Pixel-based Image Research Articles

Comparing pixel-based and object-based algorithms for classifying land use of arid basins (Case study: Mokhtaran Basin, Iran)

In this research, two techniques of pixel-based and object-based image analysis were investigated and compared for providing land use map in arid basin of Mokhtaran, Birjand. Using Landsat satellite imagery in 2015, the classification of land use was performed with three object-based algorithms of supervised fuzzy-maximum likelihood, maximum likelihood, and K-nearest neighbor. Nine combinations were examined in terms of scale level (SL10, SL30, and SL50) and the nearest neighborhood (NN3, NN5, and NN7) in an object-based classification. Ultimately, the validity was evaluated through the usage of two disagreement components including allocation disagreement and quantity disagreement. Results of maximum likelihood classification showed higher overall inaccuracycompared to images categorized based on fuzzy-maximum likelihood and object-based nearest neighbor algorithms. The SL30-NN3 object-based classifier decreased the quantity disagreement by 290% compared to the maximum likelihood and 265% compared to fuzzy-maximum likelihood classifiers. For allocation disagreement, these values were equal to 36% and 19%, respectively. Thus, object-based classification had a better performance in land-use classification of Mokhtaran basin.

A tessellation-based colocalization analysis approach for single-molecule localization microscopy

Multicolor single-molecule localization microscopy (λSMLM) is a powerful technique to reveal the relative nanoscale organization and potential colocalization between different molecular species. While several standard analysis methods exist for pixel-based images, λSMLM still lacks such a standard. Moreover, existing methods only work on 2D data and are usually sensitive to the relative molecular organization, a very important parameter to consider in quantitative SMLM. Here, we present an efficient, parameter-free colocalization analysis method for 2D and 3D λSMLM using tessellation analysis. We demonstrate that our method allows for the efficient computation of several popular colocalization estimators directly from molecular coordinates and illustrate its capability to analyze multicolor SMLM data in a robust and efficient manner.

Explorative Study on Mapping Surface Facies of Selected Glaciers from Chandra Basin, Himalaya Using WorldView-2 Data

Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (κ = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (κ = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (κ = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.

EP-2049 Patient specific pixel-based weighting factor bone-only dual-energy x-ray imaging

EP-2049 Patient specific pixel-based weighting factor bone-only dual-energy x-ray imaging

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen’s kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks.

Pixel-based Character Image Compression for Data Transfer from ARM Controller to FPGA System

This paper proposes a pixel-based compression algorithm for character digital image in improving the storage of characters in memory during system operation. In particular, in this algorithm, each character binary image in text is grouped by binary numbers and then encoded to reduce the character image capacity of the character compared to the original character. In addition, a novel point in this algorithm is that one character image type is differently grouped binary numbers for compressing. Therefore, the compressed character image is stored in a memory using an ARM microcontroller system and transferred to an FPGA module for decoding before printing. Moreover, the compression ratio of each character is high or low depending on the font type of image characters. Therefore, the high compression ratio using this compression algorithm will allow saving memory space in the memory system. Simulation results show to illustrate the effectiveness of the proposed algorithm and also this compression algorithm was implemented to texts with characters for encoder data transfer from an ARM microcontroller into an FPGA system for effectively printing the text/logo/barcode/QR code/expired date on products with high speed after decoding. Moreover, this compression algorithm can be developed to apply to many different font types and sizes, as well as be utilized different microcontrollers/Microprocessors connected to FPGA systems for processing with high speed. It means that one industrial system using this algorithm can obtain very high performance related to processing digital image characters.

Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs but to also consider the use of independent encryption keys for both training and testing images for the first time. In this paper, a novel pixel-based image encryption method that maintains important features of original images is proposed for privacy-preserving DNNs. For training, a DNN model is trained with images encrypted by using the proposed method with independent encryption keys. For testing, the model enables us to apply both encrypted images and plain images for image classification. Therefore, there is no need to manage keys. In addition, the proposed method allows us to perform data augmentation in the encrypted domain. In an experiment, the proposed method is applied to well-known networks, that is, deep residual networks and densely connected convolutional networks, for image classification. The experimental results demonstrate that the proposed method, under the use of independent encryption keys, can maintain a high classification performance, and it is robust against ciphertext-only attacks (COAs). Moreover, the results confirm that the proposed scheme is able to classify plain images as well as encrypted images, even when data augmentation is carried out in the encrypted domain.

Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles

Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.

A Multi-Level Approach for Change Detection of Buildings Using Satellite Imagery

In this paper a novel technique for building change detection from remote sensing imagery is presented. It includes two main stages: (1) Object-specific discriminative features are extracted using Morphological Building Index (MBI) to automatically detect the existence of buildings in remote sensing images. (2) Pixel-based image matching is measured on the basis of Mutual Information (MI) of the images by Normalized Mutual Information (NMI). Here, the MBI features values are computed for each of the pair images taken over the same region at two different times and then changes in these two MBI images are measured to indicate the building change. MI is estimated locally for all the pixels for image matching and then thresholding is applied for eliminating those pixels which are responsible for strong similarity. Finally, after getting the MBI and NMI images, a further fusion of these two images is done for refinement of the change result. For evaluation purpose, the experiments are carried on QuickBird, IKONOS images and images taken from Google Earth. The results show that the proposed technique can attain acceptable correctness rates above 90% with Overall Accuracy (OA) 89.52%.

Rural land use problems and management options in Debre Tsyon Kebele, Ethiopia

Examining the current land use problems is essential for designing management options for sustainable use and to obtain optimal benefit out of land. This study was intended to examine the current land use problems and propose management options and strategies for sustainable land use in Debre Tsyon kebele of Enebsie Sar Midir district, Ethiopia. The Landsat-8 OLI-TIRS of 2017, ASTER Global Digital Elevation Model, interview and field observation was employed to generate data on the current land use, land use problems and management options for the future use. The hybrid pixel-based image classification (supervised and unsupervised) method was used to map the land use classes. The results show that the cropland occupy the vast portion (75%) of the study area. However, the improper land use was very high in agricultural and grazing lands use classes. The growing demand for land resources pushed the community to expand farmlands to fragile steep areas with slope > 30%. Of the total agricultural land, 12.6% found in fragile areas with slope > 30%. Likewise, the grazing land has already deteriorated and could not bring pasture for livestock year-round. The vegetation cover has been declining due to improper use. Hence, this calls for the local government to implement appropriate land use plan and efficient land management options to curb the unsustainability spiral.

Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery

ABSTRACTA detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.

MULTI-DATE SENTINEL1 SAR IMAGE TEXTURES DISCRIMINATE PERENNIAL AGROFORESTS IN A TROPICAL FOREST-SAVANNAH TRANSITION LANDSCAPE

Abstract. Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07 % (95 % CI: 85.52–90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region.

An Exploratory Study of Hyoid Visibility, Position, and Swallowing-Related Displacement in a Pediatric Population.

Hyoid position and swallowing-related displacement has been studied in healthy adults and adults with dysphagia, but research is limited in children. The aim of this study was to investigate feasibility of visualizing and measuring position and swallowing-related displacement of the hyoid bone in children. We explored relationships between hyoid displacement, age and aspiration risk scores. Pediatric swallowing data were extracted from a videofluoroscopy database containing recordings from 133 children aged 9days to 21years (mean 36months, SD 3years) referred for videofluoroscopy due to concerns regarding their feeding. Children presented with varying etiologies: neurological, structural, respiratory, and other diagnoses. Still shot images were extracted for the frame of hyoid peak position and a frame showing the hyoid at rest. Pixel-based image analysis software was used to measure hyoid position in three directions (X = anterior, Y = superior, XY = hypotenuse) relative to C4 vertebra. Difference between rest and peak position was used to measure hyoid displacement (X, Y and XY). The hyoid was not visible in children < 9months, but could be reliably visualized and measured in 49 children. Descriptive statistics were collected for hyoid parameters. Age was significantly associated with rest (Y and XY) and peak (Y and XY) hyoid position parameters as well as anterior displacement. No significant associations were observed between hyoid parameters and aspiration risk scores. This study successfully explored hyoid visibility, position and swallowing-related displacement in a pediatric population. Hyoid can be reliably visualized and tracked through videofluoroscopy in children > 9months of age.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Characterization of indicator tree species in neotropical environments and implications for geological mapping

Geobotanical remote sensing (GbRS) in the strict sense is an indirect approach to obtain geological information in heavily vegetated areas for mineral prospecting and geological mapping. Using ultra- and hyperspectral technologies, the goals of this research comprise the definition and mapping of Neotropical tree species that are associated with geological facies (here called geo-environments) as well as their spectral discrimination at leaf and crown scales. This work also aims to investigate the possible relationship between leaf and crown spectral and chemical properties. The study was developed at the Mogi-Guaçu Ecological Station, in the Cerrado domain, southeastern Brazil. Data from 70 sample units, such as sediment texture and species from inventories, were first analyzed through vectorial quantization using Self-Organizing Maps (SOM). Principal Component Analysis and Spearman's ranked correlation coefficients were used to define geo-environments and target-species, respectively. Biochemical and visible to shortwave infrared (VSWIR) point spectral data (350–2500 nm) were collected from the leaves of the target-species, during both rainy and dry seasons. Spectral data from target-species crowns were obtained from hyperspectral images (530–2.532 nm, ProSpecTIR-VS sensor) with 1 m spatial resolution, and acquired in the beginning of the dry season. These spectra were classified using Multiple Endmember Spectral Mixture Analysis (MESMA) with two endmembers (EMs). Based on the MESMA results with two EMs, the best dataset per target-species was chosen for pixel-based image unmixing with three EMs (target-species, other vegetation types and shade). From 121 species sampled in the field, two proved to be associated with floodplains (Alluvial Deposits sequence), two with hills and plateaus of the Aquidauna Formation (Carboniferous sedimentary rocks, Paraná Basin), and two more with a specific facies of the Aquidauna Formation that has a distinctive presence of coarse and very coarse sand. Five target-species were well discriminated at the leaf scale, reaching 90.0% and 85.0% of global accuracies in the rainy season and in the dry season, respectively. Accurate spectral discrimination appears to be linked to the considerable biochemical variability of their leaves in both seasons. Three species were discriminated at the crown scale, with 70.6% of global accuracy. When eight other landscape scale vegetation classes were included in the analyses, only Qualea grandiflora Mart. produced a satisfactory accuracy (61.1% and 100% of producer and user's accuracies, respectively). The spatial distribution of its fraction in the unmixed image, particularly, matches with the geological facies to which it was associated in the field. Ecological requirements for successfully mapping indicator species include broad and random distribution of the target-species' population, and singular physiological, phenological (and spectral) behavior at the imagery acquisition date. Our study shows that, even in tropical conditions, it is possible to use plant species mapping to support geological delineation, where rock exposures are typically rare.

Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery

In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB), random trees (RT), and support vector machine (SVM). For pixel-based classification, we utilized the maximum likelihood classifier (MLC). Specifically, we explored the influence of repeated sampling on classification results with different training sample sizes. We found that (1) except the overall accuracy of NB, those of the other four classifiers increased as the training sample size increased; (2) repeated sampling had a significant effect on classification accuracy, especially for the DT and NB classifiers; and (3) SVM achieved the best classification accuracy. In addition, the performance of the object-based classifiers was superior to that of the pixel-based classifier. The results of this study can provide guidance on the training sample size and classifier selection.

Examining Urbanization Dynamics in Turkey Using DMSP–OLS and Socio-Economic Data

The present study tried to determine the spatial expansion of urban areas in all the cities in Turkey and to examine the relationship between this spatial expansion with the related demographic, employment, educational, industrial and other indicators using the Geographical Information Systems. The present study was made up of three parts. In the first part, the urban areas in Turkey were determined using the pixel-based image classification methods. In the second part, the development levels of the cities, one of socio-economic indicators, were determined using the Principle Components Analysis. In the last part, statistical analyses were conducted to examine the relationship between the development levels of cities and urban areas. The results obtained revealed that the cities with larger urban area were more in the western part of the country and while those with less urban area were in the eastern part of the country. A similar distribution was also true for the socio-economic development order. The Pearson correlation coefficient of 0.711 between these variables demonstrated that there was a linear positive correlation in between. According to the results of Moran’s I spatial auto-correlation analysis, the distribution of both urban area and socio-economic development throughout the country had a relationship with place. According to geographically weighted regression analysis, the demographic, education and health indicators had the biggest influence on urban area.

Comparison of pixel-based and object-based image classification techniques in extracting information from UAV imagery data

As the rapid development is being focused in the urban area, there is a need for the utilisation of a rapid system for updating this profile immediately. One of the current technologies being applied in recent years is the use of unmanned aerial vehicle (UAV) for mapping purposes. The use of UAV is widespread in various fields because it is low cost, has high resolution and is able to fly at low altitude without the constraints of cloudy weather. Typically, the method of data extraction for UAV in Malaysia is still very limited and the traditional methods are still being implemented by some industries. The features from aerial photo orthomosaic are manually detected and digitised from visual interpretation for the mapping purposes. Unfortunately, these methods are tedious, expensive, consume much time, and may involve much fieldwork, to acquire only a limited information. Pixel-based technique is often used to extract low level features where the image is classified according to the spectral information where the pixels in the overlapping region will be misclassified due to the confusion among the classes. The supervised object-based image analysis (OBIA) classification technique is widely used nowadays for automatic data extraction. Therefore, the general objective of this study is to assess the capability of UAV with high resolution data for image classifications. The pixel-based and OBIA classifications were compared using the Support Vector Machine (SVM) classifier. The classifications were assessed using different numbers of sample size. The result shows that OBIA gives a better result of Overall Accuracy (OA) than pixel-based. The consequences of this study accommodate further understanding and additional insight of utilising OBIA technique with different classifiers for the extended study.

GEOBIA an (Geographic) Object-Based Image Analysis for coastal mapping in Indonesia: A Review

The traditionally human-visual or manual interpretation is generally chosen method for spatial feature extraction on map producing process in Indonesia, although the pixel-based feature extraction is used in particular step. The main problem of human-visual method is the dependency to operator capacity. Subjectivity may occur because each operator has different ability. In early 20th century, the image segmentation method was still developing and now Geographic Object-based Image Analysis (GEOBIA) method is introduced to mapping process. Researchers are still developing and applying GEOBIA for many mapping purposes. The advantage of GEOBIA is object-based segmentation which contextually influenced by spatial feature characteristics. GEOBIA provides more meaningful information than traditional pixel-based image analysis by allowing less well-defined edges or borders between different classes. On the resulting maps, there are divisions between different types of object classification, for example where some species of mangroves meet are not generally represented by a single object. The developing of remotely sensed imagery data quality requires the developing of extraction method as well. This article discusses the implementation of GEOBIA for coastal mapping in Indonesia. Some research combined pixel-based and GEOBIA method, and some get the overall accuracy > 70% of GEOBIA method. By developing a new segmentation accuracy measure, we evaluated that segmentation accuracies decrease with increasing segmentation scales and the negative impact of under-segmentation errors become significantly rise at large scale. The use of GEOBIA for coastal mapping in Indonesia are still lacking, locally applicable and still developing the transferability rule sets. The conclusion is that the innovation is needed to make GEOBIA method applicable for mass production of Indonesian coastal map.

Introduction to a mechanism for automated myocardium boundary detection with displacement encoding with stimulated echoes (DENSE).

Objective:Displacement ENcoding with Stimulated Echoes (DENSE) is an MRI technique developed to encode phase related to myocardial tissue displacements, and the displacement information directly applied towards detecting left-ventricular (LV) myocardial motion during the cardiac cycle. The purpose of this study is to present a novel, three-dimensional (3D) DENSE displacement-based and magnitude image quantization-based, semi-automated detection technique for myocardial wall motion, whose boundaries are used for rapid and automated computation of 3D myocardial strain.Methods:The architecture of this boundary detection algorithm is primarily based on pixelwise spatiotemporal increments in LV tissue displacements during the cardiac cycle and further reinforced by radially searching for pixel-based image gradients in multithreshold quantized magnitude images. This spatiotemporal edge detection methodology was applied to all LV partitions and their subsequent timeframes that lead to full 3D LV reconstructions. It was followed by quantifications of 3D chamber dimensions and myocardial strains, whose rapid computation was the primary motivation behind developing this algorithm. A pre-existing two-dimensional (2D) semi-automated contouring technique was used in parallel to validate the accuracy of the algorithm and both methods tested on DENSE data acquired in (N = 14) healthy subjects. Chamber quantifications between methods were compared using paired t-tests and Bland–Altman analysis established regional strain agreements.Results:There were no significant differences in the results of chamber quantifications between the 3D semi-automated and existing 2D boundary detection techniques. This included comparisons of ejection fractions, which were 0.62 ± 0.04 vs 0.60 ± 0.06 (p = 0.23) for apical, 0.60 ± 0.04 vs 0.59 ± 0.05 (p = 0.76) for midventricular and 0.56 ± 0.04 vs 0.58 ± 0.05 (p = 0.07) for basal segments, that were quantified using the 3D semi-automated and 2D pre-existing methodologies, respectively. Bland–Altman agreement between regional strains generated biases of 0.01 ± 0.06, –0.01 ± 0.01 and 0.0 ± 0.06 for the radial, circumferential and longitudinal directions, respectively.Conclusion:A new, 3D semi-automated methodology for contouring the entire LV and rapidly generating chamber quantifications and regional strains is presented that was validated in relation to an existing 2D contouring technique.Advances in knowledge:This study introduced a scientific tool for rapid, semi-automated generation of clinical information regarding shape and function in the 3D LV.