Relevant bibliographies by topics / Automated Segmentation Method

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  2. Dissertations / Theses
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Academic literature on the topic 'Automated Segmentation Method'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Automated Segmentation Method"

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Harkey, Matthew S., Nicholas Michel, Christopher Kuenze, Ryan Fajardo, Matt Salzler, Jeffrey B. Driban, and Ilker Hacihaliloglu. "Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images." CARTILAGE 13, no. 2 (April 2022): 194760352210930. http://dx.doi.org/10.1177/19476035221093069.

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Objective To validate a semi-automated technique to segment ultrasound-assessed femoral cartilage without compromising segmentation accuracy to a traditional manual segmentation technique in participants with an anterior cruciate ligament injury (ACL). Design We recruited 27 participants with a primary unilateral ACL injury at a pre-operative clinic visit. One investigator performed a transverse suprapatellar ultrasound scan with the participant’s ACL injured knee in maximum flexion. Three femoral cartilage ultrasound images were recorded. A single expert reader manually segmented the femoral cartilage cross-sectional area in each image. In addition, we created a semi-automatic program to segment the cartilage using a random walker-based method. We quantified the average cartilage thickness and echo-intensity for the manual and semi-automated segmentations. Intraclass correlation coefficients (ICC2,k) and Bland-Altman plots were used to validate the semi-automated technique to the manual segmentation for assessing average cartilage thickness and echo-intensity. A dice correlation coefficient was used to quantify the overlap between the segmentations created with the semi-automated and manual techniques. Results For average cartilage thickness, there was excellent reliability (ICC2,k = 0.99) and a small mean difference (+0.8%) between the manual and semi-automated segmentations. For average echo-intensity, there was excellent reliability (ICC2,k = 0.97) and a small mean difference (−2.5%) between the manual and semi-automated segmentations. The average dice correlation coefficient between the manual segmentation and semi-automated segmentation was 0.90, indicating high overlap between techniques. Conclusions Our novel semi-automated segmentation technique is a valid method that requires less technical expertise and time than manual segmentation in patients after ACL injury.
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Wang, Yang, Yihao Chen, Hao Yuan, and Cheng Wu. "An automated learning method of semantic segmentation for train autonomous driving environment understanding." International Journal of Advances in Intelligent Informatics 10, no. 1 (February 29, 2024): 148. http://dx.doi.org/10.26555/ijain.v10i1.1521.

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One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As one of the most fundamental and challenging problems in autonomous driving, environment understanding has been widely studied. It directly determines whether the entire in-vehicle system can effectively identify surrounding objects of vehicles and make correct path planning. Semantic segmentation is the most important means of environment understanding among the many image recognition algorithms used in autonomous driving. However, the success of semantic segmentation models is highly dependent on human expertise in data preparation and hyperparameter optimization, and the tedious process of training is repeated over and over for each new scene. Automated machine learning (AutoML) is a research area for this problem that aims to automate the development of end-to-end ML models. In this paper, we propose an automatic learning method for semantic segmentation based on reinforcement learning (RL), which can realize automatic selection of training data and guide automatic training of semantic segmentation. The results show that our scheme converges faster and has higher accuracy than researchers manually training semantic segmentation models, while requiring no human involvement.
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Kemnitz, Jana, Christian F. Baumgartner, Felix Eckstein, Akshay Chaudhari, Anja Ruhdorfer, Wolfgang Wirth, Sebastian K. Eder, and Ender Konukoglu. "Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain." Magnetic Resonance Materials in Physics, Biology and Medicine 33, no. 4 (December 23, 2019): 483–93. http://dx.doi.org/10.1007/s10334-019-00816-5.

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Abstract Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (− 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (− 5.6 ± 7.6%, p < 0.001, effect size: 0.73). Discussion Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.
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Buser, Myrthe A. D., Alida F. W. van der Steeg, Marc H. W. A. Wijnen, Matthijs Fitski, Harm van Tinteren, Marry M. van den Heuvel-Eibrink, Annemieke S. Littooij, and Bas H. M. van der Velden. "Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients." Cancers 15, no. 7 (April 1, 2023): 2115. http://dx.doi.org/10.3390/cancers15072115.

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Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these segmentations can be automated using deep learning. We included the MRI images of 45 Wilms tumor patients (age 0–18 years). First, we compared radiological tumor volumes with manual segmentation-based tumor volume measurements. Next, we created an automated segmentation method by training a nnU-Net in a five-fold cross-validation. Segmentation quality was validated by comparing the automated segmentation with the manually created ground truth segmentations, using Dice scores and the 95th percentile of the Hausdorff distances (HD95). On average, manual tumor segmentations result in larger tumor volumes. For automated segmentation, the median dice was 0.90. The median HD95 was 7.2 mm. We showed that radiological volume measurements underestimated tumor volume by about 10% when compared to manual segmentation-based volume measurements. Deep learning can potentially be used to replace manual segmentation to benefit from accurate volume measurements without time and observer constraints.
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Matin-Mann, Farnaz, Ziwen Gao, Chunjiang Wei, Felix Repp, Eralp-Niyazi Artukarslan, Samuel John, Dorian Alcacer Labrador, Thomas Lenarz, and Verena Scheper. "Development and In-Silico and Ex-Vivo Validation of a Software for a Semi-Automated Segmentation of the Round Window Niche to Design a Patient Specific Implant to Treat Inner Ear Disorders." Journal of Imaging 9, no. 2 (February 20, 2023): 51. http://dx.doi.org/10.3390/jimaging9020051.

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The aim of this study was to develop and validate a semi-automated segmentation approach that identifies the round window niche (RWN) and round window membrane (RWM) for use in the development of patient individualized round window niche implants (RNI) to treat inner ear disorders. Twenty cone beam computed tomography (CBCT) datasets of unilateral temporal bones of patients were included in the study. Defined anatomical landmarks such as the RWM were used to develop a customized 3D Slicer™ plugin for semi-automated segmentation of the RWN. Two otolaryngologists (User 1 and User 2) segmented the datasets manually and semi-automatically using the developed software. Both methods were compared in-silico regarding the resulting RWM area and RWN volume. Finally, the developed software was validated ex-vivo in N = 3 body donor implantation tests with additively manufactured RNI. The independently segmented temporal bones of the different Users showed a strong consistency in the volume of the RWN and the area of the RWM. The volume of the semi-automated RWN segmentations were 48 ± 11% smaller on average than the manual segmentations and the area of the RWM of the semi-automated segmentations was 21 ± 17% smaller on average than the manual segmentation. All additively manufactured implants, based on the semi-automated segmentation method could be implanted successfully in a pressure-tight fit into the RWN. The implants based on the manual segmentations failed to fit into the RWN and this suggests that the larger manual segmentations were over-segmentations. This study presents a semi-automated approach for segmenting the RWN and RWM in temporal bone CBCT scans that is efficient, fast, accurate, and not dependent on trained users. In addition, the manual segmentation, often positioned as the gold-standard, actually failed to pass the implantation validation.
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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, Gabriel A. Nketiah, Olmo Zavala-Romero, Radka Stoyanova, Tone F. Bathen, and Mattijs Elschot. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI." Diagnostics 10, no. 9 (September 18, 2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.

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Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.
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Clark, A. E., B. Biffi, R. Sivera, A. Dall'Asta, L. Fessey, T. L. Wong, G. Paramasivam, D. Dunaway, S. Schievano, and C. C. Lees. "Developing and testing an algorithm for automatic segmentation of the fetal face from three-dimensional ultrasound images." Royal Society Open Science 7, no. 11 (November 2020): 201342. http://dx.doi.org/10.1098/rsos.201342.

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Fetal craniofacial abnormalities are challenging to detect and diagnose on prenatal ultrasound (US). Image segmentation and computer analysis of three-dimensional US volumes of the fetal face may provide an objective measure to quantify fetal facial features and identify abnormalities. We have developed and tested an atlas-based partially automated facial segmentation algorithm; however, the volumes require additional manual segmentation (MS), which is time and labour intensive and may preclude this method from clinical adoption. These manually refined segmentations can then be used as a reference (atlas) by the partially automated segmentation algorithm to improve algorithmic performance with the aim of eliminating the need for manual refinement and developing a fully automated system. This study assesses the inter- and intra-operator variability of MS and tests an optimized version of our automatic segmentation (AS) algorithm. The manual refinements of 15 fetal faces performed by three operators and repeated by one operator were assessed by Dice score, average symmetrical surface distance and volume difference. The performance of the partially automatic algorithm with difference size atlases was evaluated by Dice score and computational time. Assessment of the manual refinements showed low inter- and intra-operator variability demonstrating its suitability for optimizing the AS algorithm. The algorithm showed improved performance following an increase in the atlas size in turn reducing the need for manual refinement.
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Nguyen, Philon, Thanh An Nguyen, and Yong Zeng. "Segmentation of design protocol using EEG." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 33, no. 1 (April 3, 2018): 11–23. http://dx.doi.org/10.1017/s0890060417000622.

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AbstractDesign protocol data analysis methods form a well-known set of techniques used by design researchers to further understand the conceptual design process. Verbal protocols are a popular technique used to analyze design activities. However, verbal protocols are known to have some limitations. A recurring problem in design protocol analysis is to segment and code protocol data into logical and semantic units. This is usually a manual step and little work has been done on fully automated segmentation techniques. Physiological signals such as electroencephalograms (EEG) can provide assistance in solving this problem. Such problems are typical inverse problems that occur in the line of research. A thought process needs to be reconstructed from its output, an EEG signal. We propose an EEG-based method for design protocol coding and segmentation. We provide experimental validation of our methods and compare manual segmentation by domain experts to algorithmic segmentation using EEG. The best performing automated segmentation method (when manual segmentation is the baseline) is found to have an average deviation from manual segmentations of 2 s. Furthermore, EEG-based segmentation can identify cognitive structures that simple observation of design protocols cannot. EEG-based segmentation does not replace complex domain expert segmentation but rather complements it. Techniques such as verbal protocols are known to fail in some circumstances. EEG-based segmentation has the added feature that it is fully automated and can be readily integrated in engineering systems and subsystems. It is effectively a window into the mind.
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Nishiyama, Daisuke, Hiroshi Iwasaki, Takaya Taniguchi, Daisuke Fukui, Manabu Yamanaka, Teiji Harada, and Hiroshi Yamada. "Deep generative models for automated muscle segmentation in computed tomography scanning." PLOS ONE 16, no. 9 (September 10, 2021): e0257371. http://dx.doi.org/10.1371/journal.pone.0257371.

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Accurate gluteus medius (GMd) volume evaluation may aid in the analysis of muscular atrophy states and help gain an improved understanding of patient recovery via rehabilitation. However, the segmentation of muscle regions in GMd images for cubic muscle volume assessment is time-consuming and labor-intensive. This study automated GMd-region segmentation from the computed tomography (CT) images of patients diagnosed with hip osteoarthritis using deep learning and evaluated the segmentation accuracy. To this end, 5250 augmented pairs of training data were obtained from five participants, and a conditional generative adversarial network was used to identify the relationships between the image pairs. Using the preserved test datasets, the results of automatic segmentation with the trained deep learning model were compared to those of manual segmentation in terms of the dice similarity coefficient (DSC), volume similarity (VS), and shape similarity (MS). As observed, the average DSC values for automatic and manual segmentations were 0.748 and 0.812, respectively, with a significant difference (p < 0.0001); the average VS values were 0.247 and 0.203, respectively, with no significant difference (p = 0.069); and the average MS values were 1.394 and 1.156, respectively, with no significant difference (p = 0.308). The GMd volumes obtained by automatic and manual segmentation were 246.2 cm3 and 282.9 cm3, respectively. The noninferiority of the DSC obtained by automatic segmentation was verified against that obtained by manual segmentation. Accordingly, the proposed GAN-based automatic GMd-segmentation technique is confirmed to be noninferior to manual segmentation. Therefore, the findings of this research confirm that the proposed method not only reduces time and effort but also facilitates accurate assessment of the cubic muscle volume.
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G, Mohandass, Hari Krishnan G, and Hemalatha R J. "An approach to automated retinal layer segmentation in SDOCT images." International Journal of Engineering & Technology 7, no. 2.25 (May 3, 2018): 56. http://dx.doi.org/10.14419/ijet.v7i2.25.12371.

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The optical coherence tomography (OCT) imaging technique is a precise and well-known approach to the diagnosis of retinal layers. The pathological changes in the retina challenge the accuracy of computational segmentation approaches in the evaluation and identification of defects in the boundary layer. The layer segmentations and boundary detections are distorted by noise in the computation. In this work, we propose a fully automated segmentation algorithm using a denoising technique called the Boisterous Obscure Ratio (BOR) for human and mammal retina. First, the BOR is derived using noise detection, i.e., from the Robust Outlyingness Ratio (ROR). It is then applied to edge and layer detection using a gradient-based deformable contour model. Second, the image is vectorised. In this method, a cluster and column intensity grid is applied to identify and determine the unsegmented layers. Using the layer intensity and a region growth seed point algorithm, segmentation of the prominent layers is achieved. The automatic BOR method is an image segmentation process that determines the eight layers in retinal spectral domain optical coherence tomography images. The highlight of the BOR method is that the results produced are accurate, highly substantial, and effective, although time consuming.
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Dissertations / Theses on the topic "Automated Segmentation Method"

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Tran, Philippe. "Segmentation and characterization of cerebral white matter hyperintensities : application in individuals with multiple sclerosis and age-related pathologies." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS243.pdf.

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Les hyperintensités de la substance blanche (HSB) sont de plus en plus prises en compte dans le suivi clinique des personnes âgées et/ou des patients atteints de démences, et sont cruciales chez les patients atteints de Sclérose en Plaques (SEP). Des méthodes d'analyse ont été proposées pour aider à quantifier ces lésions à grande échelle, afin de mieux comprendre les mécanismes sous-jacents de ces pathologies. Cependant, à notre connaissance, il n'y a pas de consensus aujourd'hui sur la méthode à utiliser et aucune méthode n'est validée sur ces deux types de sujets. Cette thèse présente plusieurs outils et leurs validations dans le but de mieux caractériser les HSB. Tout d'abord, WHASA-3D (Tran et al., 2022) est une nouvelle méthode de segmentation automatique des HSB adaptée pour les données 3D T2-FLAIR et aux patients SEP dans un cadre multicentrique. C'est une amélioration majeure de WHASA (Samaille et al. 2012). Les performances de WHASA-3D sont comparées ici avec six méthodes de la littérature avec leurs paramètres par défaut et optimisés lorsque cela est possible. Deux extensions ont alors été développées dans le but d'apporter une aide lors du diagnostic et du suivi clinique des patients. WHASA-Spatial est une extension pour caractériser spatialement les HSB fournies par WHASA-3D selon quatre classes (périventriculaire, infratentorielle, juxtacorticales/corticales, profondes). La classification a été évaluée visuellement sur 104 sujets SEP et a montré des résultats très satisfaisants. Enfin, WHASA-Longitudinal, est une extension permettant de segmenter automatiquement les nouvelles lésions ou lésions élargies entre deux acquisitions successives. Les performances de cette méthode se sont révélées satisfaisantes au niveau de la volumétrie et une piste est proposée pour améliorer le comptage de lésions. Ces résultats doivent être confirmés sur une plus grande base de sujets
White matter hyperintensities (WMH) are more and more taken into account in the clinical monitoring of elderly subjects and/or dementia patients, and are crucial in patients with Multiple Sclerosis (MS). Automated methods have been proposed to better quantify these lesions on a large scale, in order to better understand the underlying mechanisms of these pathologies. However, to our knowledge, no automated method has reached consensus today for the segmentation of WMH, and no method has been validated on these two types of subjects. This thesis introduces several tools and their validations in order to better characterize WMH. First of all, WHASA-3D (Tran et al. 2022) is a new automated method for WMH segmentation adapted for 3D T2-FLAIR data and MS patients in a multicenter setting. It is a major improvement of WHASA (Samaille et al. 2012). WHASA-3D's performances are here compared with six state-of-the-art methods with their default parameters and optimized settings, when possible. Two extensions have then been developped to support the clinician for patient diagnosis and clinical monitoring. WHASA-Spatial is an extension for the automatic spatial characterization of WMH provided by WHASA-3D according to four classes (periventricular, infratentorial, juxtacortical/cortical, deep). The visual assessment on 104 MS subjects showed that the global classification was very satisfactory. Finally, WHASA-Longitudinal, is an extension that allows the automatic segmentation of new or enlarged lesions between two successive acquisitions. The performance of this method was satisfactory for volume agreement and a solution is proposed and needs to be investigated to improve new lesion count. These results need to be confirmed on a larger number of subjects
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Shan, Juan. "A Fully Automatic Segmentation Method for Breast Ultrasound Images." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/905.

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Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task. This research focuses on developing a novel, effective, and fully automatic lesion segmentation method for breast ultrasound images. By incorporating empirical domain knowledge of breast structure, a region of interest is generated. Then, a novel enhancement algorithm (using a novel phase feature) and a newly developed neutrosophic clustering method are developed to detect the precise lesion boundary. Neutrosophy is a recently introduced branch of philosophy that deals with paradoxes, contradictions, antitheses, and antinomies. When neutrosophy is used to segment images with vague boundaries, its unique ability to deal with uncertainty is brought to bear. In this work, we apply neutrosophy to breast ultrasound image segmentation and propose a new clustering method named neutrosophic l-means. We compare the proposed method with traditional fuzzy c-means clustering and three other well-developed segmentation methods for breast ultrasound images, using the same database. Both accuracy and time complexity are analyzed. The proposed method achieves the best accuracy (TP rate is 94.36%, FP rate is 8.08%, and similarity rate is 87.39%) with a fairly rapid processing speed (about 20 seconds). Sensitivity analysis shows the robustness of the proposed method as well. Cases with multiple-lesions and severe shadowing effect (shadow areas having similar intensity values of the lesion and tightly connected with the lesion) are not included in this study.
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Vestergren, Sara, and Navid Zandpour. "Automatic Image Segmentation for Hair Masking: two Methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254258.

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We propose two different methods for image segmentation with the objective of marking contaminated regions in images from biochemical tests. The contaminated regions consists of thin hair or fibers and the purpose of this thesis is to eliminate the tedious task of masking the contaminated regions by hand by implementing automatic hair masking. Initially an algorithm based on Morphological Image Processing is presented, followed by solving the problem of pixelwise classification using a Convolutional Neural Network (CNN). Finally, the performance of each implementation is measured by comparing the segmented images with labelled images which are considered to be the ground truth. The result shows that both implementations have strong potential at successfully performing semantic segmentation on the images from the biochemical tests.
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Benhabiles, Halim. "3D-mesh segmentation : automatic evaluation and a new learning-based method." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00834344.

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Dans cette thèse, nous abordons deux problèmes principaux, à savoir l'évaluation quantitative des algorithmes de segmentation de maillages ainsi que la segmentation de maillages par apprentissage en exploitant le facteur humain. Nous proposons les contributions suivantes : - Un benchmark dédié à l'évaluation des algorithmes de segmentation de maillages 3D. Le benchmark inclut un corpus de segmentations vérités-terrains réalisées par des volontaires ainsi qu'une nouvelle métrique de similarité pertinente qui quantifie la cohérence entre ces segmentations vérités-terrains et celles produites automatique- ment par un algorithme donné sur les mêmes modèles. De plus, nous menons un ensemble d'expérimentations, y compris une expérimentation subjective, pour respectivement démontrer et valider la pertinence de notre benchmark. - Un algorithme de segmentation par apprentissage. Pour cela, l'apprentissage d'une fonction d'arête frontière est effectué, en utilisant plusieurs critères géométriques, à partir d'un ensemble de segmentations vérités-terrains. Cette fonction est ensuite utilisée, à travers une chaîne de traitement, pour segmenter un nouveau maillage 3D. Nous montrons, à travers une série d'expérimentations s'appuyant sur différents benchmarks, les excellentes performances de notre algorithme par rapport à ceux de l'état de l'art. Nous présentons également une application de notre algorithme de segmentation pour l'extraction de squelettes cinématiques pour les maillages 3D dynamiques.
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Sun, Felice (Felice Tzu-yun) 1976. "Integrating statistical and knowledge-based methods for automatic phonemic segmentation." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80127.

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Reavy, Richard Wilson. "Image segmentation for automatic target recognition : an investigation of a method of applying post-segmentation derived information to a secondary segmentation process." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/12840.

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A fundamental automatic target recognition (ATR) system can be composed of an object segmentation stage, followed by feature extraction from those objects produced by segmentation, and finally classification of these object features. The capability of such a system in terms of classification success is therefore limited not only by the quality of the feature extraction and classification methods used, but also by the quality of the initial object segmentation. In this thesis, a novel architecture is described which uses two stages of segmentation. This allows image features derived after a primary segmentation stage to influence the parameters of a secondary segmentation stage which is applied to the same image area. This is aimed at allowing improved, and locally optimised, segmentation of those objects which were poorly segmented by the primary segmentation stage. To enable the implementation of the system, a probability density estimate function is used as a method of detecting novelty in objects presented for classification. This is found to be a non-ideal solution, although useful in the context of the application concerned. The development of all the system components, and ultimately the full ATR system, is described with experimental results derived from real-world infrared imagery. From this work, conclusions are drawn as to the usefulness of a such a two-stage segmentation architecture; specifically, the clutter rejection flexibility and the potential ability for the system to locally optimise segmentation on a per object basis are highlighted.
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Li, Xiaolong. "Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1265412807.

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Arif, Omar. "Robust target localization and segmentation using statistical methods." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33882.

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This thesis aims to contribute to the area of visual tracking, which is the process of identifying an object of interest through a sequence of successive images. The thesis explores kernel-based statistical methods, which map the data to a higher dimensional space. A pre-image framework is provided to find the mapping from the embedding space to the input space for several manifold learning and dimensional learning algorithms. Two algorithms are developed for visual tracking that are robust to noise and occlusions. In the first algorithm, a kernel PCA-based eigenspace representation is used. The de-noising and clustering capabilities of the kernel PCA procedure lead to a robust algorithm. This framework is extended to incorporate the background information in an energy based formulation, which is minimized using graph cut and to track multiple objects using a single learned model. In the second method, a robust density comparison framework is developed that is applied to visual tracking, where an object is tracked by minimizing the distance between a model distribution and given candidate distributions. The superior performance of kernel-based algorithms comes at a price of increased storage and computational requirements. A novel method is developed that takes advantage of the universal approximation capabilities of generalized radial basis function neural networks to reduce the computational and storage requirements for kernel-based methods.
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Kolesov, Ivan A. "Statistical methods for coupling expert knowledge and automatic image segmentation and registration." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47739.

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The objective of the proposed research is to develop methods that couple an expert user's guidance with automatic image segmentation and registration algorithms. Often, complex processes such as fire, anatomical changes/variations in human bodies, or unpredictable human behavior produce the target images; in these cases, creating a model that precisely describes the process is not feasible. A common solution is to make simplifying assumptions when performing detection, segmentation, or registration tasks automatically. However, when these assumptions are not satisfied, the results are unsatisfactory. Hence, removing these, often times stringent, assumptions at the cost of minimal user input is considered an acceptable trade-off. Three milestones towards reaching this goal have been achieved. First, an interactive image segmentation approach was created in which the user is coupled in a closed-loop control system with a level set segmentation algorithm. The user's expert knowledge is combined with the speed of automatic segmentation. Second, a stochastic point set registration algorithm is presented. The point sets can be derived from simple user input (e.g. a thresholding operation), and time consuming correspondence labeling is not required. Furthermore, common smoothness assumptions on the non-rigid deformation field are removed. Third, a stochastic image registration algorithm is designed to capture large misalignments. For future research, several improvements of the registration are proposed, and an iterative, landmark based segmentation approach, which couples the segmentation and registration, is envisioned.
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McCormick, Neil Howie. "Bayesian methods for automatic segmentation and classification of SLO and SONAR data." Thesis, Heriot-Watt University, 2001. http://hdl.handle.net/10399/452.

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Books on the topic "Automated Segmentation Method"

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Behrooz, Ali. Systems and Methods for Automated Segmentation of Individual Skeletal Bones in 3D Anatomical Images: United States Patent 9999400. Independently Published, 2020.

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Book chapters on the topic "Automated Segmentation Method"

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Li, Zhihui, Fenggang Huang, and Yongmei Liu. "A Method of Motion Segmentation Based on Region Shrinking." In Intelligent Data Engineering and Automated Learning – IDEAL 2006, 275–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_33.

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Tsagaan, Baigalmaa, Akinobu Shimizu, Hidefumi Kobatake, and Kunihisa Miyakawa. "An Automated Segmentation Method of Kidney Using Statistical Information." In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, 556–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45786-0_69.

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Chan, Robin, Svenja Uhlemeyer, Matthias Rottmann, and Hanno Gottschalk. "Detecting and Learning the Unknown in Semantic Segmentation." In Deep Neural Networks and Data for Automated Driving, 277–313. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_10.

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AbstractSemantic segmentation is a crucial component for perception in automated driving. Deep neural networks (DNNs) are commonly used for this task, and they are usually trained on a closed set of object classes appearing in a closed operational domain. However, this is in contrast to the open world assumption in automated driving that DNNs are deployed to. Therefore, DNNs necessarily face data that they have never encountered previously, also known as anomalies, which are extremely safety-critical to properly cope with. In this chapter, we first give an overview about anomalies from an information-theoretic perspective. Next, we review research in detecting unknown objects in semantic segmentation. We present a method outperforming recent approaches by training for high entropy responses on anomalous objects, which is in line with our theoretical findings. Finally, we propose a method to assess the occurrence frequency of anomalies in order to select anomaly types to include into a model’s set of semantic categories. We demonstrate that those anomalies can then be learned in an unsupervised fashion which is particularly suitable in online applications.
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Hashemi, Atiye Sadat, Andreas Bär, Saeed Mozaffari, and Tim Fingscheidt. "Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation." In Deep Neural Networks and Data for Automated Driving, 171–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_6.

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AbstractAlthough deep neural networks (DNNs) are high-performance methods for various complex tasks, e.g., environment perception in automated vehicles (AVs), they are vulnerable to adversarial perturbations. Recent works have proven the existence of universal adversarial perturbations (UAPs), which, when added to most images, destroy the output of the respective perception function. Existing attack methods often show a low success rate when attacking target models which are different from the one that the attack was optimized on. To address such weak transferability, we propose a novel learning criterion by combining a low-level feature loss, addressing the similarity of feature representations in the first layer of various model architectures, with a cross-entropy loss. Experimental results on ImageNet and Cityscapes datasets show that our method effectively generates universal adversarial perturbations achieving state-of-the-art fooling rates across different models, tasks, and datasets. Due to their effectiveness, we propose the use of such novel generated UAPs in robustness evaluation of DNN-based environment perception functions for AVs.
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Klingner, Marvin, and Tim Fingscheidt. "Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task." In Deep Neural Networks and Data for Automated Driving, 149–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_5.

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AbstractWhile deep neural networks for environment perception tasks in autonomous driving systems often achieve impressive performance on clean and well-prepared images, their robustness under real conditions, i.e., on images being perturbed with noise patterns or adversarial attacks, is often subject to a significantly decreased performance. In this chapter, we address this problem for the task of semantic segmentation by proposing multi-task training with the additional task of depth estimation with the goal to improve the DNN robustness. This method has a very wide potential applicability as the additional depth estimation task can be trained in a self-supervised fashion, relying only on unlabeled image sequences during training. The final trained segmentation DNN is, however, still applicable on a single-image basis during inference without additional computational overhead compared to the single-task model. Additionally, our evaluation introduces a measure which allows for a meaningful comparison between different noise and attack types. We show the effectiveness of our approach on the Cityscapes and KITTI datasets, where our method improves the DNN performance w.r.t. the single-task baseline in terms of robustness against multiple noise and adversarial attack types, which is supplemented by an improved absolute prediction performance of the resulting DNN.
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Popescu, Iulia A., Alessandra Borlotti, Erica Dall’Armellina, and Vicente Grau. "Automated LGE Myocardial Scar Segmentation Using MaskSLIC Supervoxels - Replicating the Clinical Method." In Communications in Computer and Information Science, 229–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60964-5_20.

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Schneider, Zofia, and Elżbieta Pociask. "Automated External Contour-Segmentation Method for Vertebrae in Lateral Cervical Spine Radiographs." In Advances in Intelligent Systems and Computing, 118–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88976-0_16.

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Zhan, Yiqiang, and Dinggang Shen. "Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method." In Lecture Notes in Computer Science, 688–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39899-8_84.

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Duda, Julia, Izabela Cywińska, and Elżbieta Pociask. "Fully Automated Lumen Segmentation Method and BVS Stent Struts Detection in OCT Images." In Communications in Computer and Information Science, 353–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19647-8_25.

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Duan, Lijuan, Xuan Feng, Jie Chen, and Fan Xu. "An Automated Method with Feature Pyramid Encoder and Dual-Path Decoder for Nuclei Segmentation." In Pattern Recognition and Computer Vision, 341–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_28.

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Conference papers on the topic "Automated Segmentation Method"

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Renner, Johan, Roland Gårdhagen, and Matts Karlsson. "Subject Specific In-Vivo CFD Estimated Aortic WSS: Comparison Between Manual and Automated Segmentation Methods." In ASME 2008 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2008. http://dx.doi.org/10.1115/sbc2008-192735.

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When making computational fluid dynamics (CFD) based estimations of wall shear stress (WSS) in the human aorta, medical image converting processes to 3D geometries are important as the result is strongly dependent on the quality of the geometry [1]. The image interpretation process or segmentation can be more or less automated; however in clinical work today the gold standard is to manually interpret the medical image information. This combined magnetic resonance imaging (MRI) and CFD method aims to estimate WSS in human arteries in-vivo as WSS is strongly linked to atherosclerosis [2]. More or less automated segmentation has been used in previous studies but normally based on a stack of 2D individually segmented slices which is combined into a 3D model [3]. The aim of this work is to compare manual 2D and automatic 3D segmentations.
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Khouaja, Sourour, Hajer Jlassi, and Kamel Hamrouni. "An automated method for breast mass segmentation." In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2014. http://dx.doi.org/10.1109/socpar.2014.7008002.

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Narote, Sandipan P., Abhilasha S. Narote, Laxman M. Waghmare, and Arun N. Gaikwad. "An Automated Segmentation Method For Iris Recognition." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344211.

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Huo, Zhimin, and Maryellen L. Giger. "Evaluation of an automated segmentation method based on performances of an automated classification method." In Medical Imaging 2000, edited by Elizabeth A. Krupinski. SPIE, 2000. http://dx.doi.org/10.1117/12.383111.

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Huang, Jida, and Tsz-Ho Kwok. "Comparing Segmentation Approaches for Learning-Aware Wireframe Generation on Human Model." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22616.

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Abstract Wireframe has been proved very useful for learning human body from semantic parameters. However, the definition of the wireframe is highly dependent on the anthropological experiences of experts in previous works. Hence it is usually not easy to obtain a well-defined wireframe for a new set of human models in the available database. To overcome such difficulty, an automated wireframe generation method would be very helpful in relieving the need for manual anthropometric definition. In order to find such an automated wireframe designing method, a natural way is using automatic segmentation methods to divide the human body model into small mesh patches. Nevertheless, different segmentation approaches could have various segmented patches, thus resulting in various wireframes. How these wireframes affect human body learning performance? In this paper, we attempt to answer this research question by comparing different segmentation methods. Different wireframes are generated with the mesh segmentation methods, and then we use these wireframes as an intermediate agent to learn the relationship between the human body mesh models and the semantic parameters. We compared the reconstruction accuracy with different generated wireframe sets and summarized several meaningful design guidelines for developing an automatic wireframe-aware segmentation method for human body learning.
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Kalka, Nathan, Nick Bartlow, and Bojan Cukic. "An automated method for predicting iris segmentation failures." In 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS). IEEE, 2009. http://dx.doi.org/10.1109/btas.2009.5339062.

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Chakraborty, Shouvik, Kalyani Mali, Sankhadeep Chatterjee, Soumen Banerjee, Kyamelia Roy, Kamelia Deb, Sayan Sarkar, and Neha Prasad. "An integrated method for automated biomedical image segmentation." In 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix). IEEE, 2017. http://dx.doi.org/10.1109/optronix.2017.8349978.

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Wenjun Tan, Jinzhu Yang, Dazhe Zhao, Shuang Ma, Li Qu, and Jinchi Wang. "A novel method for automated segmentation of airway tree." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244152.

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Rosidi, Rasyiqah Annani Mohd, Aida Syafiqah Ahmad Khaizi, Hong-Seng Gan, and Hafiz Basarudin. "Boundary correction in semi-automated segmentation using scribbling method." In 2017 International Conference on Engineering Technology and Technopreneurship (ICE2T). IEEE, 2017. http://dx.doi.org/10.1109/ice2t.2017.8215986.

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Paranjape, Amit S., Badr Elmaanaoui, Jordan Dewelle, H. Grady Rylander, and Thomas E. Milner. "Automated method for RNFL segmentation in spectral domain OCT." In Biomedical Optics (BiOS) 2008, edited by Tuan Vo-Dinh, Warren S. Grundfest, David A. Benaron, and Gerald E. Cohn. SPIE, 2008. http://dx.doi.org/10.1117/12.763491.

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Reports on the topic "Automated Segmentation Method"

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Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.

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Urban trees are a key component of the urban environment. In Sweden, ambitious goals have been expressed by authorities regarding the retention and increase of urban tree cover, aiming to mitigate climate change and provide a healthy, livable urban environment in a highly contested space. Tracking urban tree cover through remote sensing serves as an indicator of how past urban planning has succeeded in retaining trees as part of the urban fabric, and historical imagery spanning back decades for such analysis is widely available. This short study examines the viability of automated detection using open-source Deep Learning methods for long-term change detection in urban tree cover, aiming to evaluate past practices in urban planning. Results indicate that preprocessing of old imagery is necessary to enhance the detection and segmentation of urban tree cover, as the currently available training models were found to be severely lacking upon visual inspection.
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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.

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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.
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