the radiometric calibration of DNB HGS at night, the vicarious calibration of S-NPP/VIIRS DNB on the basis of the deep convection clouds (DCCs) under lunar illumination is presented. DCCs are selected by applying the spatial homogeneity criteria of brightness temperature and radiance. To determine the macro- and microphysical properties of DCCs at night, the CloudSat–CALIPSO nighttime observation data for 2-year period from January 2009 to December 2010 are analyzed. It is demonstrated that the average of the cloud top heights, cloud base heights, and cloud effective radius are 15km, 0.9km and 30μm separately over tropical area from 2009 to 2010. These properties of DCCs are assumed to be the typical values for DCCs. The top of atmosphere reflectance values of the identified DCC targets at the VIIRS DNB at night are simulated by the promoted radiative transfer model, which combined the lunar irradiance model with the SCIATRAN to realize the radiative transfer simulation at any time. The comparison between the simulated reflectance values and the corresponding DNB HGS measurements for a 1-year period from August 2012 to July 2013 shows that, on a daily basis, the DNB HGS data are calibrated on orbit within a −4.9%±8.8% uncertainty range near full moon. The vicarious calibration method using DCCs are suggested to be reliably used for DNB HGS radiometric calibration directly.

Optimal feature space for semantic image segmentation

A new method for semantic segmentation with object adaptation is proposed. Segmentation is performed on a feature map obtained as a weighted sum of HSV-space components: hue, saturation, and- value. Homogeneity criterion for grouping pixels into clusters and weighted sum coefficients is adjusted using the particle swarm optimization (PSO) algorithm. The method is tested using images wherein a face is a semantically meaningful object. The accuracy of the segmentation is shown to be higher when using a feature map obtained as a weighted sum of color components than in cases when a single color component is used. The accuracy of the proposed method is estimated as the correspondence of a fragmented segment to a face region, detected using the Viola-Jones method, within an image.

Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series

Landscapes can be described by seasonal and spatial patterns linked to vegetation type and phenology, environmental conditions, and human activities. The objective of this work is to propose and test an approach for delineating homogeneous landscape units at a regional scale by using only Earth observation data. We used MODIS (Moderate Imaging Spectroradiometer) images from 2007 to 2011, acquired over the whole continental French territory at 250m spatial resolution. The data set includes time series of the Enhanced Vegetation Index (EVI) and time series of five Haralick texture indices. A principal components analysis (PCA) allowed us to choose the most representative indices (spectral and textural) and dates to be used in the region-growing segmentation. Different combinations of input data, as well as different segmentation parameters, were tested and compared using unsupervised evaluation methods. These methods were used to analyze the radiometric homogeneity of the regions and the radiometric disparity between regions when changing the homogeneity criterion of the segmentation. The best segmentation results obtained included three EVI images, together with three images of the texture 2nd moment, corresponding to the average of the months of April, July and December from 2007 to 2011. The optimum homogeneity criterion for the region-growing segmentation using this combination of variables was 15. We believe this method is applicable at other scales and other data sets for vegetation and biodiversity studies, and for habitat mapping.

SAR Image Segmentation Using Morphological Attribute Profiles

Abstract. In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

A model on achieving higher performance in the classification of hyperspectral satellite data: a case study on Hyperion data

Hyperspectral remote sensing data is characterized by the large number of contiguous spectral bands with narrow bandwidth. Enormous information available in hyperspectral data is quite challenging for classification as compared to multispectral remote sensing data. Most of the widely used conventional'hard'classifiersareproducinginconsistentclas- sification results while employed in classification of hyperspectral data. In this paper, we present an effective hyperspectral classification model for achieving higher accu- racy. The proposed model is characterized by three major components: dimensionality reduction using principal com- ponent analysis (PCA), multiresolution segmentation, and fuzzy membership-based nearest neighbor (NN)-classifica- tion. Here, the bands of the dimensionality-reduced images are represented by the first principal component (PC) of each of the spectral region covered by the hyperspectral sensor. Then, multiresolution segmentation is carried out on these PC composite images based on color and shape homogeneity criterion. The conventional NN-classifier is effectively used by appropriate utilization of fuzzy membership function de- fined on a set of optimal features derived from the segmented image objects. We demonstrate a case study on Hyperion sensor data of Earth Observing-1 (EO-1) satellite. A compar- ative assessment is carried out with other competing tech- niques such as spectral angle mapper (SAM), artificial neural network (ANN), and support vector machine (SVM) on a set of images with different land cover surfaces. The proposed classification model outperforms the existing classification approaches investigated here.

ClustOfVar and the segmentation of cruise passengers from mixed data: Some managerial implications

Market segmentation comprises a variety of measurement methodologies that are used to support management, marketing and promotional policies in tourism destinations. This study applies ClustOfVar, a relatively recent algorithm for cluster analysis from mixed variables. The technique finds groups of variables by using a homogeneity criterion based on the sum of correlation ratios for qualitative variables, and squared correlations for quantitative variables. Then principal components from each cluster of variables are extracted in order to segment cruise passengers. CART analysis is finally used for the sake of finding the variables that drove the formation of the clusters. All the analysis is based on an official survey of tourists who disembarked in Uruguayan ports. The analysis identified five clusters, both for variables and cruise passengers. Findings highlight the importance of the enjoyment of the contact with local people for the economic impact, as well as the important role of age and gender related variables. Managerial implications are also discussed.

Multifocus Images Fusion Based On Homogenity and Edges Measures

Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM, RVS and WT) and spatial techniques (HPFA, HFA and HFM). As these techniques have been developed and build programs using the language MATLAB (b 2010). In this work homogeneity criteria have been suggested for evaluation fused digital image's quality, especially fine details. This criterion is correlation criteria to guess homogeneity in different regions within the image by taking a number of blocks of different regions in the image and different sizes and work shifted blocks per pixel. As dependence was on traditional statistical criteria such as (mean, standard deviation, and signal to noise ratio, mutual information and spatial frequency) and compared with the suggested criteria to the work. The results showed that the evaluation process was effective and well because it took into measure the quality of the homogenous regions.

Multifocus Images Fusion Based On Homogenity and Edges Measures

Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM, RVS and WT) and spatial techniques (HPFA, HFA and HFM). As these techniques have been developed and build programs using the language MATLAB (b 2010). In this work homogeneity criteria have been suggested for evaluation fused digital image's quality, especially fine details. This criterion is correlation criteria to guess homogeneity in different regions within the image by taking a number of blocks of different regions in the image and different sizes and work shifted blocks per pixel. As dependence was on traditional statistical criteria such as (mean, standard deviation, and signal to noise ratio, mutual information and spatial frequency) and compared with the suggested criteria to the work. The results showed that the evaluation process was effective and well because it took into measure the quality of the homogenous regions.

Two‐dimensional imaging in a lightweight portable MRI scanner without gradient coils

As the premiere modality for brain imaging, MRI could find wider applicability if lightweight, portable systems were available for siting in unconventional locations such as intensive care units, physician offices, surgical suites, ambulances, emergency rooms, sports facilities, or rural healthcare sites. We construct and validate a truly portable (<100 kg) and silent proof-of-concept MRI scanner which replaces conventional gradient encoding with a rotating lightweight cryogen-free, low-field magnet. When rotated about the object, the inhomogeneous field pattern is used as a rotating spatial encoding magnetic field (rSEM) to create generalized projections which encode the iteratively reconstructed two-dimensional (2D) image. Multiple receive channels are used to disambiguate the nonbijective encoding field. The system is validated with experimental images of 2D test phantoms. Similar to other nonlinear field encoding schemes, the spatial resolution is position dependent with blurring in the center, but is shown to be likely sufficient for many medical applications. The presented MRI scanner demonstrates the potential for portability by simultaneously relaxing the magnet homogeneity criteria and eliminating the gradient coil. This new architecture and encoding scheme shows convincing proof of concept images that are expected to be further improved with refinement of the calibration and methodology.

English

A Survey on Modified SVM for Image Segmentation &nbsp; &nbsp; International Journal of Computer &amp; Organization Trends&nbsp; (IJCOT) &nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp;&nbsp; &nbsp; © 2014 by IJCOT Journal Volume - 4 Issue - 2 Year

La transcendencia de lo cotidiano: vínculos, chistes y subjetividad

This paper proposes a psycho-sociological approach to the configuration of human bonds. Bonds are discussed in its most latent, less obvious and explicit, in order to reveal those emotional and representational elements that tend to be expressed in a rather obvious. The empirical material of the research has been the set of jokes told in different “focus groups”, formed from social homogeneity criteria. We analyzed the associative chains developed in group dynamics. We find that the jokes being universal social phenomenon, the dormant accounts arising from the analysis of each discus-sion group, have shown significant differences between them, being expressive loop model of each social context. It also confirms the phenomenon of the joke as a valuable tool for social research.

Probabilistic region growing method for improving magnetic resonance image segmentation

In this paper, we propose a new region growing algorithm called ‘probabilistic region growing (PRG)’ which could improve the magnetic resonance image (MRI) segmentation. The proposed approach includes a threshold based on estimating the probability of pixel intensities of a given image. This threshold uses a homogeneity criterion which is obtained automatically from characteristics of the regions. The homogeneity criterion will be calculated for each pixel as well as the probability of pixel value. The proposed PRG algorithm selects the pixels sequentially in a random walk starting at the seed point, and the homogeneity criterion is updated continuously. The proposed PRG algorithm is applied to the challenging applications: grey matter/white matter segmentation in MRI data sets. The experimental results compared with other segmentation techniques show that the proposed PRG method produces more accurate and stable results.

Range image segmentation for tree detection in forest scans

Abstract. To make a tree-wise analysis inside a forest stand, the trees have to be identified. An interactive segmentation is often labourintensive and time-consuming. Therefore, an automatic detection process will aspired using a range image. This paper presents a method for the segmentation of range images extracted from terrestrial laser scanner point clouds of forest stands. After range image generation the segmentation is carried out with a connectivity analysis using the differences of the range values as homogeneity criterion. Subsequently, the tree detection is performed interactively by analysing one horizontal image line. When passing objects with a specific width, the object indicates a potential tree. By using the edge points of a segmented pixel group the tree position and diameter is calculated. Results from one test site are presented to show the performance of the method.

Image Segmentation based on Histogram Analysis and Soft Thresholding

researched area in the field of object oriented image processing procedure is efficient and effective image segmentation. Segmentation is a process of partitioning a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. In this paper, we introduce a spatial domain segmentation framework based on the histogram analysis and soft threshold. The histogram analysis uses discontinuity and similarity properties of image statistics in tandem with distribution of pixels to define the binary label for a homogenous region. The soft threshold used for classification is determined based on the localized statistics of the image in consideration for merging of the regions. Simulation results and analysis would verify that the proposed algorithm shows good performance in image segmentation without choosing the region of interest.

Automatic segmentation of the carotid artery bifurcation region with a region-growing approach

The original contribution is to propose an intensity-based segmentation algorithm for extracting the carotid artery bifurcation region and validate the proposed solution on real patients’ CTA data. The proposed homogeneity criteria allow the production of locally smooth segmentations and prevent excessive growth into neighboring tissues of similar densities. The obtained segmentation results are compared to manual findings of a radiologist and measured with the Dice similarity coefficient (D si ). This technique has been shown to be a reliable tool as effective as top state-of-the-art methods (D si =93.6%±3.5% ).