Relevant bibliographies by topics / Segmentation accuracy

Academic literature on the topic 'Segmentation accuracy'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Segmentation accuracy.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Segmentation accuracy"

1

Zhang, Jin Xi, Hong Zhi Yu, Ning Ma, and Zhao Yao Li. "The Phoneme Automatic Segmentation Algorithms Study of Tibetan Lhasa Words Continuous Speech Stream." Advanced Materials Research 765-767 (September 2013): 2051–54. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2051.

Full text
Abstract:
In this paper, we adopt two methods to voice phoneme segmentation when building Tibetan corpus: One is the traditional artificial segmentation method, one is the automatic segmentation method based on the Mono prime HMM model. And experiments are performed to analyze the accuracy of both methods of segmentations. The results showed: Automatic segmentation method based tone prime HMM model helps to shorten the cycle of building Tibetan corpus, especially in building a large corpus segmentation and labeling a lot of time and manpower cost savings, and have greatly improved the accuracy and consistency of speech corpus annotation information.
APA, Harvard, Vancouver, ISO, and other styles
2

Akcay, Ozgun, Emin Avsar, Melis Inalpulat, Levent Genc, and Ahmet Cam. "Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery." ISPRS International Journal of Geo-Information 7, no. 11 (October 31, 2018): 424. http://dx.doi.org/10.3390/ijgi7110424.

Full text
Abstract:
Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.
APA, Harvard, Vancouver, ISO, and other styles
3

Van den Broeck, Joyce, Evie Vereecke, Roel Wirix-Speetjens, and Jos Vander Sloten. "Segmentation accuracy of long bones." Medical Engineering & Physics 36, no. 7 (July 2014): 949–53. http://dx.doi.org/10.1016/j.medengphy.2014.03.016.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Schmidt-Richberg, A., J. Fiehler, T. Illies, D. Möller, H. Handels, D. Säring, and N. D. Forkert. "Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets." Methods of Information in Medicine 51, no. 05 (2012): 415–22. http://dx.doi.org/10.3414/me11-02-0037.

Full text
Abstract:
Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.
APA, Harvard, Vancouver, ISO, and other styles
5

Rossi, Farli. "APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION." Jurnal Teknoinfo 15, no. 1 (January 15, 2021): 56. http://dx.doi.org/10.33365/jti.v15i1.945.

Full text
Abstract:
Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this study, we apply a semi-automated technique that combines an active contour and low-level processing techniques in lung lesion segmentation by extracting lung lesions from thoracic Positron Emission Tomography (PET)/Computed Tomography (CT) images. The lesions were first segmented in Positron Emission Tomography (PET) images which have been converted previously to Standardised Uptake Values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To measure accuracy, the Jaccard Index (JI) was used. Jaccard Index (JI) was calculated by comparing the segmented lesion to alternative segmentations obtained from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results showed that the semi-automated technique (combination techniques between an active contour and low-level processing) in lung lesion segmentation has moderate accuracy with an average JI value of 0.76±0.12.
APA, Harvard, Vancouver, ISO, and other styles
6

Ferrante, Matteo, Lisa Rinaldi, Francesca Botta, Xiaobin Hu, Andreas Dolp, Marta Minotti, Francesca De Piano, et al. "Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models." Journal of Clinical Medicine 11, no. 24 (December 9, 2022): 7334. http://dx.doi.org/10.3390/jcm11247334.

Full text
Abstract:
Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models’ accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings.
APA, Harvard, Vancouver, ISO, and other styles
7

Yang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, Lin Ma, Strahinja Stojadinovic, Robert Timmerman, Tu Dan, Zabi Wardak, Weiguo Lu, and Xuejun Gu. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (January 19, 2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.

Full text
Abstract:
Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.
APA, Harvard, Vancouver, ISO, and other styles
8

Vania, Malinda, Dawit Mureja, and Deukhee Lee. "Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels." Journal of Computational Design and Engineering 6, no. 2 (February 13, 2019): 224–32. http://dx.doi.org/10.1016/j.jcde.2018.05.002.

Full text
Abstract:
Abstract There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we developed a fully automatic segmentation method to segment the spine from CT images, and we compared our segmentation results with reference segmentations obtained by well-known methods. We use a hybrid method. This method combines the convolutional neural network (CNN) and fully convolutional network (FCN), and utilizes class redundancy as a soft constraint to greatly improve the segmentation results. The proposed method was found to significantly enhance the accuracy of the segmentation results and the system processing time. Our comparison was based on 12 measurements: the Dice coefficient (94%), Jaccard index (93%), volumetric similarity (96%), sensitivity (97%), specificity (99%), precision (over segmentation 8.3 and under segmentation 2.6), accuracy (99%), Matthews correlation coefficient (0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and global consistency error (0.02). We experimented with CT images from 32 patients, and the experimental results demonstrated the efficiency of the proposed method. Highlights A method to enhance the accuracy of spine segmentation from CT data was proposed. The proposed method uses Convolutional Neural Network via redundant generation of class labels. Experiments show the segmentation accuracy has been enhanced.
APA, Harvard, Vancouver, ISO, and other styles
9

Wei, Yun Tao, and Yi Bing Zhou. "Segmentations of Liver and Hepatic Tumors from 3D Computed Tomography Abdominal Images." Advanced Materials Research 898 (February 2014): 684–87. http://dx.doi.org/10.4028/www.scientific.net/amr.898.684.

Full text
Abstract:
The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumors were compared to their reference standard delineations manually performed by a specialist. Segmentation accuracy has been assessed through the following evaluation framework: dice similarity coefficient, false negative ratio, false positive ratio and processing time. The implemented initialization method allows fully automatic segmentation leading to superior overall performances of graph-cut algorithm in terms of accuracy and processing time. The initialization method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended.
APA, Harvard, Vancouver, ISO, and other styles
10

Lacerda, M. G., E. H. Shiguemori, A. J. Damião, C. S. Anjos, and M. Habermann. "IMPACT OF SEGMENTATION PARAMETERS ON THE CLASSIFICATION OF VHR IMAGES ACQUIRED BY RPAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 4, 2020): 43–48. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-43-2020.

Full text
Abstract:
Abstract. RPAs (Remotely Piloted Aircrafts) have been used in many Remote Sensing applications, featuring high-quality imaging sensors. In some situations, the images are interpreted in an automated fashion using object-oriented classification. In this case, the first step is segmentation. However, the setting of segmentation parameters such as scale, shape, and compactness may yield too many different segmentations, thus it is necessary to understand the influence of those parameters on the final output. This paper compares 24 segmentation parameter sets by taking into account classification scores. The results indicate that the segmentation parameters exert influence on both classification accuracy and processing time.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Segmentation accuracy"

1

Zhu, Fan. "Brain perfusion imaging : performance and accuracy." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8848.

Full text
Abstract:
Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. The purpose of my PhD research is to develop novel methodologies for improving the efficiency and quality of brain perfusion-imaging analysis so that clinical decisions can be made more accurately and in a shorter time. This thesis consists of three parts: My research investigates the possibility that parallel computing brings to make perfusion-imaging analysis faster in order to deliver results that are used in stroke diagnosis earlier. Brain perfusion analysis using local Arterial Input Functions (AIF) techniques takes a long time to execute due to its heavy computational load. As time is vitally important in the case of acute stroke, reducing analysis time and therefore diagnosis time can reduce the number of brain cells damaged and improve the chances for patient recovery. We present the implementation of a deconvolution algorithm for brain perfusion quantification on GPGPU (General Purpose computing on Graphics Processing Units) using the CUDA programming model. Our method aims to accelerate the process without any quality loss. Specific features of perfusion source images are also used to reduce noise impact, which consequently improves the accuracy of hemodynamic maps. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR) makes use of the temporal information in the perfusion source imges to reduce the noise level. Over the entire image, our noise reduction method based on Gaussian process regression gains a 99% contrast-to-noise ratio improvement over the raw image and also improves the quality of hemodynamic maps, allowing a better identification of edges and detailed information. At the level of individual voxels, GPR provides a stable baseline, helps identify key parameters from tissue time-concentration curves and reduces the oscillations in the curves. Furthermore, the results show that GPR is superior to the alternative techniques compared in this study. My research also explores automatic segmentation of perfusion images into potentially healthy areas and lesion areas, which can be used as additional information that assists in clinical diagnosis. Since perfusion source images contain more information than hemodynamic maps, good utilisation of source images leads to better understanding than the hemodynamic maps alone. Correlation coefficient tests are used to measure the similarities between the expected tissue time-concentration curves (from reference tissue) and the measured time-concentration curves (from target tissue). This information is then used to distinguish tissues at risk and dead tissues from healthy tissues. A correlation coefficient based signal analysis method that directly spots suspected lesion areas from perfusion source images is presented. Our method delivers a clear automatic segmentation of healthy tissue, tissue at risk and dead tissue. From our segmentation maps, it is easier to identify lesion boundaries than using traditional hemodynamic maps.
APA, Harvard, Vancouver, ISO, and other styles
2

Kraljevic, Matija. "Character recognition in natural images : Testing the accuracy of OCR and potential improvement by image segmentation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187991.

Full text
Abstract:
In recent years, reading text from natural images has gained renewed research attention. One of the main reasons for this is the rapid growth of camera-based applications on smart phones and other portable devices. With the increasing availability of high performance, low-priced, image-capturing devices, the application of scene text recognition is rapidly expanding and becoming increasingly popular. Despite many efforts, character recognition in natural images, is still considered a challenging and unresolved problem. The difficulties stem from the fact that natural images suffer from a wide variety of obstacles such as complex backgrounds, font variation, uneven illumination, resolution problems, occlusions, perspective effects, just to mention a few. This paper aims to test the accuracy of OCR in character recognition of natural images as well as testing the possible improvement in accuracy after implementing three different segmentation methods.The results showed that the accuracy of OCR was very poor and no improvments in accuracy were found after implementing the chosen segmentation methods.
APA, Harvard, Vancouver, ISO, and other styles
3

Ghattas, Andrew Emile. "Medical imaging segmentation assessment via Bayesian approaches to fusion, accuracy and variability estimation with application to head and neck cancer." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5759.

Full text
Abstract:
With the advancement of technology, medical imaging has become a fast growing area of research. Some imaging questions require little physician analysis, such as diagnosing a broken bone, using a 2-D X-ray image. More complicated questions, using 3-D scans, such as computerized tomography (CT), can be much more difficult to answer. For example, estimating tumor growth to evaluate malignancy; which informs whether intervention is necessary. This requires careful delineation of different structures in the image. For example, what is the tumor versus what is normal tissue; this is referred to as segmentation. Currently, the gold standard of segmentation is for a radiologist to manually trace structure edges in the 3-D image, however, this can be extremely time consuming. Additionally, manual segmentation results can differ drastically between and even within radiologists. A more reproducible, less variable, and more time efficient segmentation approach would drastically improve medical treatment. This potential, as well as the continued increase in computing power, has led to computationally intensive semiautomated segmentation algorithms. Segmentation algorithms' widespread use is limited due to difficulty in validating their performance. Fusion models, such as STAPLE, have been proposed as a way to combine multiple segmentations into a consensus ground truth; this allows for evaluation of both manual and semiautomated segmentation in relation to the consensus ground truth. Once a consensus ground truth is obtained, a multitude of approaches have been proposed for evaluating different aspects of segmentation performance; segmentation accuracy, between- and within -reader variability. The focus of this dissertation is threefold. First, a simulation based tool is introduced to allow for the validation of fusion models. The simulation properties closely follow a real dataset, in order to ensure that they mimic reality. Second, a statistical hierarchical Bayesian fusion model is proposed, in order to estimate a consensus ground truth within a robust statistical framework. The model is validated using the simulation tool and compared to other fusion models, including STAPLE. Additionally, the model is applied to real datasets and the consensus ground truth estimates are compared across different fusion models. Third, a statistical hierarchical Bayesian performance model is proposed in order to estimate segmentation method specific accuracy, between- and within -reader variability. An extensive simulation study is performed to validate the model’s parameter estimation and coverage properties. Additionally, the model is fit to a real data source and performance estimates are summarized.
APA, Harvard, Vancouver, ISO, and other styles
4

Porter, Sarah Ann. "Land cover study in Iowa: analysis of classification methodology and its impact on scale, accuracy, and landscape metrics." Thesis, University of Iowa, 2011. https://ir.uiowa.edu/etd/1169.

Full text
Abstract:
For landscapes dominated by agriculture, land cover plays an important role in the balance between anthropogenic and natural forces. Therefore, the objective of this thesis is to describe two different methodologies that have been implemented to create high-resolution land cover classifications in a dominant agricultural landscape. First, an object-based segmentation approach will be presented, which was applied to historic, high resolution, panchromatic aerial photography. Second, a traditional per-pixel technique was applied to multi-temporal, multispectral, high resolution aerial photography, in combination with light detection and ranging (LIDAR) and independent component analysis (ICA). A critical analysis of each approach will be discussed in detail, as well as the ability of each methodology to generate landscape metrics that can accurately characterize the quality of the landscape. This will be done through the comparison of various landscape metrics derived from the different classifications approaches, with a goal of enhancing the literature concerning how these metrics vary across methodologies and across scales. This is a familiar problem encountered when analyzing land cover datasets over time, which are often at different scales or generated using different methodologies. The diversity of remotely sensed imagery, including varying spatial resolutions, landscapes, and extents, as well as the wide range of spatial metrics that can be created, has generated concern about the integrity of these metrics when used to make inferences about landscape quality. Finally, inferences will be made about land cover and land cover change dynamics for the state of Iowa based on insight gained throughout the process.
APA, Harvard, Vancouver, ISO, and other styles
5

Shrestha, Ujjwal. "Automatic Liver and Tumor Segmentation from CT Scan Images using Gabor Feature and Machine Learning Algorithms." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1522411364001198.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Burgada, Muñoz Santiago. "Improvement on the sales forecast accuracy for a fast growing company by the best combination of historical data usage and clients segmentation." reponame:Repositório Institucional do FGV, 2014. http://hdl.handle.net/10438/13322.

Full text
Abstract:
Submitted by SANTIAGO BURGADA (sburgada@maxam.net) on 2015-01-25T12:10:08Z No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5)
Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-02-04T19:27:15Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5)
Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-02-11T13:27:32Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5)
Made available in DSpace on 2015-02-11T13:34:18Z (GMT). No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) Previous issue date: 2014-10-29
Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.
APA, Harvard, Vancouver, ISO, and other styles
7

Hast, Isak, and Asmelash Mehari. "Automating Geographic Object-Based Image Analysis and Assessing the Methods Transferability : A Case Study Using High Resolution Geografiska SverigedataTM Orthophotos." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-22570.

Full text
Abstract:
Geographic object-based image analysis (GEOBIA) is an innovative image classification technique that treats spatial features in an image as objects, rather than as pixels; thus resembling closer to that of human perception of the geographic space. However, the process of a GEOBIA application allows for multiple interpretations. Particularly sensitive parts of the process include image segmentation and training data selection. The multiresolution segmentation algorithm (MSA) is commonly applied. The performance of segmentation depends primarily on the algorithms scale parameter, since scale controls the size of image objects produced. The fact that the scale parameter is unit less makes it a challenge to select a suitable one; thus, leaving the analyst to a method of trial and error. This can lead to a possible bias. Additionally, part from the segmentation, training area selection usually means that the data has to be manually collected. This is not only time consuming but also prone to subjectivity. In order to overcome these challenges, we tested a GEOBIA scheme that involved automatic methods of MSA scale parameterisation and training area selection which enabled us to more objectively classify images. Three study areas within Sweden were selected. The data used was high resolution Geografiska Sverigedata (GSD) orthophotos from the Swedish mapping agency, Lantmäteriet. We objectively found scale for each classification using a previously published technique embedded as a tool in eCognition software. Based on the orthophoto inputs, the tool calculated local variance and rate of change at different scales. These figures helped us to determine scale value for the MSA segmentation. Moreover, we developed in this study a novel method for automatic training area selection. The method is based on thresholded feature statistics layers computed from the orthophoto band derivatives. Thresholds were detected by Otsu’s single and multilevel algorithms. The layers were run through a filtering process which left only those fit for use in the classification process. We also tested the transferability of classification rule-sets for two of the study areas. This test helped us to investigate the degree to which automation can be realised. In this study we have made progress toward a more objective way of object-based image classification, realised by automating the scheme. Particularly noteworthy is the algorithm for automatic training area selection proposed, which compared to manual selection restricts human intervention to a minimum. Results of the classification show overall well delineated classes, in particular, the border between open area and forest contributed by the elevation data. On the other hand, there still persists some challenges regarding separating between deciduous and coniferous forest. Furthermore, although water was accurately classified in most instances, in one of the study areas, the water class showed contradictory results between its thematic and positional accuracy; hence stressing the importance of assessing the result based on more than the thematic accuracy. From the transferability test we noted the importance of considering the spatial/spectral characteristics of an area before transferring of rule-sets as these factors are a key to determine whether a transfer is possible.
APA, Harvard, Vancouver, ISO, and other styles
8

Gauci, Marc-Olivier. "Description et classification 3D des glènes arthrosiques pour une planification préopératoire 3D assistée par ordinateur : l'épaule digitale normale et arthrosique Patient-specific glenoid guides provide accuracy and reproducibility in total shoulder arthroplasty, in The Bone & Joint Journal 98-B(8), 2016 A modification to the Walch classification of the glenoid in primary glenohumeral osteoarthritis using three-dimensional imaging, in Journal of Shoulder and Elbow Surgery 25(10), October 2016 Automated three-dimensional measurement of glenoid version and inclination in arthritic shoulders, in the Journal of Bone & Joint Surgery 100(1), January 2018 Proper benefit of a three dimensional pre-operative planning software for glenoid component positioning in total shoulder arthroplasty, in International Orthopaedics 42, 2018 The reverse shoulder arthroplasty angle: a new measurement of glenoid inclination for reverse shoulder arthroplasty, in Journal of Shoulder and Elbow Surgery 28(7), July 2019." Thesis, Brest, 2019. http://www.theses.fr/2019BRES0091.

Full text
Abstract:
La modélisation tridimensionnelle est devenue plus accessible et plus rapide en orthopédie et en particulier en chirurgie de l’épaule. L’analyse morphométrique qui en est issue est utilisée pour permettre une meilleure compréhension de l’omarthrose. L’objectif global de cette thèse était de valider l’application d’un logiciel de segmentation automatisée tridimensionnelle dans les étapes de prise en charge du patient. Huit études ont permis de valider les mesures automatiques calculées par le logiciel, d’améliorer la classification des omarthroses primaires puis de décrire la géométrie 3D normale et pathologique de l’épaule. Des seuils numériques précis ont pu être établis entre les différents types. Le logiciel a permis de développer et valider l’utilisation d’un angle (RSA-angle) permettant de mieux positionner l’implant glénoïdien dans les prothèses inversées d’épaule. L’utilisation des mobilités simulées en 3D démontrait l’intérêt du logiciel dans la compréhension des conflits osseux après prothèse et des faiblesses de design d’implant. Enfin, le positionnement de l’implant glénoïdien en peropératoire avec un guide patient-spécifique imprimé en 3D correspondait fidèlement à sa planification préopératoire, cependant, la planification à elle seule améliorait déjà considérablement ce positionnement. Ce travail de thèse a permis de valider les performances et l’utilisation d’un logiciel de segmentation tridimensionnel et de planification préopératoire. Son application se retrouve dans plusieurs étapes de la prise en charge d’un patient atteint d’omarthrose et devrait progressivement s’intégrer dans la pratique quotidienne des chirurgiens
Three-dimensional modelling has become more accessible and faster in orthopedics and especially in shoulder surgery. The subsequent morphometric analysis is used to provide a better understanding of shoulder arthritis.The overall objective of this Thesis was to validate the use of a 3D-automated segmentation software in the various steps of patients management.Eight studies allowed validating the automatic measurements calculated by the software, improving the classification of primary shoulder arthritis and then describing the normal and pathological 3D geometry of the shoulder. Accurate numerical thresholds could be established between the different types. The software developed and validated the use of an angle (RSAangle) to better position the glenoid implant in reverse shoulder arthroplasty. The use of simulated range of motion in 3D demonstrated the software’s interest in understanding bone impingements after prosthesis and implant design weaknesses.Finally, the positioning of the glenoid implant intraoperatively with a patient specific guide printed in 3D corresponded faithfully to its preoperative planning. However, planning alone already greatly improved this positioning. This Thesis made it possible to validate the performance and use of a software of three-dimensional segmentation and pre-operative planning. Its application is found in several steps of the management of a patient with shoulder arthritis and should gradually be integrated into the daily practice of surgeons
APA, Harvard, Vancouver, ISO, and other styles
9

Rajan, Rachel. "Semi Supervised Learning for Accurate Segmentation of Roughly Labeled Data." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1597082270750151.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hayrapetyan, Nare. "Adaptive Re-Segmentation Strategies For Accurate Bright Field Cell Tracking." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1230.

Full text
Abstract:
Understanding complex interactions in cellular systems requires accurate tracking of individual cells observed in microscopic image sequence and acquired from multi-day in vitro experiments. To be effective, methods must follow each cell through the whole experimental sequence to recognize significant phenotypic transitions, such as mitosis, chemotaxis, apoptosis, and cell/cell interactions, and to detect the effect of cell treatments. However, high accuracy long-range cell tracking is difficult because the collection and detection of cells in images is error-prone, and single error in a one frame can cause a tracked cell to be lost. Detection of cells is especially difficult when using bright field microscopy images wherein the contrast difference between the cells and the background is very low. This work introduces a new method that automatically identifies and then corrects tracking errors using a combination of combinatorial registration, flow constraints, and image segmentation repair.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Segmentation accuracy"

1

Kainmueller, Dagmar. Deformable Meshes for Medical Image Segmentation: Accurate Automatic Segmentation of Anatomical Structures. Springer Vieweg, 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kainmueller, Dagmar. Deformable Meshes for Medical Image Segmentation: Accurate Automatic Segmentation of Anatomical Structures. Springer Vieweg. in Springer Fachmedien Wiesbaden GmbH, 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kainmueller, Dagmar. Deformable Meshes for Medical Image Segmentation: Accurate Automatic Segmentation of Anatomical Structures. Springer Vieweg, 2014.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Shiffrar, Maggie. The Aperture Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0076.

Full text
Abstract:
The accurate visual perception of an object’s motion requires the simultaneous integration of motion information arising from that object along with the segmentation of motion information from other objects. When moving objects are seen through apertures, or viewing windows, the resultant illusions highlight some of the challenges that the visual system faces as it balances motion segmentation with motion integration. One example is the barber pole Illusion, in which lines appear to translate orthogonally to their true direction of emotion. Another is the illusory perception of incoherence when simple rectilinear objects translate or rotate behind disconnected apertures. Studies of these illusions suggest that visual motion processes frequently rely on simple form cues.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Segmentation accuracy"

1

Wang, Yaping, Hongjun Jia, Pew-Thian Yap, Bo Cheng, Chong-Yaw Wee, Lei Guo, and Dinggang Shen. "Groupwise Segmentation Improves Neuroimaging Classification Accuracy." In Multimodal Brain Image Analysis, 185–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33530-3_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liedlgruber, M., K. Butz, Y. Höller, G. Kuchukhidze, A. Taylor, A. Thomschewski, O. Tomasi, E. Trinka, and A. Uhl. "Pathology-Related Automated Hippocampus Segmentation Accuracy." In Informatik aktuell, 128–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54345-0_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Peskin, Adele P., Alden A. Dima, Joe Chalfoun, and John T. Elliott. "Predicting Segmentation Accuracy for Biological Cell Images." In Advances in Visual Computing, 549–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17289-2_53.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Mittal, Praveen, and Charul Bhatnagar. "Effectual Accuracy of Ophthalmological Image Retinal Layer Segmentation." In Advances in Intelligent Systems and Computing, 35–41. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4538-9_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Paetz, Friederike. "Improving the Forecasting Accuracy of 2-Step Segmentation Models." In Operations Research Proceedings 2016, 57–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55702-1_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gupta, Laxmi, Barbara M. Klinkhammer, Peter Boor, Dorit Merhof, and Michael Gadermayr. "GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy." In Lecture Notes in Computer Science, 631–39. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32239-7_70.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Cao, Rongyu, Hongwei Li, Ganbin Zhou, and Ping Luo. "Towards Document Panoptic Segmentation with Pinpoint Accuracy: Method and Evaluation." In Document Analysis and Recognition – ICDAR 2021, 3–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Rahmatullah, Bahbibi, Siti Tasnim Mahamud, Khairul Fikri Tamrin, and Suzani Mohd Samuri. "Boundary Accuracy of Interactive Segmentation Methods on Various Distorted Images." In Lecture Notes in Electrical Engineering, 632–38. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8129-5_96.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gibson, Eli, Yipeng Hu, Nooshin Ghavami, Hashim U. Ahmed, Caroline Moore, Mark Emberton, Henkjan J. Huisman, and Dean C. Barratt. "Inter-site Variability in Prostate Segmentation Accuracy Using Deep Learning." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 506–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00937-3_58.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Roohani, Yusuf H., and Eric G. Kiss. "Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability." In Computational Pathology and Ophthalmic Medical Image Analysis, 3–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00949-6_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Segmentation accuracy"

1

Santen, Jan P. H. van, and Richard W. Sproat. "High-accuracy automatic segmentation." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA: ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-620.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Aljahdali, Sultan, and E. A. Zanaty. "Combining multiple segmentation methods for improving the segmentation accuracy." In 2008 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2008. http://dx.doi.org/10.1109/iscc.2008.4625766.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tizhoosh, H. R., and A. A. Othman. "Anatomy-aware measurement of segmentation accuracy." In SPIE Medical Imaging, edited by Martin A. Styner and Elsa D. Angelini. SPIE, 2016. http://dx.doi.org/10.1117/12.2214869.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Yujin, and Jan J. Gerbrands. "Segmentation evaluation using ultimate measurement accuracy." In EI 92, edited by James R. Sullivan, Benjamin M. Dawson, and Majid Rabbani. SPIE, 1992. http://dx.doi.org/10.1117/12.58350.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Guo, Ruohao, Liao Qu, Dantong Niu, Zhenbo Li, and Jun Yue. "LeafMask: Towards Greater Accuracy on Leaf Segmentation." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00145.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Nan, Hong Huo, and Tao Fang. "Integrating segmentation and classification accuracy for accuracy assessment in object-based image analysis." In 2012 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2012. http://dx.doi.org/10.1109/icalip.2012.6376687.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kiaei, Pantea, Mojan Javaheripi, and Hoda Mohammadzade. "High Accuracy Farsi Language Character Segmentation and Recognition." In 2019 27th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2019. http://dx.doi.org/10.1109/iraniancee.2019.8786480.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Kim, Sujong, Yunsung Han, Soobin Jeon, and Dongmhan Seo. "Improvement of Object Segmentation Accuracy in Aerial Images." In 2022 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2022. http://dx.doi.org/10.1109/icce53296.2022.9730543.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Alqahtani, Hussain, Naif Alqahtani, Ryan T. Armstrong, and Peyman Mostaghimi. "Segmentation of X-Ray Images of Rocks Using Supervoxels Over-Segmentation." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22131-ms.

Full text
Abstract:
Abstract Digital core analysis has gained the interest of many scientific communities because of its impact on our understanding of flow in porous media. A typical workflow in digital core analysis includes scanning, reconstruction, denoising, segmentation, and modeling. Image analysis and modeling highly depend on the quality of the segmentation step. In this regard, conventional image segmentation methods often require user input/interference. This results in user bias and may produce a range of possible segmentation outcomes. To address this, we propose an unsupervised machine learning framework that offers multiple functionalities including improved mineral and micro-porosity identification. Superpixel (2D) and (3D) work by over-segmenting greyscale images using a family of over-segmentation algorithms. Simple Linear Iterative Clustering (SLIC) is one of these algorithms that is recognized for its speed and memory efficiency. The proposed framework utilizes SLIC and unsupervised clustering methods for segmenting greyscale images. SLIC divides the 2D and 3D images into segments having pixels (or voxels) with similar features (i.e., intensity range). Statistical features of each segment are computed and used for identifying the segment label through unsupervised clustering techniques. The unsupervised voting clustering implements a majority voting policy from multiple clustering algorithms including Hierarchical clustering and k-means clustering. A North Sea sandstone 2D X-ray image along with its SEM image were used to validate this framework. Different metrics were used to measure the accuracy of the X-ray segmentation with SEM segmentation. Our results show a mean Jaccard index of 70% and a mean Dice index of 81%. The same workflow is applied using supervoxels on a high-resolution 3D Indiana Limestone image and the results show similar accuracy margins compared to watershed segmentation. Comparison with other segmentation methods shows an average Jaccard score of 74% and an average Dice index score of 83%. To the best of our knowledge, this is the first application of superpixels over-segmentation algorithms in semantic segmentation of X-ray micro-CT images of porous media. The findings of this study highlighted the advantage of these algorithms in detecting sub-resolution porosity regions in greyscale images and obtaining accurate multi-label segmentation.
APA, Harvard, Vancouver, ISO, and other styles
10

Ruxin Zhang, Qiuyan Li, Wanggen Wan, and Feng Qian. "Reserach of a new segmentation algorithm with high accuracy." In IET International Communication Conference on Wireless Mobile & Computing (CCWMC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.2015.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Segmentation accuracy"

1

Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

Full text
Abstract:
The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Segmentation accuracy' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography