Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

Science.gov (United States)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.

Kernel-based discriminant feature extraction using a representative dataset

Science.gov (United States)

Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

2002-07-01

Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Directory of Open Access Journals (Sweden)

Ya-Shuo Li

2012-03-01

Full Text Available Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.

Blood vessel-based liver segmentation through the portal phase of a CT dataset

Science.gov (United States)

Maklad, Ahmed S.; Matsuhiro, Mikio; Suzuki, Hidenobu; Kawata, Yoshiki; Niki, Noboru; Moriyama, Noriyuki; Utsunomiya, Toru; Shimada, Mitsuo

2013-02-01

Blood vessels are dispersed throughout the human body organs and carry unique information for each person. This information can be used to delineate organ boundaries. The proposed method relies on abdominal blood vessels (ABV) to segment the liver considering the potential presence of tumors through the portal phase of a CT dataset. ABV are extracted and classified into hepatic (HBV) and nonhepatic (non-HBV) with a small number of interactions. HBV and non-HBV are used to guide an automatic segmentation of the liver. HBV are used to individually segment the core region of the liver. This region and non-HBV are used to construct a boundary surface between the liver and other organs to separate them. The core region is classified based on extracted posterior distributions of its histogram into low intensity tumor (LIT) and non-LIT core regions. Non-LIT case includes normal part of liver, HBV, and high intensity tumors if exist. Each core region is extended based on its corresponding posterior distribution. Extension is completed when it reaches either a variation in intensity or the constructed boundary surface. The method was applied to 80 datasets (30 Medical Image Computing and Computer Assisted Intervention (MICCAI) and 50 non-MICCAI data) including 60 datasets with tumors. Our results for the MICCAI-test data were evaluated by sliver07 [1] with an overall score of 79.7, which ranks seventh best on the site (December 2013). This approach seems a promising method for extraction of liver volumetry of various shapes and sizes and low intensity hepatic tumors.

TU-CD-BRB-04: Automated Radiomic Features Complement the Prognostic Value of VASARI in the TCGA-GBM Dataset

Energy Technology Data Exchange (ETDEWEB)

Velazquez, E Rios [Dana-Farber Cancer Institute | Harvard Medical School, Boston, MA (United States); Narayan, V [Dana-Farber Cancer Institute, Brigham and Womens Hospital, Harvard Medic, Boston, MA (United States); Grossmann, P [Dana-Farber Cancer Institute/Harvard Medical School, Boston, MA (United States); Dunn, W; Gutman, D [Emory University School of Medicine, Atlanta, GA (United States); Aerts, H [Dana-Farber/Brigham Womens Cancer Center, Boston, MA (United States)

2015-06-15

Purpose: To compare the complementary prognostic value of automated Radiomic features to that of radiologist-annotated VASARI features in TCGA-GBM MRI dataset. Methods: For 96 GBM patients, pre-operative MRI images were obtained from The Cancer Imaging Archive. The abnormal tumor bulks were manually defined on post-contrast T1w images. The contrast-enhancing and necrotic regions were segmented using FAST. From these sub-volumes and the total abnormal tumor bulk, a set of Radiomic features quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the post-contrast T1w images. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative Radiomic, VASARI and combined Radiomic-VASARI features in 70% of the dataset (training-set). Multivariate Cox-proportional hazards models were evaluated in 30% of the dataset (validation-set) using the C-index for OS. A bootstrap procedure was used to assess significance while comparing the C-Indices of the different models. Results: Overall, the Radiomic features showed a moderate correlation with the radiologist-annotated VASARI features (r = −0.37 – 0.49); however that correlation was stronger for the Tumor Diameter and Proportion of Necrosis VASARI features (r = −0.71 – 0.69). After MRMR feature selection, the best-performing Radiomic, VASARI, and Radiomic-VASARI Cox-PH models showed a validation C-index of 0.56 (p = NS), 0.58 (p = NS) and 0.65 (p = 0.01), respectively. The combined Radiomic-VASARI model C-index was significantly higher than that obtained from either the Radiomic or VASARI model alone (p = <0.001). Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative annotated VASARI feature set. The prognostic value of informative qualitative VASARI features such as Eloquent Brain and Multifocality is increased with the addition of quantitative volumetric and textural features from the

Image segmentation-based robust feature extraction for color image watermarking

Science.gov (United States)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

Election Districts and Precincts, PrecinctPoly-The data set is a polygon feature consisting of 220 segments representing voter precinct boundaries., Published in 1991, Davis County Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Election Districts and Precincts dataset current as of 1991. PrecinctPoly-The data set is a polygon feature consisting of 220 segments representing voter precinct...

Upper airway segmentation and dimensions estimation from cone-beam CT image datasets

Energy Technology Data Exchange (ETDEWEB)

Shi, Hongjian; Scarfe, W.C. [Louisville Univ., KY (United States). School of Dentistry; Farman, A.G. [Louisville Univ., KY (United States). School of Dentistry; Louisville Univ., KY (United States). Div. of Radiology and Imaging Science

2006-11-15

Objective: To segment and measure the upper airway using cone-beam computed tomography (CBCT). This information may be useful as an imaging biomarker in the diagnostic assessment of patients with obstructive sleep apnea and in the planning of any necessary therapy. Methods: With Institutional Review Board Approval, anonymous CBCT datasets from subjects who had been imaged for a variety of conditions unrelated to the airway were evaluated. DICOM images were available. A segmentation algorithm was developed to separate the bounded upper airway and measurements were performed manually to determine the smallest cross-sectional area and the anteriorposterior distance of the retropalatal space (RP-SCA and RP-AP, respectively) and retroglossal space (RG-SCA and RG-AP, respectively). A segmentation algorithm was developed to separate the bounded upper airway and it was applied to determine RP-AP, RG-AP, the smallest transaxial-sectional area (TSCA) and largest sagittal view airway area (LCSA). A second algorithm was created to evaluate the airway volume within this bounded upper airway. Results: Measurements of the airway segmented automatically by the developed algorithm agreed with those obtained using manual segmentation. The corresponding volumes showed only very small differences considered clinically insignificant. Conclusion: Automatic segmentation of the airway imaged using CBCT is feasible and this method can be used to evaluate airway cross-section and volume comparable to measurements extracted using manual segmentation. (orig.)

A robust dataset-agnostic heart disease classifier from Phonocardiogram.

Science.gov (United States)

Banerjee, Rohan; Dutta Choudhury, Anirban; Deshpande, Parijat; Bhattacharya, Sakyajit; Pal, Arpan; Mandana, K M

2017-07-01

Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.

Feature-space transformation improves supervised segmentation across scanners

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Achterberg, Hakim C.; de Bruijne, Marleen

2015-01-01

Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions...

Objectness Supervised Merging Algorithm for Color Image Segmentation

Directory of Open Access Journals (Sweden)

Haifeng Sima

2016-01-01

Full Text Available Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

Image Mosaic Method Based on SIFT Features of Line Segment

Directory of Open Access Journals (Sweden)

Jun Zhu

2014-01-01

Full Text Available This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling.

Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

International Nuclear Information System (INIS)

Quanqing, Zhu.; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

2002-01-01

In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images

Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

Energy Technology Data Exchange (ETDEWEB)

Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

2011-01-01

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

Evaluating automatic laughter segmentation in meetings using acoustic and acoustic-phonetic features

NARCIS (Netherlands)

Truong, K.P.; Leeuwen, D.A. van

2007-01-01

In this study, we investigated automatic laughter segmentation in meetings. We first performed laughterspeech discrimination experiments with traditional spectral features and subsequently used acousticphonetic features. In segmentation, we used Gaussian Mixture Models that were trained with

Interaction features for prediction of perceptual segmentation

DEFF Research Database (Denmark)

Hartmann, Martin; Lartillot, Olivier; Toiviainen, Petri

2017-01-01

As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise...... and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners...... was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians...

Statistics-based segmentation using a continuous-scale naive Bayes approach

DEFF Research Database (Denmark)

Laursen, Morten Stigaard; Midtiby, Henrik Skov; Kruger, Norbert

2014-01-01

Segmentation is a popular preprocessing stage in the field of machine vision. In agricultural applications it can be used to distinguish between living plant material and soil in images. The normalized difference vegetation index (NDVI) and excess green (ExG) color features are often used...... segmentation over the normalized vegetation difference index and excess green. The inputs to this color feature are the R, G, B, and near-infrared color wells, their chromaticities, and NDVI, ExG, and excess red. We apply the developed technique to a dataset consisting of 20 manually segmented images captured...

News video story segmentation method using fusion of audio-visual features

Science.gov (United States)

Wen, Jun; Wu, Ling-da; Zeng, Pu; Luan, Xi-dao; Xie, Yu-xiang

2007-11-01

News story segmentation is an important aspect for news video analysis. This paper presents a method for news video story segmentation. Different form prior works, which base on visual features transform, the proposed technique uses audio features as baseline and fuses visual features with it to refine the results. At first, it selects silence clips as audio features candidate points, and selects shot boundaries and anchor shots as two kinds of visual features candidate points. Then this paper selects audio feature candidates as cues and develops different fusion method, which effectively using diverse type visual candidates to refine audio candidates, to get story boundaries. Experiment results show that this method has high efficiency and adaptability to different kinds of news video.

Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming

Science.gov (United States)

Chiu, Stephanie J.; Toth, Cynthia A.; Bowes Rickman, Catherine; Izatt, Joseph A.; Farsiu, Sina

2012-01-01

This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique. PMID:22567602

Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

Science.gov (United States)

Paul, Subir; Nagesh Kumar, D.

2018-04-01

Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers.

Science.gov (United States)

Prinyakupt, Jaroonrut; Pluempitiwiriyawej, Charnchai

2015-06-30

Blood smear microscopic images are routinely investigated by haematologists to diagnose most blood diseases. However, the task is quite tedious and time consuming. An automatic detection and classification of white blood cells within such images can accelerate the process tremendously. In this paper we propose a system to locate white blood cells within microscopic blood smear images, segment them into nucleus and cytoplasm regions, extract suitable features and finally, classify them into five types: basophil, eosinophil, neutrophil, lymphocyte and monocyte. Two sets of blood smear images were used in this study's experiments. Dataset 1, collected from Rangsit University, were normal peripheral blood slides under light microscope with 100× magnification; 555 images with 601 white blood cells were captured by a Nikon DS-Fi2 high-definition color camera and saved in JPG format of size 960 × 1,280 pixels at 15 pixels per 1 μm resolution. In dataset 2, 477 cropped white blood cell images were downloaded from CellaVision.com. They are in JPG format of size 360 × 363 pixels. The resolution is estimated to be 10 pixels per 1 μm. The proposed system comprises a pre-processing step, nucleus segmentation, cell segmentation, feature extraction, feature selection and classification. The main concept of the segmentation algorithm employed uses white blood cell's morphological properties and the calibrated size of a real cell relative to image resolution. The segmentation process combined thresholding, morphological operation and ellipse curve fitting. Consequently, several features were extracted from the segmented nucleus and cytoplasm regions. Prominent features were then chosen by a greedy search algorithm called sequential forward selection. Finally, with a set of selected prominent features, both linear and naïve Bayes classifiers were applied for performance comparison. This system was tested on normal peripheral blood smear slide images from two datasets. Two sets

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.

Science.gov (United States)

Lee, Myungeun; Woo, Boyeong; Kuo, Michael D; Jamshidi, Neema; Kim, Jong Hyo

2017-01-01

The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC NDR ≥1), while above 35% of the texture features showed poor NDR (software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

Road and Street Centerlines, Street-The data set is a line feature consisting of 13948 line segments representing streets. It was created to maintain the location of city and county based streets., Published in 1989, Davis County Government.

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Road and Street Centerlines dataset current as of 1989. Street-The data set is a line feature consisting of 13948 line segments representing streets. It was created...

Quality of radiomic features in glioblastoma multiforme: Impact of semi-automated tumor segmentation software

International Nuclear Information System (INIS)

Lee, Myung Eun; Kim, Jong Hyo; Woo, Bo Yeong; Ko, Micheal D.; Jamshidi, Neema

2017-01-01

The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Science.gov (United States)

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

A large-scale dataset of solar event reports from automated feature recognition modules

Science.gov (United States)

Schuh, Michael A.; Angryk, Rafal A.; Martens, Petrus C.

2016-05-01

The massive repository of images of the Sun captured by the Solar Dynamics Observatory (SDO) mission has ushered in the era of Big Data for Solar Physics. In this work, we investigate the entire public collection of events reported to the Heliophysics Event Knowledgebase (HEK) from automated solar feature recognition modules operated by the SDO Feature Finding Team (FFT). With the SDO mission recently surpassing five years of operations, and over 280,000 event reports for seven types of solar phenomena, we present the broadest and most comprehensive large-scale dataset of the SDO FFT modules to date. We also present numerous statistics on these modules, providing valuable contextual information for better understanding and validating of the individual event reports and the entire dataset as a whole. After extensive data cleaning through exploratory data analysis, we highlight several opportunities for knowledge discovery from data (KDD). Through these important prerequisite analyses presented here, the results of KDD from Solar Big Data will be overall more reliable and better understood. As the SDO mission remains operational over the coming years, these datasets will continue to grow in size and value. Future versions of this dataset will be analyzed in the general framework established in this work and maintained publicly online for easy access by the community.

Discovery and fusion of salient multimodal features toward news story segmentation

Science.gov (United States)

Hsu, Winston; Chang, Shih-Fu; Huang, Chih-Wei; Kennedy, Lyndon; Lin, Ching-Yung; Iyengar, Giridharan

2003-12-01

In this paper, we present our new results in news video story segmentation and classification in the context of TRECVID video retrieval benchmarking event 2003. We applied and extended the Maximum Entropy statistical model to effectively fuse diverse features from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to automatically discover relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features -- asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measures up to 0.76 in ABC news and 0.73 in CNN news), present how these multi-level multi-modal features construct the probabilistic framework, and more importantly observe an interesting opportunity for further improvement.

Quality of radiomic features in glioblastoma multiforme: Impact of semi-automated tumor segmentation software

Energy Technology Data Exchange (ETDEWEB)

Lee, Myung Eun; Kim, Jong Hyo [Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Seoul National University, Suwon (Korea, Republic of); Woo, Bo Yeong [Dept. of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Suwon (Korea, Republic of); Ko, Micheal D.; Jamshidi, Neema [Dept. of Radiological Sciences, University of California, Los Angeles, Los Angeles (United States)

2017-06-15

The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

Science.gov (United States)

Chen, K.; Weinmann, M.; Gao, X.; Yan, M.; Hinz, S.; Jutzi, B.; Weinmann, M.

2018-05-01

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.

Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Directory of Open Access Journals (Sweden)

Pradipta Maji

Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets

NARCIS (Netherlands)

Aalaei, Shokoufeh; Shahraki, Hadi; Rowhanimanesh, Alireza; Eslami, Saeid

2016-01-01

This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. To

Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

Science.gov (United States)

Luo, Yuan; Cheng, Yu; Uzuner, Özlem; Szolovits, Peter; Starren, Justin

2018-01-01

We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multiscale CNNs for Brain Tumor Segmentation and Diagnosis.

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Zhao, Liya; Jia, Kebin

2016-01-01

Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs). Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

Textural feature calculated from segmental fluences as a modulation index for VMAT.

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Park, So-Yeon; Park, Jong Min; Kim, Jung-In; Kim, Hyoungnyoun; Kim, Il Han; Ye, Sung-Joon

2015-12-01

Textural features calculated from various segmental fluences of volumetric modulated arc therapy (VMAT) plans were optimized to enhance its performance to predict plan delivery accuracy. Twenty prostate and twenty head and neck VMAT plans were selected retrospectively. Fluences were generated for each VMAT plan by summations of segments at sequential groups of control points. The numbers of summed segments were 5, 10, 20, 45, 90, 178 and 356. For each fluence, we investigated 6 textural features: angular second moment, inverse difference moment, contrast, variance, correlation and entropy (particular displacement distances, d = 1, 5 and 10). Spearman's rank correlation coefficients (rs) were calculated between each textural feature and several different measures of VMAT delivery accuracy. The values of rs of contrast (d = 10) with 10 segments to both global and local gamma passing rates with 2%/2 mm were 0.666 (p <0.001) and 0.573 (p <0.001), respectively. It showed rs values of -0.895 (p <0.001) and 0.727 (p <0.001) to multi-leaf collimator positional errors and gantry angle errors during delivery, respectively. The number of statistically significant rs values (p <0.05) to the changes in dose-volumetric parameters during delivery was 14 among a total of 35 tested parameters. Contrast (d = 10) with 10 segments showed higher correlations to the VMAT delivery accuracy than did the conventional modulation indices. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Segmentation of color images by chromaticity features using self-organizing maps

Directory of Open Access Journals (Sweden)

Farid García-Lamont

2016-05-01

Full Text Available Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.

SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.

Science.gov (United States)

Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga

2013-01-01

High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.

Color Image Segmentation Based on Statistics of Location and Feature Similarity

Science.gov (United States)

Mori, Fumihiko; Yamada, Hiromitsu; Mizuno, Makoto; Sugano, Naotoshi

The process of “image segmentation and extracting remarkable regions” is an important research subject for the image understanding. However, an algorithm based on the global features is hardly found. The requisite of such an image segmentation algorism is to reduce as much as possible the over segmentation and over unification. We developed an algorithm using the multidimensional convex hull based on the density as the global feature. In the concrete, we propose a new algorithm in which regions are expanded according to the statistics of the region such as the mean value, standard deviation, maximum value and minimum value of pixel location, brightness and color elements and the statistics are updated. We also introduced a new concept of conspicuity degree and applied it to the various 21 images to examine the effectiveness. The remarkable object regions, which were extracted by the presented system, highly coincided with those which were pointed by the sixty four subjects who attended the psychological experiment.

Weakly supervised semantic segmentation using fore-background priors

Science.gov (United States)

Han, Zheng; Xiao, Zhitao; Yu, Mingjun

2017-07-01

Weakly-supervised semantic segmentation is a challenge in the field of computer vision. Most previous works utilize the labels of the whole training set and thereby need the construction of a relationship graph about image labels, thus result in expensive computation. In this study, we tackle this problem from a different perspective. We proposed a novel semantic segmentation algorithm based on background priors, which avoids the construction of a huge graph in whole training dataset. Specifically, a random forest classifier is obtained using weakly supervised training data .Then semantic texton forest (STF) feature is extracted from image superpixels. Finally, a CRF based optimization algorithm is proposed. The unary potential of CRF derived from the outputting probability of random forest classifier and the robust saliency map as background prior. Experiments on the MSRC21 dataset show that the new algorithm outperforms some previous influential weakly-supervised segmentation algorithms. Furthermore, the use of efficient decision forests classifier and parallel computing of saliency map significantly accelerates the implementation.

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology.

Science.gov (United States)

Kumar, Neeraj; Verma, Ruchika; Sharma, Sanuj; Bhargava, Surabhi; Vahadane, Abhishek; Sethi, Amit

2017-07-01

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

Nearest neighbor 3D segmentation with context features

Science.gov (United States)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

Exudate-based diabetic macular edema detection in fundus images using publicly available datasets

Energy Technology Data Exchange (ETDEWEB)

Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

2011-01-01

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4 s (9.3 s, considering the optic nerve localization) per image on an 2.6 GHz platform with an unoptimized Matlab implementation.

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

Science.gov (United States)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

Directory of Open Access Journals (Sweden)

Y. Di

2017-05-01

Full Text Available Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA on the accuracy and slightly inferior to FNEA on the efficiency.

Multiscale CNNs for Brain Tumor Segmentation and Diagnosis

Directory of Open Access Journals (Sweden)

Liya Zhao

2016-01-01

Full Text Available Early brain tumor detection and diagnosis are critical to clinics. Thus segmentation of focused tumor area needs to be accurate, efficient, and robust. In this paper, we propose an automatic brain tumor segmentation method based on Convolutional Neural Networks (CNNs. Traditional CNNs focus only on local features and ignore global region features, which are both important for pixel classification and recognition. Besides, brain tumor can appear in any place of the brain and be any size and shape in patients. We design a three-stream framework named as multiscale CNNs which could automatically detect the optimum top-three scales of the image sizes and combine information from different scales of the regions around that pixel. Datasets provided by Multimodal Brain Tumor Image Segmentation Benchmark (BRATS organized by MICCAI 2013 are utilized for both training and testing. The designed multiscale CNNs framework also combines multimodal features from T1, T1-enhanced, T2, and FLAIR MRI images. By comparison with traditional CNNs and the best two methods in BRATS 2012 and 2013, our framework shows advances in brain tumor segmentation accuracy and robustness.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

Science.gov (United States)

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

Science.gov (United States)

Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

2012-07-01

Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

Science.gov (United States)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection.

Science.gov (United States)

Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

2014-11-01

Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

Science.gov (United States)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

NUCLEAR SEGMENTATION IN MICROSCOPE CELL IMAGES: A HAND-SEGMENTED DATASET AND COMPARISON OF ALGORITHMS

OpenAIRE

Coelho, Luís Pedro; Shariff, Aabid; Murphy, Robert F.

2009-01-01

Image segmentation is an essential step in many image analysis pipelines and many algorithms have been proposed to solve this problem. However, they are often evaluated subjectively or based on a small number of examples. To fill this gap, we hand-segmented a set of 97 fluorescence microscopy images (a total of 4009 cells) and objectively evaluated some previously proposed segmentation algorithms.

Contextually guided very-high-resolution imagery classification with semantic segments

Science.gov (United States)

Zhao, Wenzhi; Du, Shihong; Wang, Qiao; Emery, William J.

2017-10-01

Contextual information, revealing relationships and dependencies between image objects, is one of the most important information for the successful interpretation of very-high-resolution (VHR) remote sensing imagery. Over the last decade, geographic object-based image analysis (GEOBIA) technique has been widely used to first divide images into homogeneous parts, and then to assign semantic labels according to the properties of image segments. However, due to the complexity and heterogeneity of VHR images, segments without semantic labels (i.e., semantic-free segments) generated with low-level features often fail to represent geographic entities (such as building roofs usually be partitioned into chimney/antenna/shadow parts). As a result, it is hard to capture contextual information across geographic entities when using semantic-free segments. In contrast to low-level features, "deep" features can be used to build robust segments with accurate labels (i.e., semantic segments) in order to represent geographic entities at higher levels. Based on these semantic segments, semantic graphs can be constructed to capture contextual information in VHR images. In this paper, semantic segments were first explored with convolutional neural networks (CNN) and a conditional random field (CRF) model was then applied to model the contextual information between semantic segments. Experimental results on two challenging VHR datasets (i.e., the Vaihingen and Beijing scenes) indicate that the proposed method is an improvement over existing image classification techniques in classification performance (overall accuracy ranges from 82% to 96%).

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

Energy Technology Data Exchange (ETDEWEB)

Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2014-11-01

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

International Nuclear Information System (INIS)

Park, Sang Hyun; Gao, Yaozong; Shi, Yinghuan; Shen, Dinggang

2014-01-01

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

Comparative study on the performance of textural image features for active contour segmentation.

Science.gov (United States)

Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

Local appearance features for robust MRI brain structure segmentation across scanning protocols

DEFF Research Database (Denmark)

Achterberg, H.C.; Poot, Dirk H. J.; van der Lijn, Fedde

2013-01-01

Segmentation of brain structures in magnetic resonance images is an important task in neuro image analysis. Several papers on this topic have shown the benefit of supervised classification based on local appearance features, often combined with atlas-based approaches. These methods require...... a representative annotated training set and therefore often do not perform well if the target image is acquired on a different scanner or with a different acquisition protocol than the training images. Assuming that the appearance of the brain is determined by the underlying brain tissue distribution...... with substantially different imaging protocols and on different scanners. While a combination of conventional appearance features trained on data from a different scanner with multiatlas segmentation performed poorly with an average Dice overlap of 0.698, the local appearance model based on the new acquisition...

Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

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Mustafa Serter Uzer

2013-01-01

Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

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Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

2015-06-15

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

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Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters

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Xia Zheng

2015-01-01

Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.

Radiological features of epiploic appendagitis and segmental omental infarction

International Nuclear Information System (INIS)

McClure, Mark J.; Khalili, Korosh; Sarrazin, Josee; Hanbidge, Anthony

2001-01-01

Epiploic appendagitis and segmental omental infarction are more frequently encountered with the increased use of abdominal ultrasound and Computed tomography (CT) in the radiological assessment of the patient who presents clinically with acute abdominal pain. Recognition of specific imaging abnormalities enables the radiologist to make the correct diagnosis. This is important, as the appropriate management of both conditions is often conservative. Follow-up imaging features correlate with clinical improvement. McClure, M.J. et al. (2001)

BFROST: binary features from robust orientation segment tests accelerated on the GPU

CSIR Research Space (South Africa)

Cronje, J

2011-11-01

Full Text Available purpose parallel algo- rithms. The CUDA (Compute Unified Device Architecture) [1] framework from NVidia provides a programmable interface for GPUs. FAST (Features from Accelerated Segment Tests) [2], [3] is one of the fastest and most reliable corner... runs. Our detector detects slightly more keypoints because the decision tree of FAST does not perform a complete segment test. Timing comparisons were performed on a NVidia GeForce GTX 460 for our GPU implementation and on a Intel Core i7 2.67 GHz...

A fusion network for semantic segmentation using RGB-D data

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Yuan, Jiahui; Zhang, Kun; Xia, Yifan; Qi, Lin; Dong, Junyu

2018-04-01

Semantic scene parsing is considerable in many intelligent field, including perceptual robotics. For the past few years, pixel-wise prediction tasks like semantic segmentation with RGB images has been extensively studied and has reached very remarkable parsing levels, thanks to convolutional neural networks (CNNs) and large scene datasets. With the development of stereo cameras and RGBD sensors, it is expected that additional depth information will help improving accuracy. In this paper, we propose a semantic segmentation framework incorporating RGB and complementary depth information. Motivated by the success of fully convolutional networks (FCN) in semantic segmentation field, we design a fully convolutional networks consists of two branches which extract features from both RGB and depth data simultaneously and fuse them as the network goes deeper. Instead of aggregating multiple model, our goal is to utilize RGB data and depth data more effectively in a single model. We evaluate our approach on the NYU-Depth V2 dataset, which consists of 1449 cluttered indoor scenes, and achieve competitive results with the state-of-the-art methods.

A Higher-Order Neural Network Design for Improving Segmentation Performance in Medical Image Series

International Nuclear Information System (INIS)

Selvi, Eşref; Selver, M Alper; Güzeliş, Cüneyt; Dicle, Oǧuz

2014-01-01

Segmentation of anatomical structures from medical image series is an ongoing field of research. Although, organs of interest are three-dimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme. To be able to use slice-by-slice techniques effectively, adjacent slice information, which represents likelihood of a region to be the structure of interest, plays critical role. Recent studies focus on using distance transform directly as a feature or to increase the feature values at the vicinity of the search area. This study presents a novel approach by constructing a higher order neural network, the input layer of which receives features together with their multiplications with the distance transform. This allows higher-order interactions between features through the non-linearity introduced by the multiplication. The application of the proposed method to 9 CT datasets for segmentation of the liver shows higher performance than well-known higher order classification neural networks

SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

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A. Rezaeian

2015-12-01

Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR sensors can collect useful observations from earth’s surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT. Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

Science.gov (United States)

Rezaeian, A.; Homayouni, S.; Safari, A.

2015-12-01

Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

Calibrated Full-Waveform Airborne Laser Scanning for 3D Object Segmentation

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Fanar M. Abed

2014-05-01

Full Text Available Segmentation of urban features is considered a major research challenge in the fields of photogrammetry and remote sensing. However, the dense datasets now readily available through airborne laser scanning (ALS offer increased potential for 3D object segmentation. Such potential is further augmented by the availability of full-waveform (FWF ALS data. FWF ALS has demonstrated enhanced performance in segmentation and classification through the additional physical observables which can be provided alongside standard geometric information. However, use of FWF information is not recommended without prior radiometric calibration, taking into account all parameters affecting the backscatter energy. This paper reports the implementation of a radiometric calibration workflow for FWF ALS data, and demonstrates how the resultant FWF information can be used to improve segmentation of an urban area. The developed segmentation algorithm presents a novel approach which uses the calibrated backscatter cross-section as a weighting function to estimate the segmentation similarity measure. The normal vector and the local Euclidian distance are used as criteria to segment the point clouds through a region growing approach. The paper demonstrates the potential to enhance 3D object segmentation in urban areas by integrating the FWF physical backscattered energy alongside geometric information. The method is demonstrated through application to an interest area sampled from a relatively dense FWF ALS dataset. The results are assessed through comparison to those delivered from utilising only geometric information. Validation against a manual segmentation demonstrates a successful automatic implementation, achieving a segmentation accuracy of 82%, and out-performs a purely geometric approach.

Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

Science.gov (United States)

Wang, Shuo; Zhou, Mu; Liu, Zaiyi; Liu, Zhenyu; Gu, Dongsheng; Zang, Yali; Dong, Di; Gevaert, Olivier; Tian, Jie

2017-08-01

Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. Copyright © 2017. Published by Elsevier B.V.

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

Science.gov (United States)

Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

2018-04-15

This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

Science.gov (United States)

Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

2016-05-01

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

Directory of Open Access Journals (Sweden)

Rasha Al Shehhi

2016-01-01

Full Text Available This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH morphology and context and graph-analysis algorithms (e.g., Dijkstra path. The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

SU-E-J-252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi-Automated Segmentation Methods

Energy Technology Data Exchange (ETDEWEB)

Lee, M; Woo, B; Kim, J [Seoul National University, Seoul (Korea, Republic of); Jamshidi, N; Kuo, M [UCLA School of Medicine, Los Angeles, CA (United States)

2015-06-15

Purpose: Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi-automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM). Methods: MRI examinations with T1 post-gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi-automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automatically from the segmented tumor. The inter-observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility. Results: Inter-observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE-T1W1. Conclusion: Comparison of two semi-automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI.

SU-E-J-252: Reproducibility of Radiogenomic Image Features: Comparison of Two Semi-Automated Segmentation Methods

International Nuclear Information System (INIS)

Lee, M; Woo, B; Kim, J; Jamshidi, N; Kuo, M

2015-01-01

Purpose: Objective and reliable quantification of imaging phenotype is an essential part of radiogenomic studies. We compared the reproducibility of two semi-automatic segmentation methods for quantitative image phenotyping in magnetic resonance imaging (MRI) of glioblastoma multiforme (GBM). Methods: MRI examinations with T1 post-gadolinium and FLAIR sequences of 10 GBM patients were downloaded from the Cancer Image Archive site. Two semi-automatic segmentation tools with different algorithms (deformable model and grow cut method) were used to segment contrast enhancement, necrosis and edema regions by two independent observers. A total of 21 imaging features consisting of area and edge groups were extracted automatically from the segmented tumor. The inter-observer variability and coefficient of variation (COV) were calculated to evaluate the reproducibility. Results: Inter-observer correlations and coefficient of variation of imaging features with the deformable model ranged from 0.953 to 0.999 and 2.1% to 9.2%, respectively, and the grow cut method ranged from 0.799 to 0.976 and 3.5% to 26.6%, respectively. Coefficient of variation for especially important features which were previously reported as predictive of patient survival were: 3.4% with deformable model and 7.4% with grow cut method for the proportion of contrast enhanced tumor region; 5.5% with deformable model and 25.7% with grow cut method for the proportion of necrosis; and 2.1% with deformable model and 4.4% with grow cut method for edge sharpness of tumor on CE-T1W1. Conclusion: Comparison of two semi-automated tumor segmentation techniques shows reliable image feature extraction for radiogenomic analysis of GBM patients with multiparametric Brain MRI

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

Science.gov (United States)

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV

Multi-scale Gaussian representation and outline-learning based cell image segmentation

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2013-01-01

Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

Automatic Craniomaxillofacial Landmark Digitization via Segmentation-guided Partially-joint Regression Forest Model and Multi-scale Statistical Features

Science.gov (United States)

Zhang, Jun; Gao, Yaozong; Wang, Li; Tang, Zhen; Xia, James J.; Shen, Dinggang

2016-01-01

Objective The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images. Methods We propose a Segmentation-guided Partially-joint Regression Forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization (VQ) method to extract high-level multi-scale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts. Results Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than 2mm. Conclusion Our model has addressed challenges of both inter-patient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization. Significance Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency. PMID:26625402

WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

Energy Technology Data Exchange (ETDEWEB)

Yang, X; Rossi, P; Jani, A; Ogunleye, T; Curran, W; Liu, T [Emory Univ, Atlanta, GA (United States)

2015-06-15

Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful

Adaptive Binary Arithmetic Coder-Based Image Feature and Segmentation in the Compressed Domain

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Hsi-Chin Hsin

2012-01-01

Full Text Available Image compression is necessary in various applications, especially for efficient transmission over a band-limited channel. It is thus desirable to be able to segment an image in the compressed domain directly such that the burden of decompressing computation can be avoided. Motivated by the adaptive binary arithmetic coder (MQ coder of JPEG2000, we propose an efficient scheme to segment the feature vectors that are extracted from the code stream of an image. We modify the Compression-based Texture Merging (CTM algorithm to alleviate the influence of overmerging problem by making use of the rate distortion information. Experimental results show that the MQ coder-based image segmentation is preferable in terms of the boundary displacement error (BDE measure. It has the advantage of saving computational cost as the segmentation results even at low rates of bits per pixel (bpp are satisfactory.

Automatic liver volume segmentation and fibrosis classification

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Bal, Evgeny; Klang, Eyal; Amitai, Michal; Greenspan, Hayit

2018-02-01

In this work, we present an automatic method for liver segmentation and fibrosis classification in liver computed-tomography (CT) portal phase scans. The input is a full abdomen CT scan with an unknown number of slices, and the output is a liver volume segmentation mask and a fibrosis grade. A multi-stage analysis scheme is applied to each scan, including: volume segmentation, texture features extraction and SVM based classification. Data contains portal phase CT examinations from 80 patients, taken with different scanners. Each examination has a matching Fibroscan grade. The dataset was subdivided into two groups: first group contains healthy cases and mild fibrosis, second group contains moderate fibrosis, severe fibrosis and cirrhosis. Using our automated algorithm, we achieved an average dice index of 0.93 ± 0.05 for segmentation and a sensitivity of 0.92 and specificity of 0.81for classification. To the best of our knowledge, this is a first end to end automatic framework for liver fibrosis classification; an approach that, once validated, can have a great potential value in the clinic.

Benefit-feature segmentation: A tool for the design of a supply-chain strategy

NARCIS (Netherlands)

Canever, M.D.; Trijp, van J.C.M.; Lans, van der I.A.

2007-01-01

Abstract: Purpose ¿ This paper aims to assess the effectiveness of different segmentation schemes as the basis of marketing strategy, with particular respect to supply-chain decisions, and to propose a new procedure capable of combining benefits sought and features available.

Segmenting texts from outdoor images taken by mobile phones using color features

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Liu, Zongyi; Zhou, Hanning

2011-01-01

Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

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Dane Brown

2017-07-01

Full Text Available The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization.

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Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang

2013-01-01

Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

Fast and robust segmentation of white blood cell images by self-supervised learning.

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Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo

2018-04-01

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

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Ward Kevin R

2009-11-01

Full Text Available Abstract Background Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI. Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information. Methods First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM and Maximum A Posteriori Spatial Probability (MASP are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases. Results Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images. Conclusion The experiments show the reliability of the proposed algorithms. The

Automatical and accurate segmentation of cerebral tissues in fMRI dataset with combination of image processing and deep learning

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Kong, Zhenglun; Luo, Junyi; Xu, Shengpu; Li, Ting

2018-02-01

Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.

Contour tracing for segmentation of mammographic masses

International Nuclear Information System (INIS)

Elter, Matthias; Held, Christian; Wittenberg, Thomas

2010-01-01

CADx systems have the potential to support radiologists in the difficult task of discriminating benign and malignant mammographic lesions. The segmentation of mammographic masses from the background tissue is an important module of CADx systems designed for the characterization of mass lesions. In this work, a novel approach to this task is presented. The segmentation is performed by automatically tracing the mass' contour in-between manually provided landmark points defined on the mass' margin. The performance of the proposed approach is compared to the performance of implementations of three state-of-the-art approaches based on region growing and dynamic programming. For an unbiased comparison of the different segmentation approaches, optimal parameters are selected for each approach by means of tenfold cross-validation and a genetic algorithm. Furthermore, segmentation performance is evaluated on a dataset of ROI and ground-truth pairs. The proposed method outperforms the three state-of-the-art methods. The benchmark dataset will be made available with publication of this paper and will be the first publicly available benchmark dataset for mass segmentation.

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

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Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images

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Alshehhi, Rasha; Marpu, Prashanth Reddy

2017-04-01

Extraction of road networks in urban areas from remotely sensed imagery plays an important role in many urban applications (e.g. road navigation, geometric correction of urban remote sensing images, updating geographic information systems, etc.). It is normally difficult to accurately differentiate road from its background due to the complex geometry of the buildings and the acquisition geometry of the sensor. In this paper, we present a new method for extracting roads from high-resolution imagery based on hierarchical graph-based image segmentation. The proposed method consists of: 1. Extracting features (e.g., using Gabor and morphological filtering) to enhance the contrast between road and non-road pixels, 2. Graph-based segmentation consisting of (i) Constructing a graph representation of the image based on initial segmentation and (ii) Hierarchical merging and splitting of image segments based on color and shape features, and 3. Post-processing to remove irregularities in the extracted road segments. Experiments are conducted on three challenging datasets of high-resolution images to demonstrate the proposed method and compare with other similar approaches. The results demonstrate the validity and superior performance of the proposed method for road extraction in urban areas.

Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data

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Raphael Falque

2017-10-01

Full Text Available Remote-Field Eddy-Current (RFEC technology is often used as a Non-Destructive Evaluation (NDE method to prevent water pipe failures. By analyzing the RFEC data, it is possible to quantify the corrosion present in pipes. Quantifying the corrosion involves detecting defects and extracting their depth and shape. For large sections of pipelines, this can be extremely time-consuming if performed manually. Automated approaches are therefore well motivated. In this article, we propose an automated framework to locate and segment defects in individual pipe segments, starting from raw RFEC measurements taken over large pipelines. The framework relies on a novel feature to robustly detect these defects and a segmentation algorithm applied to the deconvolved RFEC signal. The framework is evaluated using both simulated and real datasets, demonstrating its ability to efficiently segment the shape of corrosion defects.

A fully automated algorithm of baseline correction based on wavelet feature points and segment interpolation

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Qian, Fang; Wu, Yihui; Hao, Peng

2017-11-01

Baseline correction is a very important part of pre-processing. Baseline in the spectrum signal can induce uneven amplitude shifts across different wavenumbers and lead to bad results. Therefore, these amplitude shifts should be compensated before further analysis. Many algorithms are used to remove baseline, however fully automated baseline correction is convenient in practical application. A fully automated algorithm based on wavelet feature points and segment interpolation (AWFPSI) is proposed. This algorithm finds feature points through continuous wavelet transformation and estimates baseline through segment interpolation. AWFPSI is compared with three commonly introduced fully automated and semi-automated algorithms, using simulated spectrum signal, visible spectrum signal and Raman spectrum signal. The results show that AWFPSI gives better accuracy and has the advantage of easy use.

CT-based manual segmentation and evaluation of paranasal sinuses.

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Pirner, S; Tingelhoff, K; Wagner, I; Westphal, R; Rilk, M; Wahl, F M; Bootz, F; Eichhorn, Klaus W G

2009-04-01

Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.

Electronic Gaming Machine (EGM) Environments: Market Segments and Risk.

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Rockloff, Matthew; Moskovsky, Neda; Thorne, Hannah; Browne, Matthew; Bryden, Gabrielle

2017-12-01

This study used a marketing-research paradigm to explore gamblers' attraction to EGMs based on different elements of the environment. A select set of environmental features was sourced from a prior study (Thorne et al. in J Gambl Issues 2016b), and a discrete choice experiment was conducted through an online survey. Using the same dataset first described by Rockloff et al. (EGM Environments that contribute to excess consumption and harm, 2015), a sample of 245 EGM gamblers were sourced from clubs in Victoria, Australia, and 7516 gamblers from an Australian national online survey-panel. Participants' choices amongst sets of hypothetical gambling environments allowed for an estimation of the implied individual-level utilities for each feature (e.g., general sounds, location, etc.). K-means clustering on these utilities identified four unique market segments for EGM gambling, representing four different types of consumers. The segments were named according to their dominant features: Social, Value, High Roller and Internet. We found that the environments orientated towards the Social and Value segments were most conducive to attracting players with relatively few gambling problems, while the High Roller and Internet-focused environments had greater appeal for players with problems and vulnerabilities. This study has generated new insights into the kinds of gambling environments that are most consistent with safe play.

Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

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Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

2015-01-01

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

Texture-based segmentation with Gabor filters, wavelet and pyramid decompositions for extracting individual surface features from areal surface topography maps

International Nuclear Information System (INIS)

Senin, Nicola; Leach, Richard K; Pini, Stefano; Blunt, Liam A

2015-01-01

Areal topography segmentation plays a fundamental role in those surface metrology applications concerned with the characterisation of individual topography features. Typical scenarios include the dimensional inspection and verification of micro-structured surface features, and the identification and characterisation of localised defects and other random singularities. While morphological segmentation into hills or dales is the only partitioning operation currently endorsed by the ISO specification standards on surface texture metrology, many other approaches are possible, in particular adapted from the literature on digital image segmentation. In this work an original segmentation approach is introduced and discussed, where topography partitioning is driven by information collected through the application of texture characterisation transforms popular in digital image processing. Gabor filters, wavelets and pyramid decompositions are investigated and applied to a selected set of test cases. The behaviour, performance and limitations of the proposed approach are discussed from the viewpoint of the identification and extraction of individual surface topography features. (paper)

Cell nuclei segmentation in fluorescence microscopy images using inter- and intra-region discriminative information.

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Song, Yang; Cai, Weidong; Feng, David Dagan; Chen, Mei

2013-01-01

Automated segmentation of cell nuclei in microscopic images is critical to high throughput analysis of the ever increasing amount of data. Although cell nuclei are generally visually distinguishable for human, automated segmentation faces challenges when there is significant intensity inhomogeneity among cell nuclei or in the background. In this paper, we propose an effective method for automated cell nucleus segmentation using a three-step approach. It first obtains an initial segmentation by extracting salient regions in the image, then reduces false positives using inter-region feature discrimination, and finally refines the boundary of the cell nuclei using intra-region contrast information. This method has been evaluated on two publicly available datasets of fluorescence microscopic images with 4009 cells, and has achieved superior performance compared to popular state of the art methods using established metrics.

A variational approach to liver segmentation using statistics from multiple sources

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Zheng, Shenhai; Fang, Bin; Li, Laquan; Gao, Mingqi; Wang, Yi

2018-01-01

Medical image segmentation plays an important role in digital medical research, and therapy planning and delivery. However, the presence of noise and low contrast renders automatic liver segmentation an extremely challenging task. In this study, we focus on a variational approach to liver segmentation in computed tomography scan volumes in a semiautomatic and slice-by-slice manner. In this method, one slice is selected and its connected component liver region is determined manually to initialize the subsequent automatic segmentation process. From this guiding slice, we execute the proposed method downward to the last one and upward to the first one, respectively. A segmentation energy function is proposed by combining the statistical shape prior, global Gaussian intensity analysis, and enforced local statistical feature under the level set framework. During segmentation, the shape of the liver shape is estimated by minimization of this function. The improved Chan-Vese model is used to refine the shape to capture the long and narrow regions of the liver. The proposed method was verified on two independent public databases, the 3D-IRCADb and the SLIVER07. Among all the tested methods, our method yielded the best volumetric overlap error (VOE) of 6.5 +/- 2.8 % , the best root mean square symmetric surface distance (RMSD) of 2.1 +/- 0.8 mm, the best maximum symmetric surface distance (MSD) of 18.9 +/- 8.3 mm in 3D-IRCADb dataset, and the best average symmetric surface distance (ASD) of 0.8 +/- 0.5 mm, the best RMSD of 1.5 +/- 1.1 mm in SLIVER07 dataset, respectively. The results of the quantitative comparison show that the proposed liver segmentation method achieves competitive segmentation performance with state-of-the-art techniques.

Formation Features of the Customer Segments for the Network Organizations in the Smart Era

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Elena V. Yaroshenko

2017-01-01

Full Text Available Modern network society is based on the advances of information era of Smart, connecting information and communication technologies, intellectual resources and new forms of managing in the global electronic space. It leads to domination of network forms of the organization of economic activity. Many experts prove the importance of segmentation process of consumers when developing competitive strategy of the organization. Every company needs a competent segmentation of the customer base, allowing to concentrate the attention on satisfaction of requirements of the most perspective client segments. The network organizations have specific characteristics; therefore, it is important to understand how they can influence on the formation of client profiles. It causes the necessity of the network organizations’ research in terms of management of high-profitable client segments.The aim of this study is to determine the characteristics of the market segmentation and to choose the key customers for the network organizations. This purpose has defined the statement and the solution of the following tasks: to explore characteristic features of the network forms of the organization of economic activity of the companies, their prospects, Smart technologies’ influence on them; to reveal the work importance with different client profiles; to explore the existing methods and tools of formation of key customer segments; to define criteria for selection of key groups; to reveal the characteristics of customer segments’ formation for the network organizations.In the research process, methods of the system analysis, a method of analogies, methods of generalizations, a method of the expert evaluations, methods of classification and clustering were applied.This paper explores the characteristics and principles of functioning of network organizations, the appearance of which is directly linked with the development of Smart society. It shows the influence on the

DeepCotton: in-field cotton segmentation using deep fully convolutional network

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Li, Yanan; Cao, Zhiguo; Xiao, Yang; Cremers, Armin B.

2017-09-01

Automatic ground-based in-field cotton (IFC) segmentation is a challenging task in precision agriculture, which has not been well addressed. Nearly all the existing methods rely on hand-crafted features. Their limited discriminative power results in unsatisfactory performance. To address this, a coarse-to-fine cotton segmentation method termed "DeepCotton" is proposed. It contains two modules, fully convolutional network (FCN) stream and interference region removal stream. First, FCN is employed to predict initially coarse map in an end-to-end manner. The convolutional networks involved in FCN guarantee powerful feature description capability, simultaneously, the regression analysis ability of neural network assures segmentation accuracy. To our knowledge, we are the first to introduce deep learning to IFC segmentation. Second, our proposed "UP" algorithm composed of unary brightness transformation and pairwise region comparison is used for obtaining interference map, which is executed to refine the coarse map. The experiments on constructed IFC dataset demonstrate that our method outperforms other state-of-the-art approaches, either in different common scenarios or single/multiple plants. More remarkable, the "UP" algorithm greatly improves the property of the coarse result, with the average amplifications of 2.6%, 2.4% on accuracy and 8.1%, 5.5% on intersection over union for common scenarios and multiple plants, separately.

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models.

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Lee, Wen-Li; Chang, Koyin; Hsieh, Kai-Sheng

2016-09-01

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

Mammogram segmentation using maximal cell strength updation in cellular automata.

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Anitha, J; Peter, J Dinesh

2015-08-01

Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.

MO-DE-207B-05: Predicting Gene Mutations in Renal Cell Carcinoma Based On CT Imaging Features: Validation Using TCGA-TCIA Datasets

Energy Technology Data Exchange (ETDEWEB)

Chen, X; Zhou, Z; Thomas, K; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: The goal of this work is to investigate the use of contrast enhanced computed tomographic (CT) features for the prediction of mutations of BAP1, PBRM1, and VHL genes in renal cell carcinoma (RCC). Methods: For this study, we used two patient databases with renal cell carcinoma (RCC). The first one consisted of 33 patients from our institution (UT Southwestern Medical Center, UTSW). The second one consisted of 24 patients from the Cancer Imaging Archive (TCIA), where each patient is connected by a unique identi?er to the tissue samples from the Cancer Genome Atlas (TCGA). From the contrast enhanced CT image of each patient, tumor contour was first delineated by a physician. Geometry, intensity, and texture features were extracted from the delineated tumor. Based on UTSW dataset, we completed feature selection and trained a support vector machine (SVM) classifier to predict mutations of BAP1, PBRM1 and VHL genes. We then used TCIA-TCGA dataset to validate the predictive model build upon UTSW dataset. Results: The prediction accuracy of gene expression of TCIA-TCGA patients was 0.83 (20 of 24), 0.83 (20 of 24), and 0.75 (18 of 24) for BAP1, PBRM1, and VHL respectively. For BAP1 gene, texture feature was the most prominent feature type. For PBRM1 gene, intensity feature was the most prominent. For VHL gene, geometry, intensity, and texture features were all important. Conclusion: Using our feature selection strategy and models, we achieved predictive accuracy over 0.75 for all three genes under the condition of using patient data from one institution for training and data from other institutions for testing. These results suggest that radiogenomics can be used to aid in prognosis and used as convenient surrogates for expensive and time consuming gene assay procedures.

Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

International Nuclear Information System (INIS)

Lassen, B C; Kuhnigk, J-M; Van Ginneken, B; Van Rikxoort, E M; Jacobs, C

2015-01-01

The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of

Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

Science.gov (United States)

Lassen, B. C.; Jacobs, C.; Kuhnigk, J.-M.; van Ginneken, B.; van Rikxoort, E. M.

2015-02-01

The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of

Automatic registration method for multisensor datasets adopted for dimensional measurements on cutting tools

International Nuclear Information System (INIS)

Shaw, L; Mehari, F; Weckenmann, A; Ettl, S; Häusler, G

2013-01-01

Multisensor systems with optical 3D sensors are frequently employed to capture complete surface information by measuring workpieces from different views. During coarse and fine registration the resulting datasets are afterward transformed into one common coordinate system. Automatic fine registration methods are well established in dimensional metrology, whereas there is a deficit in automatic coarse registration methods. The advantage of a fully automatic registration procedure is twofold: it enables a fast and contact-free alignment and further a flexible application to datasets of any kind of optical 3D sensor. In this paper, an algorithm adapted for a robust automatic coarse registration is presented. The method was originally developed for the field of object reconstruction or localization. It is based on a segmentation of planes in the datasets to calculate the transformation parameters. The rotation is defined by the normals of three corresponding segmented planes of two overlapping datasets, while the translation is calculated via the intersection point of the segmented planes. First results have shown that the translation is strongly shape dependent: 3D data of objects with non-orthogonal planar flanks cannot be registered with the current method. In the novel supplement for the algorithm, the translation is additionally calculated via the distance between centroids of corresponding segmented planes, which results in more than one option for the transformation. A newly introduced measure considering the distance between the datasets after coarse registration evaluates the best possible transformation. Results of the robust automatic registration method are presented on the example of datasets taken from a cutting tool with a fringe-projection system and a focus-variation system. The successful application in dimensional metrology is proven with evaluations of shape parameters based on the registered datasets of a calibrated workpiece. (paper)

Improving image segmentation by learning region affinities

Energy Technology Data Exchange (ETDEWEB)

Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.

2010-11-03

We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

Science.gov (United States)

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; van Uden, Inge W M; Sanchez, Clara I; Litjens, Geert; de Leeuw, Frank-Erik; van Ginneken, Bram; Marchiori, Elena; Platel, Bram

2017-07-11

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

Science.gov (United States)

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Characterization of mammographic masses based on level set segmentation with new image features and patient information

International Nuclear Information System (INIS)

Shi Jiazheng; Sahiner, Berkman; Chan Heangping; Ge Jun; Hadjiiski, Lubomir; Helvie, Mark A.; Nees, Alexis; Wu Yita; Wei Jun; Zhou Chuan; Zhang Yiheng; Cui Jing

2008-01-01

Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. The authors' previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. The authors' primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based A z value of 0.83±0.01. The improvement compared to the previous CAD system was statistically significant (p=0.02). When patient age was included in the new CAD system, view-based and case-based A z values were 0.85±0.01 and 0.87±0.02, respectively. The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening

SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

Energy Technology Data Exchange (ETDEWEB)

Verburg, E; Waard, SN de; Veldhuis, WB; Gils, CH van; Gilhuijs, KGA [University Medical Center Utrecht, Utrecht (Netherlands)

2016-06-15

Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.

SU-C-207B-04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts

International Nuclear Information System (INIS)

Verburg, E; Waard, SN de; Veldhuis, WB; Gils, CH van; Gilhuijs, KGA

2016-01-01

Purpose: To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts. Methods: Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI-RADS category 4). Each MR dataset consisted of 178 or 179 T1-weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions. Results: According to the radiologist’s scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%. Conclusion: Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

Science.gov (United States)

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-07-01

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Science.gov (United States)

Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

International Nuclear Information System (INIS)

Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

2014-01-01

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Energy Technology Data Exchange (ETDEWEB)

Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

2014-07-15

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

SU-E-J-131: Augmenting Atlas-Based Segmentation by Incorporating Image Features Proximal to the Atlas Contours

International Nuclear Information System (INIS)

Li, Dengwang; Liu, Li; Kapp, Daniel S.; Xing, Lei

2015-01-01

Purpose: For facilitating the current automatic segmentation, in this work we propose a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. Methods: In setting up an atlas-based library, we include not only the coordinates of contour points, but also the image features adjacent to the contour. 139 planning CT scans with normal appearing livers obtained during their radiotherapy treatment planning were used to construct the library. The CT images within the library were registered each other using affine registration. A nonlinear narrow shell with the regional thickness determined by the distance between two vertices alongside the contour. The narrow shell was automatically constructed both inside and outside of the liver contours. The common image features within narrow shell between a new case and a library case were first selected by a Speed-up Robust Features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the images of the new patient by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy function within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by a physician. Results: Application of the technique to 30 liver cases suggested that the technique was capable of reliably segment organs such as the liver with little human intervention. Compared with the manual segmentation results by a physician, the average and discrepancies of the volumetric overlap percentage (VOP) was found to be 92.43%+2.14%. Conclusion: Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically

SU-E-J-131: Augmenting Atlas-Based Segmentation by Incorporating Image Features Proximal to the Atlas Contours

Energy Technology Data Exchange (ETDEWEB)

Li, Dengwang; Liu, Li [College of Physics and Electronics, Shandong Normal University, Jinan, Shandong (China); Kapp, Daniel S.; Xing, Lei [Department of Radiation Oncology, Stanford University, School of Medicine, Stanford, CA (United States)

2015-06-15

Purpose: For facilitating the current automatic segmentation, in this work we propose a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. Methods: In setting up an atlas-based library, we include not only the coordinates of contour points, but also the image features adjacent to the contour. 139 planning CT scans with normal appearing livers obtained during their radiotherapy treatment planning were used to construct the library. The CT images within the library were registered each other using affine registration. A nonlinear narrow shell with the regional thickness determined by the distance between two vertices alongside the contour. The narrow shell was automatically constructed both inside and outside of the liver contours. The common image features within narrow shell between a new case and a library case were first selected by a Speed-up Robust Features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the images of the new patient by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy function within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by a physician. Results: Application of the technique to 30 liver cases suggested that the technique was capable of reliably segment organs such as the liver with little human intervention. Compared with the manual segmentation results by a physician, the average and discrepancies of the volumetric overlap percentage (VOP) was found to be 92.43%+2.14%. Conclusion: Incorporation of image features into the library contours improves the currently available atlas-based auto-contouring techniques and provides a clinically

Multi-modal RGB–Depth–Thermal Human Body Segmentation

DEFF Research Database (Denmark)

Palmero, Cristina; Clapés, Albert; Bahnsen, Chris

2016-01-01

This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB-Depth-Thermal dataset along with a multi-modal seg- mentation baseline. The several modalities are registered us- ing a calibration...... to other state-of-the-art meth- ods, obtaining an overlap above 75% on the novel dataset when compared to the manually annotated ground-truth of human segmentations....

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

Science.gov (United States)

Parkinson, Craig; Foley, Kieran; Whybra, Philip; Hills, Robert; Roberts, Ashley; Marshall, Chris; Staffurth, John; Spezi, Emiliano

2018-04-11

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

Segmentation-less Digital Rock Physics

Science.gov (United States)

Tisato, N.; Ikeda, K.; Goldfarb, E. J.; Spikes, K. T.

2017-12-01

In the last decade, Digital Rock Physics (DRP) has become an avenue to investigate physical and mechanical properties of geomaterials. DRP offers the advantage of simulating laboratory experiments on numerical samples that are obtained from analytical methods. Potentially, DRP could allow sparing part of the time and resources that are allocated to perform complicated laboratory tests. Like classic laboratory tests, the goal of DRP is to estimate accurately physical properties of rocks like hydraulic permeability or elastic moduli. Nevertheless, the physical properties of samples imaged using micro-computed tomography (μCT) are estimated through segmentation of the μCT dataset. Segmentation proves to be a challenging and arbitrary procedure that typically leads to inaccurate estimates of physical properties. Here we present a novel technique to extract physical properties from a μCT dataset without the use of segmentation. We show examples in which we use segmentation-less method to simulate elastic wave propagation and pressure wave diffusion to estimate elastic properties and permeability, respectively. The proposed method takes advantage of effective medium theories and uses the density and the porosity that are measured in the laboratory to constrain the results. We discuss the results and highlight that segmentation-less DRP is more accurate than segmentation based DRP approaches and theoretical modeling for the studied rock. In conclusion, the segmentation-less approach here presented seems to be a promising method to improve accuracy and to ease the overall workflow of DRP.

GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain

Science.gov (United States)

Huang, Lan; Du, Youfu; Chen, Gongyang

2015-03-01

Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.

Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review

Directory of Open Access Journals (Sweden)

Rezty Amalia Aras

2016-05-01

Full Text Available Diabetic detinopathy (DR is effect of diabetes mellitus to the human vision that is the major cause of blindness. Early diagnosis of DR is an important requirement in diabetes treatment. Retinal fundus image is commonly used to observe the diabetic retinopathy symptoms. It can present retinal features such as blood vessel and also capture the pathologies which may lead to DR. Blood vessel is one of retinal features which can show the retina pathologies. It can be extracted from retinal image by image processing with following stages: pre-processing, segmentation, and post-processing. This paper contains a review of public retinal image dataset and several methods from various conducted researches. All discussed methods are applicable to each researcher cases. There is no further analysis to conclude the best method which can be used for general cases. However, we suggest morphological and multiscale method that gives the best accuracy in segmentation.

A Nonparametric Shape Prior Constrained Active Contour Model for Segmentation of Coronaries in CTA Images

Directory of Open Access Journals (Sweden)

Yin Wang

2014-01-01

Full Text Available We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.

Video segmentation using keywords

Science.gov (United States)

Ton-That, Vinh; Vong, Chi-Tai; Nguyen-Dao, Xuan-Truong; Tran, Minh-Triet

2018-04-01

At DAVIS-2016 Challenge, many state-of-art video segmentation methods achieve potential results, but they still much depend on annotated frames to distinguish between background and foreground. It takes a lot of time and efforts to create these frames exactly. In this paper, we introduce a method to segment objects from video based on keywords given by user. First, we use a real-time object detection system - YOLOv2 to identify regions containing objects that have labels match with the given keywords in the first frame. Then, for each region identified from the previous step, we use Pyramid Scene Parsing Network to assign each pixel as foreground or background. These frames can be used as input frames for Object Flow algorithm to perform segmentation on entire video. We conduct experiments on a subset of DAVIS-2016 dataset in half the size of its original size, which shows that our method can handle many popular classes in PASCAL VOC 2012 dataset with acceptable accuracy, about 75.03%. We suggest widely testing by combining other methods to improve this result in the future.

Cluster Ensemble-Based Image Segmentation

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Xiaoru Wang

2013-07-01

Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

Science.gov (United States)

Guo, Lu; Wang, Ping; Sun, Ranran; Yang, Chengwen; Zhang, Ning; Guo, Yu; Feng, Yuanming

2018-02-19

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

Parallel fuzzy connected image segmentation on GPU

OpenAIRE

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

2011-01-01

Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm impleme...

Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation.

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Ross, James C; San José Estépar, Rail; Kindlmann, Gordon; Díaz, Alejandro; Westin, Carl-Fredrik; Silverman, Edwin K; Washko, George R

2010-01-01

We present a fully automatic lung lobe segmentation algorithm that is effective in high resolution computed tomography (CT) datasets in the presence of confounding factors such as incomplete fissures (anatomical structures indicating lobe boundaries), advanced disease states, high body mass index (BMI), and low-dose scanning protocols. In contrast to other algorithms that leverage segmentations of auxiliary structures (esp. vessels and airways), we rely only upon image features indicating fissure locations. We employ a particle system that samples the image domain and provides a set of candidate fissure locations. We follow this stage with maximum a posteriori (MAP) estimation to eliminate poor candidates and then perform a post-processing operation to remove remaining noise particles. We then fit a thin plate spline (TPS) interpolating surface to the fissure particles to form the final lung lobe segmentation. Results indicate that our algorithm performs comparably to pulmonologist-generated lung lobe segmentations on a set of challenging cases.

Multi-dimension feature fusion for action recognition

Science.gov (United States)

Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

2018-04-01

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network

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Hasan Asy’ari Arief

2018-06-01

Full Text Available Inspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1 First, we implement the multiclass version of the intersection-over-union (IoU loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2 Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3 Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation.

Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring

DEFF Research Database (Denmark)

Kallenberg, Michiel Gijsbertus J.; Petersen, Peter Kersten; Nielsen, Mads

2016-01-01

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used...... as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime...... and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy...

Status of the segment interconnect, cable segment ancillary logic, and the cable segment hybrid driver projects

International Nuclear Information System (INIS)

Swoboda, C.; Barsotti, E.; Chappa, S.; Downing, R.; Goeransson, G.; Lensy, D.; Moore, G.; Rotolo, C.; Urish, J.

1985-01-01

The FASTBUS Segment Interconnect (SI) provides a communication path between two otherwise independent, asynchronous bus segments. In particular, the Segment Interconnect links a backplane crate segment to a cable segment. All standard FASTBUS address and data transactions can be passed through the SI or any number of SIs and segments in a path. Thus systems of arbitrary connection complexity can be formed, allowing simultaneous independent processing, yet still permitting devices associated with one segment to be accessed from others. The model S1 Segment Interconnect and the Cable Segment Ancillary Logic covered in this report comply with all the mandatory features stated in the FASTBUS specification document DOE/ER-0189. A block diagram of the SI is shown

Large scale validation of the M5L lung CAD on heterogeneous CT datasets

Energy Technology Data Exchange (ETDEWEB)

Lopez Torres, E., E-mail: Ernesto.Lopez.Torres@cern.ch, E-mail: cerello@to.infn.it [CEADEN, Havana 11300, Cuba and INFN, Sezione di Torino, Torino 10125 (Italy); Fiorina, E.; Pennazio, F.; Peroni, C. [Department of Physics, University of Torino, Torino 10125, Italy and INFN, Sezione di Torino, Torino 10125 (Italy); Saletta, M.; Cerello, P., E-mail: Ernesto.Lopez.Torres@cern.ch, E-mail: cerello@to.infn.it [INFN, Sezione di Torino, Torino 10125 (Italy); Camarlinghi, N.; Fantacci, M. E. [Department of Physics, University of Pisa, Pisa 56127, Italy and INFN, Sezione di Pisa, Pisa 56127 (Italy)

2015-04-15

Purpose: M5L, a fully automated computer-aided detection (CAD) system for the detection and segmentation of lung nodules in thoracic computed tomography (CT), is presented and validated on several image datasets. Methods: M5L is the combination of two independent subsystems, based on the Channeler Ant Model as a segmentation tool [lung channeler ant model (lungCAM)] and on the voxel-based neural approach. The lungCAM was upgraded with a scan equalization module and a new procedure to recover the nodules connected to other lung structures; its classification module, which makes use of a feed-forward neural network, is based of a small number of features (13), so as to minimize the risk of lacking generalization, which could be possible given the large difference between the size of the training and testing datasets, which contain 94 and 1019 CTs, respectively. The lungCAM (standalone) and M5L (combined) performance was extensively tested on 1043 CT scans from three independent datasets, including a detailed analysis of the full Lung Image Database Consortium/Image Database Resource Initiative database, which is not yet found in literature. Results: The lungCAM and M5L performance is consistent across the databases, with a sensitivity of about 70% and 80%, respectively, at eight false positive findings per scan, despite the variable annotation criteria and acquisition and reconstruction conditions. A reduced sensitivity is found for subtle nodules and ground glass opacities (GGO) structures. A comparison with other CAD systems is also presented. Conclusions: The M5L performance on a large and heterogeneous dataset is stable and satisfactory, although the development of a dedicated module for GGOs detection could further improve it, as well as an iterative optimization of the training procedure. The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large

Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

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M.F.M. Yunoh

2015-06-01

Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

Localized Segment Based Processing for Automatic Building Extraction from LiDAR Data

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Parida, G.; Rajan, K. S.

2017-05-01

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

LOCALIZED SEGMENT BASED PROCESSING FOR AUTOMATIC BUILDING EXTRACTION FROM LiDAR DATA

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G. Parida

2017-05-01

Full Text Available The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

Salient Region Detection via Feature Combination and Discriminative Classifier

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Deming Kong

2015-01-01

Full Text Available We introduce a novel approach to detect salient regions of an image via feature combination and discriminative classifier. Our method, which is based on hierarchical image abstraction, uses the logistic regression approach to map the regional feature vector to a saliency score. Four saliency cues are used in our approach, including color contrast in a global context, center-boundary priors, spatially compact color distribution, and objectness, which is as an atomic feature of segmented region in the image. By mapping a four-dimensional regional feature to fifteen-dimensional feature vector, we can linearly separate the salient regions from the clustered background by finding an optimal linear combination of feature coefficients in the fifteen-dimensional feature space and finally fuse the saliency maps across multiple levels. Furthermore, we introduce the weighted salient image center into our saliency analysis task. Extensive experiments on two large benchmark datasets show that the proposed approach achieves the best performance over several state-of-the-art approaches.

FogBank: a single cell segmentation across multiple cell lines and image modalities.

Science.gov (United States)

Chalfoun, Joe; Majurski, Michael; Dima, Alden; Stuelten, Christina; Peskin, Adele; Brady, Mary

2014-12-30

Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies. We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation. First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce. We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images. FogBank produces single cell segmentation from confluent cell

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM

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Yunyun Liang

2015-01-01

Full Text Available Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM. Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS, segmented PsePSSM, and segmented autocovariance transformation (ACT based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640 are adopted in this paper. Then a 700-dimensional (700D feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA. To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

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Nogol Memari

Full Text Available The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE, Structured Analysis of the Retina (STARE and Child Heart and Health Study in England (CHASE_DB1 datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.

Applying a new unequally weighted feature fusion method to improve CAD performance of classifying breast lesions

Science.gov (United States)

Zargari Khuzani, Abolfazl; Danala, Gopichandh; Heidari, Morteza; Du, Yue; Mashhadi, Najmeh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Higher recall rates are a major challenge in mammography screening. Thus, developing computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role to improve efficacy of mammography screening. Objective of this study is to develop and test a unique image feature fusion framework to improve performance in classifying suspicious mass-like breast lesions depicting on mammograms. The image dataset consists of 302 suspicious masses detected on both craniocaudal and mediolateral-oblique view images. Amongst them, 151 were malignant and 151 were benign. The study consists of following 3 image processing and feature analysis steps. First, an adaptive region growing segmentation algorithm was used to automatically segment mass regions. Second, a set of 70 image features related to spatial and frequency characteristics of mass regions were initially computed. Third, a generalized linear regression model (GLM) based machine learning classifier combined with a bat optimization algorithm was used to optimally fuse the selected image features based on predefined assessment performance index. An area under ROC curve (AUC) with was used as a performance assessment index. Applying CAD scheme to the testing dataset, AUC was 0.75+/-0.04, which was significantly higher than using a single best feature (AUC=0.69+/-0.05) or the classifier with equally weighted features (AUC=0.73+/-0.05). This study demonstrated that comparing to the conventional equal-weighted approach, using an unequal-weighted feature fusion approach had potential to significantly improve accuracy in classifying between malignant and benign breast masses.

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

Science.gov (United States)

Orlando, José Ignacio; Fracchia, Marcos; del Río, Valeria; del Fresno, Mariana

2017-11-01

Several ophthalmological and systemic diseases are manifested through pathological changes in the properties and the distribution of the retinal blood vessels. The characterization of such alterations requires the segmentation of the vasculature, which is a tedious and time-consuming task that is infeasible to be performed manually. Numerous attempts have been made to propose automated methods for segmenting the retinal vasculature from fundus photographs, although their application in real clinical scenarios is usually limited by their ability to deal with images taken at different resolutions. This is likely due to the large number of parameters that have to be properly calibrated according to each image scale. In this paper we propose to apply a novel strategy for automated feature parameter estimation, combined with a vessel segmentation method based on fully connected conditional random fields. The estimation model is learned by linear regression from structural properties of the images and known optimal configurations, that were previously obtained for low resolution data sets. Our experiments in high resolution images show that this approach is able to estimate appropriate configurations that are suitable for performing the segmentation task without requiring to re-engineer parameters. Furthermore, our combined approach reported state of the art performance on the benchmark data set HRF, as measured in terms of the F1-score and the Matthews correlation coefficient.

Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

Science.gov (United States)

Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

2015-12-01

The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

Metric Learning for Hyperspectral Image Segmentation

Science.gov (United States)

Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

2011-01-01

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Bayesian automated cortical segmentation for neonatal MRI

Science.gov (United States)

Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha

2017-11-01

Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.

Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

Science.gov (United States)

Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

2013-01-01

Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

Directory of Open Access Journals (Sweden)

Florian Eyben

Full Text Available Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

Improved document image segmentation algorithm using multiresolution morphology

Science.gov (United States)

Bukhari, Syed Saqib; Shafait, Faisal; Breuel, Thomas M.

2011-01-01

Page segmentation into text and non-text elements is an essential preprocessing step before optical character recognition (OCR) operation. In case of poor segmentation, an OCR classification engine produces garbage characters due to the presence of non-text elements. This paper describes modifications to the text/non-text segmentation algorithm presented by Bloomberg,1 which is also available in his open-source Leptonica library.2The modifications result in significant improvements and achieved better segmentation accuracy than the original algorithm for UW-III, UNLV, ICDAR 2009 page segmentation competition test images and circuit diagram datasets.

Volumetric multimodality neural network for brain tumor segmentation

Science.gov (United States)

Silvana Castillo, Laura; Alexandra Daza, Laura; Carlos Rivera, Luis; Arbeláez, Pablo

2017-11-01

Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.

Benchmark for license plate character segmentation

Science.gov (United States)

Gonçalves, Gabriel Resende; da Silva, Sirlene Pio Gomes; Menotti, David; Shwartz, William Robson

2016-09-01

Automatic license plate recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plate detection, segmentation of license plate characters, and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the license plate character segmentation (LPCS) step, because its effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-centroid coefficient, an evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2000 Brazilian license plates consisting of 14000 alphanumeric symbols and their corresponding bounding box annotations. We also present a straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on five LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.

Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images.

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Ji, Zexuan; Chen, Qiang; Niu, Sijie; Leng, Theodore; Rubin, Daniel L

2018-01-01

To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation. An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the training phase, the labeled A-scans with 1024 features were directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier was trained to determine the label of each individual pixel. Finally, a voting decision strategy was used to refine the segmentation results among 10 trained models. Two image data sets with GA were used to evaluate the model. For the first dataset, our algorithm obtained a mean overlap ratio (OR) 86.94% ± 8.75%, absolute area difference (AAD) 11.49% ± 11.50%, and correlation coefficients (CC) 0.9857; for the second dataset, the mean OR, AAD, and CC of the proposed method were 81.66% ± 10.93%, 8.30% ± 9.09%, and 0.9952, respectively. The proposed algorithm was capable of improving over 5% and 10% segmentation accuracy, respectively, when compared with several state-of-the-art algorithms on two data sets. Without retinal layer segmentation, the proposed algorithm could produce higher segmentation accuracy and was more stable when compared with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable GA segmentations from SD-OCT images and be useful in the clinical diagnosis of advanced nonexudative AMD. Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD.

CAMSHIFT IMPROVEMENT WITH MEAN-SHIFT SEGMENTATION, REGION GROWING, AND SURF METHOD

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Ferdinan Ferdinan

2013-10-01

Full Text Available CAMSHIFT algorithm has been widely used in object tracking. CAMSHIFT utilizescolor features as the model object. Thus, original CAMSHIFT may fail when the object color issimilar with the background color. In this study, we propose CAMSHIFT tracker combined withmean-shift segmentation, region growing, and SURF in order to improve the tracking accuracy.The mean-shift segmentation and region growing are applied in object localization phase to extractthe important parts of the object. Hue-distance, saturation, and value are used to calculate theBhattacharyya distance to judge whether the tracked object is lost. Once the object is judged lost,SURF is used to find the lost object, and CAMSHIFT can retrack the object. The Object trackingsystem is built with OpenCV. Some measurements of accuracy have done using frame-basedmetrics. We use datasets BoBoT (Bonn Benchmark on Tracking to measure accuracy of thesystem. The results demonstrate that CAMSHIFT combined with mean-shift segmentation, regiongrowing, and SURF method has higher accuracy than the previous methods.

A Large-Scale 3D Object Recognition dataset

DEFF Research Database (Denmark)

Sølund, Thomas; Glent Buch, Anders; Krüger, Norbert

2016-01-01

geometric groups; concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching...... performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat...

Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation

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Richard eBeare

2016-03-01

Full Text Available Measuring the distribution of brain tissue types (tissue classification in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation, which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF, hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks’ gestation acquired at 30 weeks’ corrected gestational age (n= 5, coronal T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5 and axial T2-weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n= 5. The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR group, consisted of T2-weighted images of preterm infants (born <30 weeks’ gestation acquired shortly after birth (n= 12, preterm infants acquired at term-equivalent age (n= 12, and healthy term-born infants (born ≥38 weeks’ gestation acquired within the first nine days of life (n= 12. For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

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Maíla de Lima Claro

2016-08-01

Full Text Available The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classiffication of images in glaucomatous or not. We obtained results of 93% accuracy.

Topic modeling for cluster analysis of large biological and medical datasets.

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Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

Recognition of sport players' numbers using fast-color segmentation

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Verleysen, Cédric; De Vleeschouwer, Christophe

2012-01-01

This paper builds on a prior work for player detection, and proposes an efficient and effective method to distinguish among players based on the numbers printed on their jerseys. To extract the numbers, the dominant colors of the jersey are learnt during an initial phase and used to speed up the segmentation of the candidate digit regions. An additional set of criteria, considering the relative position and size (compared to the player bounding box) and the density (compared to the digit rectangular support) of the digit, are used to filter out the regions that obviously do not correspond to a digit. Once the plausible digit regions have been extracted, their recognition is based on feature-based classification. A number of original features are proposed to increase the robustness against digit appearance changes, resulting from the font thickness variability and from the deformations of the jersey during the game. Finally, the efficiency and the effectiveness of the proposed method are demonstrated on a real-life basketball dataset. It shows that the proposed segmentation runs about ten times faster than the mean-shift algorithm, but also outlines that the proposed additional features significantly increase the digit recognition accuracy. Despite significant deformations, 40% of the samples, that can be visually recognized as digits, are well classified as numbers. Out of these classified samples, more than 80% of them are correctly recognized. Besides, more than 95% of the samples, that are not numbers, are correctly identified as non-numbers.

TU-H-CAMPUS-IeP3-01: Simultaneous PET Restoration and PET/CT Co-Segmentation Using a Variational Method

International Nuclear Information System (INIS)

Li, L; Tan, S; Lu, W

2016-01-01

Purpose: PET images are usually blurred due to the finite spatial resolution, while CT images suffer from low contrast. Segment a tumor from either a single PET or CT image is thus challenging. To make full use of the complementary information between PET and CT, we propose a novel variational method for simultaneous PET image restoration and PET/CT images co-segmentation. Methods: The proposed model was constructed based on the Γ-convergence approximation of Mumford-Shah (MS) segmentation model for PET/CT co-segmentation. Moreover, a PET de-blur process was integrated into the MS model to improve the segmentation accuracy. An interaction edge constraint term over the two modalities were specially designed to share the complementary information. The energy functional was iteratively optimized using an alternate minimization (AM) algorithm. The performance of the proposed method was validated on ten lung cancer cases and five esophageal cancer cases. The ground truth were manually delineated by an experienced radiation oncologist using the complementary visual features of PET and CT. The segmentation accuracy was evaluated by Dice similarity index (DSI) and volume error (VE). Results: The proposed method achieved an expected restoration result for PET image and satisfactory segmentation results for both PET and CT images. For lung cancer dataset, the average DSI (0.72) increased by 0.17 and 0.40 than single PET and CT segmentation. For esophageal cancer dataset, the average DSI (0.85) increased by 0.07 and 0.43 than single PET and CT segmentation. Conclusion: The proposed method took full advantage of the complementary information from PET and CT images. This work was supported in part by the National Cancer Institute Grants R01CA172638. Shan Tan and Laquan Li were supported in part by the National Natural Science Foundation of China, under Grant Nos. 60971112 and 61375018.

Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

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Vidyaratne, L; Alam, M; Shboul, Z; Iftekharuddin, K M

2018-01-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Deep learning and texture-based semantic label fusion for brain tumor segmentation

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Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K. M.

2018-02-01

Brain tumor segmentation is a fundamental step in surgical treatment and therapy. Many hand-crafted and learning based methods have been proposed for automatic brain tumor segmentation from MRI. Studies have shown that these approaches have their inherent advantages and limitations. This work proposes a semantic label fusion algorithm by combining two representative state-of-the-art segmentation algorithms: texture based hand-crafted, and deep learning based methods to obtain robust tumor segmentation. We evaluate the proposed method using publicly available BRATS 2017 brain tumor segmentation challenge dataset. The results show that the proposed method offers improved segmentation by alleviating inherent weaknesses: extensive false positives in texture based method, and the false tumor tissue classification problem in deep learning method, respectively. Furthermore, we investigate the effect of patient's gender on the segmentation performance using a subset of validation dataset. Note the substantial improvement in brain tumor segmentation performance proposed in this work has recently enabled us to secure the first place by our group in overall patient survival prediction task at the BRATS 2017 challenge.

Functional measurements based on feature tracking of cine magnetic resonance images identify left ventricular segments with myocardial scar

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Nylander Eva

2009-11-01

Full Text Available Abstract Background The aim of the study was to perform a feature tracking analysis on cine magnetic resonance (MR images to elucidate if functional measurements of the motion of the left ventricular wall may detect scar defined with gadolinium enhanced MR. Myocardial contraction can be measured in terms of the velocity, displacement and local deformation (strain of a particular myocardial segment. Contraction of the myocardial wall will be reduced in the presence of scar and as a consequence of reduced myocardial blood flow. Methods Thirty patients (3 women and 27 men were selected based on the presence or absence of extensive scar in the anteroseptal area of the left ventricle. The patients were investigated in stable clinical condition, 4-8 weeks post ST-elevation myocardial infarction treated with percutaneous coronary intervention. Seventeen had a scar area >75% in at least one anteroseptal segment (scar and thirteen had scar area Results In the scar patients, segments with scar showed lower functional measurements than remote segments. Radial measurements of velocity, displacement and strain performed better in terms of receiver-operator-characteristic curves (ROC than the corresponding longitudinal measurements. The best area-under-curve was for radial strain, 0.89, where a cut-off value of 38.8% had 80% sensitivity and 86% specificity for the detection of a segment with scar area >50%. As a percentage of the mean, intraobserver variability was 16-14-26% for radial measurements of displacement-velocity-strain and corresponding interobserver variability was 13-12-18%. Conclusion Feature tracking analysis of cine-MR displays velocity, displacement and strain in the radial and longitudinal direction and may be used for the detection of transmural scar. The accuracy and repeatability of the radial functional measurements is satisfactory and global measures agree.

Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features

International Nuclear Information System (INIS)

Mahmood, F.H.

2012-01-01

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer.The calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years. This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two to stages performed using the minimum distance (M D) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLC M for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from A L-llwiya Hospital in Baghdad, Iraq.The proposed technique shows better results.

Multiple Scale Music Segmentation Using Rhythm, Timbre, and Harmony

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Kristoffer Jensen

2007-01-01

Full Text Available The segmentation of music into intro-chorus-verse-outro, and similar segments, is a difficult topic. A method for performing automatic segmentation based on features related to rhythm, timbre, and harmony is presented, and compared, between the features and between the features and manual segmentation of a database of 48 songs. Standard information retrieval performance measures are used in the comparison, and it is shown that the timbre-related feature performs best.

尖锐特征诱导的点云自动分片算法%Automatic Sharp Feature Based Segmentation of Point Clouds

Institute of Scientific and Technical Information of China (English)

邹冬; 庞明勇

2012-01-01

点云模型的分片技术是数字几何处理领域的基础技术之一.提出一种尖锐特征诱导的点云模型自动分片算法.算法首先计算点云模型的局部微分属性,并以此来识别模型上的尖锐特征点；然后采用改进的折线生长算法生成并完善特征折线,并基于特征折线采用三次B样条曲线来逼近的尖锐特征点；最后采用区域生长方法将点云模型分割成多个几何特征单一、边界整齐的点云数据面片.实验表明,本文算法运行稳定,可以准确地分割点云模型.该算法可用于点云模型的形状匹配、纹理映射、CAD建模、以及逆向工程等应用中.%Segmentation of point clouds is one of basic and key technologies in digital geometry processing. In this paper, based on extracted sharp features, we present a method for automatic ally segmenting point clouds. Our algorithm first calculates local surface differentials features and uses them to identify sharp feature points. And an improved feature-ployline propagation technique is employed to approximate the feature points by a set of polylines and optimize the feature curves. Then, based on feature ploy lines, we approximate the sharp feature points by cubic B-spline curve. Subsequently, based on the extracted feature curves, region growing algorithm was applied to segment the point clouds into multiple regions, the geometric feature of the region is consistent and the boundary of the patch is neat. Experiments show that the algorithm can segment the point clouds precisely and efficiently. Our algorithm can be used in shape matching, texture mapping, CAD modeling and reverse engineering.

Automatic segmentation of psoriasis lesions

Science.gov (United States)

Ning, Yang; Shi, Chenbo; Wang, Li; Shu, Chang

2014-10-01

The automatic segmentation of psoriatic lesions is widely researched these years. It is an important step in Computer-aid methods of calculating PASI for estimation of lesions. Currently those algorithms can only handle single erythema or only deal with scaling segmentation. In practice, scaling and erythema are often mixed together. In order to get the segmentation of lesions area - this paper proposes an algorithm based on Random forests with color and texture features. The algorithm has three steps. The first step, the polarized light is applied based on the skin's Tyndall-effect in the imaging to eliminate the reflection and Lab color space are used for fitting the human perception. The second step, sliding window and its sub windows are used to get textural feature and color feature. In this step, a feature of image roughness has been defined, so that scaling can be easily separated from normal skin. In the end, Random forests will be used to ensure the generalization ability of the algorithm. This algorithm can give reliable segmentation results even the image has different lighting conditions, skin types. In the data set offered by Union Hospital, more than 90% images can be segmented accurately.

Robust and automated three-dimensional segmentation of densely packed cell nuclei in different biological specimens with Lines-of-Sight decomposition.

Science.gov (United States)

Mathew, B; Schmitz, A; Muñoz-Descalzo, S; Ansari, N; Pampaloni, F; Stelzer, E H K; Fischer, S C

2015-06-08

Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91%) whereas the other methods each failed for at least one dataset (F-measure≤69%). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. We developed a novel and

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

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Lei Ma

2016-09-01

Full Text Available Object-based change detection (OBCD has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

Segmentation of 3D ultrasound computer tomography reflection images using edge detection and surface fitting

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Hopp, T.; Zapf, M.; Ruiter, N. V.

2014-03-01

An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, as well as for the use in further image processing, is to segment the breast from the background. In this work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for manual corrections of the automated breast boundary detection. The processing time for the segmentation of an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our segmentation approach.

Interactive segmentation: a scalable superpixel-based method

Science.gov (United States)

Mathieu, Bérengère; Crouzil, Alain; Puel, Jean-Baptiste

2017-11-01

This paper addresses the problem of interactive multiclass segmentation of images. We propose a fast and efficient new interactive segmentation method called superpixel α fusion (SαF). From a few strokes drawn by a user over an image, this method extracts relevant semantic objects. To get a fast calculation and an accurate segmentation, SαF uses superpixel oversegmentation and support vector machine classification. We compare SαF with competing algorithms by evaluating its performances on reference benchmarks. We also suggest four new datasets to evaluate the scalability of interactive segmentation methods, using images from some thousand to several million pixels. We conclude with two applications of SαF.

A combination of compositional index and genetic algorithm for predicting transmembrane helical segments.

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Nazar Zaki

Full Text Available Transmembrane helix (TMH topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method.The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

International Nuclear Information System (INIS)

Harmon, S; Wendelberger, B; Jeraj, R

2014-01-01

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [ 18 F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI mean = 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI range : 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Harmon, S; Wendelberger, B [University of Wisconsin-Madison, Madison, WI (United States); Jeraj, R [University of Wisconsin-Madison, Madison, WI (United States); University of Ljubljana (Slovenia)

2014-06-01

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms

Proliferative diabetic retinopathy characterization based on fractal features: Evaluation on a publicly available dataset.

Science.gov (United States)

Orlando, José Ignacio; van Keer, Karel; Barbosa Breda, João; Manterola, Hugo Luis; Blaschko, Matthew B; Clausse, Alejandro

2017-12-01

Diabetic retinopathy (DR) is one of the most widespread causes of preventable blindness in the world. The most dangerous stage of this condition is proliferative DR (PDR), in which the risk of vision loss is high and treatments are less effective. Fractal features of the retinal vasculature have been previously explored as potential biomarkers of DR, yet the current literature is inconclusive with respect to their correlation with PDR. In this study, we experimentally assess their discrimination ability to recognize PDR cases. A statistical analysis of the viability of using three reference fractal characterization schemes - namely box, information, and correlation dimensions - to identify patients with PDR is presented. These descriptors are also evaluated as input features for training ℓ1 and ℓ2 regularized logistic regression classifiers, to estimate their performance. Our results on MESSIDOR, a public dataset of 1200 fundus photographs, indicate that patients with PDR are more likely to exhibit a higher fractal dimension than healthy subjects or patients with mild levels of DR (P≤1.3×10-2). Moreover, a supervised classifier trained with both fractal measurements and red lesion-based features reports an area under the ROC curve of 0.93 for PDR screening and 0.96 for detecting patients with optic disc neovascularizations. The fractal dimension of the vasculature increases with the level of DR. Furthermore, PDR screening using multiscale fractal measurements is more feasible than using their derived fractal dimensions. Code and further resources are provided at https://github.com/ignaciorlando/fundus-fractal-analysis. © 2017 American Association of Physicists in Medicine.

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Directory of Open Access Journals (Sweden)

David Vázquez

2017-01-01

Full Text Available Colorectal cancer (CRC is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs. We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

Directory of Open Access Journals (Sweden)

Eman Magdy

2015-01-01

Full Text Available Computer-aided diagnostic (CAD systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients’ lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR for classification step. Finally, K-nearest neighbour (KNN, support vector machine (SVM, naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

Science.gov (United States)

Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

2013-01-01

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

A segmentation approach for a delineation of terrestrial ecoregions

Science.gov (United States)

Nowosad, J.; Stepinski, T.

2017-12-01

Terrestrial ecoregions are the result of regionalization of land into homogeneous units of similar ecological and physiographic features. Terrestrial Ecoregions of the World (TEW) is a commonly used global ecoregionalization based on expert knowledge and in situ observations. Ecological Land Units (ELUs) is a global classification of 250 meters-sized cells into 4000 types on the basis of the categorical values of four environmental variables. ELUs are automatically calculated and reproducible but they are not a regionalization which makes them impractical for GIS-based spatial analysis and for comparison with TEW. We have regionalized terrestrial ecosystems on the basis of patterns of the same variables (land cover, soils, landform, and bioclimate) previously used in ELUs. Considering patterns of categorical variables makes segmentation and thus regionalization possible. Original raster datasets of the four variables are first transformed into regular grids of square-sized blocks of their cells called eco-sites. Eco-sites are elementary land units containing local patterns of physiographic characteristics and thus assumed to contain a single ecosystem. Next, eco-sites are locally aggregated using a procedure analogous to image segmentation. The procedure optimizes pattern homogeneity of all four environmental variables within each segment. The result is a regionalization of the landmass into land units characterized by uniform pattern of land cover, soils, landforms, climate, and, by inference, by uniform ecosystem. Because several disjoined segments may have very similar characteristics, we cluster the segments to obtain a smaller set of segment types which we identify with ecoregions. Our approach is automatic, reproducible, updatable, and customizable. It yields the first automatic delineation of ecoregions on the global scale. In the resulting vector database each ecoregion/segment is described by numerous attributes which make it a valuable GIS resource for

Newton SSANTA Dr Water using POU filters dataset

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset contains information about all the features extracted from the raw data files, the formulas that were assigned to some of these features, and the...

Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis

Directory of Open Access Journals (Sweden)

Baidaa Al-Bander

2018-03-01

Full Text Available Glaucoma is a group of eye diseases which can cause vision loss by damaging the optic nerve. Early glaucoma detection is key to preventing vision loss yet there is a lack of noticeable early symptoms. Colour fundus photography allows the optic disc (OD to be examined to diagnose glaucoma. Typically, this is done by measuring the vertical cup-to-disc ratio (CDR; however, glaucoma is characterised by thinning of the rim asymmetrically in the inferior-superior-temporal-nasal regions in increasing order. Automatic delineation of the OD features has potential to improve glaucoma management by allowing for this asymmetry to be considered in the measurements. Here, we propose a new deep-learning-based method to segment the OD and optic cup (OC. The core of the proposed method is DenseNet with a fully-convolutional network, whose symmetric U-shaped architecture allows pixel-wise classification. The predicted OD and OC boundaries are then used to estimate the CDR on two axes for glaucoma diagnosis. We assess the proposed method’s performance using a large retinal colour fundus dataset, outperforming state-of-the-art segmentation methods. Furthermore, we generalise our method to segment four fundus datasets from different devices without further training, outperforming the state-of-the-art on two and achieving comparable results on the remaining two.

Visualization of conserved structures by fusing highly variable datasets.

Science.gov (United States)

Silverstein, Jonathan C; Chhadia, Ankur; Dech, Fred

2002-01-01

Skill, effort, and time are required to identify and visualize anatomic structures in three-dimensions from radiological data. Fundamentally, automating these processes requires a technique that uses symbolic information not in the dynamic range of the voxel data. We were developing such a technique based on mutual information for automatic multi-modality image fusion (MIAMI Fuse, University of Michigan). This system previously demonstrated facility at fusing one voxel dataset with integrated symbolic structure information to a CT dataset (different scale and resolution) from the same person. The next step of development of our technique was aimed at accommodating the variability of anatomy from patient to patient by using warping to fuse our standard dataset to arbitrary patient CT datasets. A standard symbolic information dataset was created from the full color Visible Human Female by segmenting the liver parenchyma, portal veins, and hepatic veins and overwriting each set of voxels with a fixed color. Two arbitrarily selected patient CT scans of the abdomen were used for reference datasets. We used the warping functions in MIAMI Fuse to align the standard structure data to each patient scan. The key to successful fusion was the focused use of multiple warping control points that place themselves around the structure of interest automatically. The user assigns only a few initial control points to align the scans. Fusion 1 and 2 transformed the atlas with 27 points around the liver to CT1 and CT2 respectively. Fusion 3 transformed the atlas with 45 control points around the liver to CT1 and Fusion 4 transformed the atlas with 5 control points around the portal vein. The CT dataset is augmented with the transformed standard structure dataset, such that the warped structure masks are visualized in combination with the original patient dataset. This combined volume visualization is then rendered interactively in stereo on the ImmersaDesk in an immersive Virtual

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

Science.gov (United States)

Nasir, Muhammad; Attique Khan, Muhammad; Sharif, Muhammad; Lali, Ikram Ullah; Saba, Tanzila; Iqbal, Tassawar

2018-02-21

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods. © 2018 Wiley Periodicals, Inc.

Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model.

Science.gov (United States)

Lin, P L; Huang, P W; Huang, P Y; Hsu, H C

2015-10-01

Periodontitis involves progressive loss of alveolar bone around the teeth. Hence, automatic alveolar bone-loss (ABL) measurement in periapical radiographs can assist dentists in diagnosing such disease. In this paper, we propose an effective method for ABL area localization and denote it as ABLIfBm. ABLIfBm is a threshold segmentation method that uses a hybrid feature fused of both intensity and texture measured by the H-value of fractional Brownian motion (fBm) model, where the H-value is the Hurst coefficient in the expectation function of a fBm curve (intensity change) and is directly related to the value of fractal dimension. Adopting leave-one-out cross validation training and testing mechanism, ABLIfBm trains weights for both features using Bayesian classifier and transforms the radiograph image into a feature image obtained from a weighted average of both features. Finally, by Otsu's thresholding, it segments the feature image into normal and bone-loss regions. Experimental results on 31 periodontitis radiograph images in terms of mean true positive fraction and false positive fraction are about 92.5% and 14.0%, respectively, where the ground truth is provided by a dentist. The results also demonstrate that ABLIfBm outperforms (a) the threshold segmentation method using either feature alone or a weighted average of the same two features but with weights trained differently; (b) a level set segmentation method presented earlier in literature; and (c) segmentation methods based on Bayesian, K-NN, or SVM classifier using the same two features. Our results suggest that the proposed method can effectively localize alveolar bone-loss areas in periodontitis radiograph images and hence would be useful for dentists in evaluating degree of bone-loss for periodontitis patients. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences.

Science.gov (United States)

Gloger, Oliver; Bülow, Robin; Tönnies, Klaus; Völzke, Henry

2017-11-24

We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences. Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences. The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences. This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

Science.gov (United States)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Deformable meshes for medical image segmentation accurate automatic segmentation of anatomical structures

CERN Document Server

Kainmueller, Dagmar

2014-01-01

? Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author's core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatom

MITK-based segmentation of co-registered MRI for subject-related regional anesthesia simulation

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Teich, Christian; Liao, Wei; Ullrich, Sebastian; Kuhlen, Torsten; Ntouba, Alexandre; Rossaint, Rolf; Ullisch, Marcus; Deserno, Thomas M.

2008-03-01

With a steadily increasing indication, regional anesthesia is still trained directly on the patient. To develop a virtual reality (VR)-based simulation, a patient model is needed containing several tissues, which have to be extracted from individual magnet resonance imaging (MRI) volume datasets. Due to the given modality and the different characteristics of the single tissues, an adequate segmentation can only be achieved by using a combination of segmentation algorithms. In this paper, we present a framework for creating an individual model from MRI scans of the patient. Our work splits in two parts. At first, an easy-to-use and extensible tool for handling the segmentation task on arbitrary datasets is provided. The key idea is to let the user create a segmentation for the given subject by running different processing steps in a purposive order and store them in a segmentation script for reuse on new datasets. For data handling and visualization, we utilize the Medical Imaging Interaction Toolkit (MITK), which is based on the Visualization Toolkit (VTK) and the Insight Segmentation and Registration Toolkit (ITK). The second part is to find suitable segmentation algorithms and respectively parameters for differentiating the tissues required by the RA simulation. For this purpose, a fuzzy c-means clustering algorithm combined with mathematical morphology operators and a geometric active contour-based approach is chosen. The segmentation process itself aims at operating with minimal user interaction, and the gained model fits the requirements of the simulation. First results are shown for both, male and female MRI of the pelvis.

Segmentation of lung fields using Chan-Vese active contour model in chest radiographs

Science.gov (United States)

Sohn, Kiwon

2011-03-01

A CAD tool for chest radiographs consists of several procedures and the very first step is segmentation of lung fields. We develop a novel methodology for segmentation of lung fields in chest radiographs that can satisfy the following two requirements. First, we aim to develop a segmentation method that does not need a training stage with manual estimation of anatomical features in a large training dataset of images. Secondly, for the ease of implementation, it is desirable to apply a well established model that is widely used for various image-partitioning practices. The Chan-Vese active contour model, which is based on Mumford-Shah functional in the level set framework, is applied for segmentation of lung fields. With the use of this model, segmentation of lung fields can be carried out without detailed prior knowledge on the radiographic anatomy of the chest, yet in some chest radiographs, the trachea regions are unfavorably segmented out in addition to the lung field contours. To eliminate artifacts from the trachea, we locate the upper end of the trachea, find a vertical center line of the trachea and delineate it, and then brighten the trachea region to make it less distinctive. The segmentation process is finalized by subsequent morphological operations. We randomly select 30 images from the Japanese Society of Radiological Technology image database to test the proposed methodology and the results are shown. We hope our segmentation technique can help to promote of CAD tools, especially for emerging chest radiographic imaging techniques such as dual energy radiography and chest tomosynthesis.

Fabric defect detection based on visual saliency using deep feature and low-rank recovery

Science.gov (United States)

Liu, Zhoufeng; Wang, Baorui; Li, Chunlei; Li, Bicao; Dong, Yan

2018-04-01

Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.

Handwriting segmentation of unconstrained Oriya text

Indian Academy of Sciences (India)

Based on vertical projection profiles and structural features of Oriya characters, text lines are segmented into words. For character segmentation, at first, the isolated and connected (touching) characters in a word are detected. Using structural, topological and water reservoir concept-based features, characters of the word ...

Unsupervised tattoo segmentation combining bottom-up and top-down cues

Science.gov (United States)

Allen, Josef D.; Zhao, Nan; Yuan, Jiangbo; Liu, Xiuwen

2011-06-01

Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a figureground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is efficient and suitable for further tattoo classification and retrieval purpose.

Auto-Segmentation of Head and Neck Cancer using Textural features

DEFF Research Database (Denmark)

Hollensen, Christian; Jørgensen, Peter Stanley; Højgaard, Liselotte

- and intra observer variability. Several automatic segmentation methods have been developed using positron emission tomography (PET) and/or computerised tomography (CT). The aim of the present study is to develop a model for 3-dimensional auto-segmentation, the level set method, to contour gross tumour...

Cellular image segmentation using n-agent cooperative game theory

Science.gov (United States)

Dimock, Ian B.; Wan, Justin W. L.

2016-03-01

Image segmentation is an important problem in computer vision and has significant applications in the segmentation of cellular images. Many different imaging techniques exist and produce a variety of image properties which pose difficulties to image segmentation routines. Bright-field images are particularly challenging because of the non-uniform shape of the cells, the low contrast between cells and background, and imaging artifacts such as halos and broken edges. Classical segmentation techniques often produce poor results on these challenging images. Previous attempts at bright-field imaging are often limited in scope to the images that they segment. In this paper, we introduce a new algorithm for automatically segmenting cellular images. The algorithm incorporates two game theoretic models which allow each pixel to act as an independent agent with the goal of selecting their best labelling strategy. In the non-cooperative model, the pixels choose strategies greedily based only on local information. In the cooperative model, the pixels can form coalitions, which select labelling strategies that benefit the entire group. Combining these two models produces a method which allows the pixels to balance both local and global information when selecting their label. With the addition of k-means and active contour techniques for initialization and post-processing purposes, we achieve a robust segmentation routine. The algorithm is applied to several cell image datasets including bright-field images, fluorescent images and simulated images. Experiments show that the algorithm produces good segmentation results across the variety of datasets which differ in cell density, cell shape, contrast, and noise levels.

PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

Directory of Open Access Journals (Sweden)

S. Devaraju

2014-04-01

Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

A Pareto-based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets.

Science.gov (United States)

Fernández, Alberto; Carmona, Cristobal José; José Del Jesus, María; Herrera, Francisco

2017-09-01

Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an additional effort to be addressed. In this research, we overcome these problems by carrying out a combination between feature and instance selections. Feature selection will allow simplifying the overlapping areas easing the generation of rules to distinguish among the classes. Selection of instances from all classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as possibly removing noise and difficult borderline examples. For the sake of obtaining an optimal joint set of features and instances, we embedded the searching for both parameters in a Multi-Objective Evolutionary Algorithm, using the C4.5 decision tree as baseline classifier in this wrapper approach. The multi-objective scheme allows taking a double advantage: the search space becomes broader, and we may provide a set of different solutions in order to build an ensemble of classifiers. This proposal has been contrasted versus several state-of-the-art solutions on imbalanced classification showing excellent results in both binary and multi-class problems.

General Purpose Multimedia Dataset - GarageBand 2008

DEFF Research Database (Denmark)

Meng, Anders

This document describes a general purpose multimedia data-set to be used in cross-media machine learning problems. In more detail we describe the genre taxonomy applied at http://www.garageband.com, from where the data-set was collected, and how the taxonomy have been fused into a more human...... understandable taxonomy. Finally, a description of various features extracted from both the audio and text are presented....

Inferior vena cava segmentation with parameter propagation and graph cut.

Science.gov (United States)

Yan, Zixu; Chen, Feng; Wu, Fa; Kong, Dexing

2017-09-01

The inferior vena cava (IVC) is one of the vital veins inside the human body. Accurate segmentation of the IVC from contrast-enhanced CT images is of great importance. This extraction not only helps the physician understand its quantitative features such as blood flow and volume, but also it is helpful during the hepatic preoperative planning. However, manual delineation of the IVC is time-consuming and poorly reproducible. In this paper, we propose a novel method to segment the IVC with minimal user interaction. The proposed method performs the segmentation block by block between user-specified beginning and end masks. At each stage, the proposed method builds the segmentation model based on information from image regional appearances, image boundaries, and a prior shape. The intensity range and the prior shape for this segmentation model are estimated based on the segmentation result from the last block, or from user- specified beginning mask if at first stage. Then, the proposed method minimizes the energy function and generates the segmentation result for current block using graph cut. Finally, a backward tracking step from the end of the IVC is performed if necessary. We have tested our method on 20 clinical datasets and compared our method to three other vessel extraction approaches. The evaluation was performed using three quantitative metrics: the Dice coefficient (Dice), the mean symmetric distance (MSD), and the Hausdorff distance (MaxD). The proposed method has achieved a Dice of [Formula: see text], an MSD of [Formula: see text] mm, and a MaxD of [Formula: see text] mm, respectively, in our experiments. The proposed approach can achieve a sound performance with a relatively low computational cost and a minimal user interaction. The proposed algorithm has high potential to be applied for the clinical applications in the future.

MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION

Directory of Open Access Journals (Sweden)

X. J. Li

2018-04-01

Full Text Available The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentation rather than through a simple square window. Since PCNN segmentation agrees with the human visual system, the proposed method shows better spectral consistency. Our experiments, which have been carried out for both suburban and urban datasets, demonstrate that the proposed method outperforms other methods in multispectral pansharpening.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

International Nuclear Information System (INIS)

Martin, Spencer; Rodrigues, George; Gaede, Stewart; Brophy, Mark; Barron, John L; Beauchemin, Steven S; Palma, David; Louie, Alexander V; Yu, Edward; Yaremko, Brian; Ahmad, Belal

2015-01-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development. (paper)

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

Science.gov (United States)

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L.; Beauchemin, Steven S.; Rodrigues, George; Gaede, Stewart

2015-02-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology.

Science.gov (United States)

Held, Christian; Wenzel, Jens; Webel, Rike; Marschall, Manfred; Lang, Roland; Palmisano, Ralf; Wittenberg, Thomas

2011-01-01

In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.

Ultrafast superpixel segmentation of large 3D medical datasets

Science.gov (United States)

Leblond, Antoine; Kauffmann, Claude

2016-03-01

Even with recent hardware improvements, superpixel segmentation of large 3D medical images at interactive speed (Gauss-Seidel like acceleration. The work unit partitioning scheme will however vary on odd- and even-numbered iterations to reduce convergence barriers. Synchronization will be ensured by an 8-step 3D variant of the traditional Red Black Ordering scheme. An attack model and early termination will also be described and implemented as additional acceleration techniques. Using our hybrid framework and typical operating parameters, we were able to compute the superpixels of a high-resolution 512x512x512 aortic angioCT scan in 283 ms using a AMD R9 290X GPU. We achieved a 22.3X speed-up factor compared to the published reference GPU implementation.

Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

Science.gov (United States)

Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

1996-04-01

The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

Automated fetal brain segmentation from 2D MRI slices for motion correction.

Science.gov (United States)

Keraudren, K; Kuklisova-Murgasova, M; Kyriakopoulou, V; Malamateniou, C; Rutherford, M A; Kainz, B; Hajnal, J V; Rueckert, D

2014-11-01

Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing. Copyright �

WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION

Directory of Open Access Journals (Sweden)

P. Mathivanan

2014-02-01

Full Text Available In this paper we develop a system for writer identification which involves four processing steps like preprocessing, segmentation, feature extraction and writer identification using neural network. In the preprocessing phase the handwritten text is subjected to slant removal process for segmentation and feature extraction. After this step the text image enters into the process of noise removal and gray level conversion. The preprocessed image is further segmented by using morphological watershed algorithm, where the text lines are segmented into single words and then into single letters. The segmented image is feature extracted by Daubechies’5/3 integer wavelet transform to reduce training complexity [1, 6]. This process is lossless and reversible [10], [14]. These extracted features are given as input to our neural network for writer identification process and a target image is selected for each training process in the 2-layer neural network. With the several trained output data obtained from different target help in text identification. It is a multilingual text analysis which provides simple and efficient text segmentation.

WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

International Nuclear Information System (INIS)

Gensheimer, M; Trister, A; Ermoian, R; Hawkins, D

2014-01-01

Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was

Segmentation Scheme for Safety Enhancement of Engineered Safety Features Component Control System

International Nuclear Information System (INIS)

Lee, Sangseok; Sohn, Kwangyoung; Lee, Junku; Park, Geunok

2013-01-01

Common Caused Failure (CCF) or undetectable failure would adversely impact safety functions of ESF-CCS in the existing nuclear power plants. We propose the segmentation scheme to solve these problems. Main function assignment to segments in the proposed segmentation scheme is based on functional dependency and critical function success path by using the dependency depth matrix. The segment has functional independence and physical isolation. The segmentation structure is that prohibit failure propagation to others from undetectable failures. Therefore, the segmentation system structure has robustness to undetectable failures. The segmentation system structure has functional diversity. The specific function in the segment defected by CCF, the specific function could be maintained by diverse control function that assigned to other segments. Device level control signals and system level control signals are separated and also control signal and status signals are separated due to signal transmission paths are allocated independently based on signal type. In this kind of design, single device failure or failures on signal path in the channel couldn't result in the loss of all segmented functions simultaneously. Thus the proposed segmentation function is the design scheme that improves availability of safety functions. In conventional ESF-CCS, the single controller generates the signal to control the multiple safety functions, and the reliability is achieved by multiplication within the channel. This design has a drawback causing the loss of multiple functions due to the CCF (Common Cause Failure) and single failure Heterogeneous controller guarantees the diversity ensuring the execution of safety functions against the CCF and single failure, but requiring a lot of resources like manpower and cost. The segmentation technology based on the compartmentalization and functional diversification decreases the CCF and single failure nonetheless the identical types of controllers

Segmentation Scheme for Safety Enhancement of Engineered Safety Features Component Control System

Energy Technology Data Exchange (ETDEWEB)

Lee, Sangseok; Sohn, Kwangyoung [Korea Reliability Technology and System, Daejeon (Korea, Republic of); Lee, Junku; Park, Geunok [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2013-05-15

Common Caused Failure (CCF) or undetectable failure would adversely impact safety functions of ESF-CCS in the existing nuclear power plants. We propose the segmentation scheme to solve these problems. Main function assignment to segments in the proposed segmentation scheme is based on functional dependency and critical function success path by using the dependency depth matrix. The segment has functional independence and physical isolation. The segmentation structure is that prohibit failure propagation to others from undetectable failures. Therefore, the segmentation system structure has robustness to undetectable failures. The segmentation system structure has functional diversity. The specific function in the segment defected by CCF, the specific function could be maintained by diverse control function that assigned to other segments. Device level control signals and system level control signals are separated and also control signal and status signals are separated due to signal transmission paths are allocated independently based on signal type. In this kind of design, single device failure or failures on signal path in the channel couldn't result in the loss of all segmented functions simultaneously. Thus the proposed segmentation function is the design scheme that improves availability of safety functions. In conventional ESF-CCS, the single controller generates the signal to control the multiple safety functions, and the reliability is achieved by multiplication within the channel. This design has a drawback causing the loss of multiple functions due to the CCF (Common Cause Failure) and single failure Heterogeneous controller guarantees the diversity ensuring the execution of safety functions against the CCF and single failure, but requiring a lot of resources like manpower and cost. The segmentation technology based on the compartmentalization and functional diversification decreases the CCF and single failure nonetheless the identical types of

Atlas-based segmentation technique incorporating inter-observer delineation uncertainty for whole breast

International Nuclear Information System (INIS)

Bell, L R; Pogson, E M; Metcalfe, P; Holloway, L; Dowling, J A

2017-01-01

Accurate, efficient auto-segmentation methods are essential for the clinical efficacy of adaptive radiotherapy delivered with highly conformal techniques. Current atlas based auto-segmentation techniques are adequate in this respect, however fail to account for inter-observer variation. An atlas-based segmentation method that incorporates inter-observer variation is proposed. This method is validated for a whole breast radiotherapy cohort containing 28 CT datasets with CTVs delineated by eight observers. To optimise atlas accuracy, the cohort was divided into categories by mean body mass index and laterality, with atlas’ generated for each in a leave-one-out approach. Observer CTVs were merged and thresholded to generate an auto-segmentation model representing both inter-observer and inter-patient differences. For each category, the atlas was registered to the left-out dataset to enable propagation of the auto-segmentation from atlas space. Auto-segmentation time was recorded. The segmentation was compared to the gold-standard contour using the dice similarity coefficient (DSC) and mean absolute surface distance (MASD). Comparison with the smallest and largest CTV was also made. This atlas-based auto-segmentation method incorporating inter-observer variation was shown to be efficient (<4min) and accurate for whole breast radiotherapy, with good agreement (DSC>0.7, MASD <9.3mm) between the auto-segmented contours and CTV volumes. (paper)

Unsupervised information extraction by text segmentation

CERN Document Server

Cortez, Eli

2013-01-01

A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors' approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a

RGB-D Segmentation of Poultry Entrails

DEFF Research Database (Denmark)

Philipsen, Mark Philip; Jørgensen, Anders; Guerrero, Sergio Escalera

2016-01-01

and passed to a Random Forest classifier, which classifies the supervoxels as either belonging to heart, lung, liver or misc. The dataset consists of 150 individual entrails, with 30 of these being reserved for evaluation. Segmentation performance is evaluated on a voxel-by-voxel basis, achieving an average...

Unsupervised Tattoo Segmentation Combining Bottom-Up and Top-Down Cues

Energy Technology Data Exchange (ETDEWEB)

Allen, Josef D [ORNL

2011-01-01

Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for nding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a gure-ground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is e cient and suitable for further tattoo classi cation and retrieval purpose.

User-assisted video segmentation system for visual communication

Science.gov (United States)

Wu, Zhengping; Chen, Chun

2002-01-01

Video segmentation plays an important role for efficient storage and transmission in visual communication. In this paper, we introduce a novel video segmentation system using point tracking and contour formation techniques. Inspired by the results from the study of the human visual system, we intend to solve the video segmentation problem into three separate phases: user-assisted feature points selection, feature points' automatic tracking, and contour formation. This splitting relieves the computer of ill-posed automatic segmentation problems, and allows a higher level of flexibility of the method. First, the precise feature points can be found using a combination of user assistance and an eigenvalue-based adjustment. Second, the feature points in the remaining frames are obtained using motion estimation and point refinement. At last, contour formation is used to extract the object, and plus a point insertion process to provide the feature points for next frame's tracking.

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

Directory of Open Access Journals (Sweden)

Li Yizheng

2007-09-01

Full Text Available Abstract Background High content screening (HCS is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells. Results Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells. Conclusion Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.

Supervised methods for detection and segmentation of tissues in clinical lumbar MRI.

Science.gov (United States)

Ghosh, Subarna; Chaudhary, Vipin

2014-10-01

Lower back pain (LBP) is widely prevalent all over the world and more than 80% of the people suffer from LBP at some point of their lives. Moreover, a shortage of radiologists is the most pressing cause for the need of CAD (computer-aided diagnosis) systems. Automatic localization and labeling of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Subsequently, for diagnosis and characterization (quantification and localization) of abnormalities like disc herniation and stenosis, a completely automatic segmentation of intervertebral discs and the dural sac is extremely important. Contribution of this paper towards clinical CAD systems is two-fold. First, we propose a method to automatically detect all visible intervertebral discs in clinical sagittal MRI using heuristics and machine learning techniques. We provide a novel end-to-end framework that outputs a tight bounding box for each disc, instead of simply marking the centroid of discs, as has been the trend in the recent past. Second, we propose a method to simultaneously segment all the tissues (vertebrae, intervertebral disc, dural sac and background) in a lumbar sagittal MRI, using an auto-context approach instead of any explicit shape features or models. Past work tackles the lumbar segmentation problem on a tissue/organ basis, and which tend to perform poorly in clinical scans due to high variability in appearance. We, on the other hand, train a series of robust classifiers (random forests) using image features and sparsely sampled context features, which implicitly represent the shape and configuration of the image. Both these methods have been tested on a huge clinical dataset comprising of 212 cases and show very promising results for both disc detection (98% disc localization accuracy and 2.08mm mean deviation) and sagittal MRI segmentation (dice similarity indices of 0.87 and 0.84 for the dural sac and the inter-vertebral disc, respectively

4D segmentation of brain MR images with constrained cortical thickness variation.

Directory of Open Access Journals (Sweden)

Li Wang

Full Text Available Segmentation of brain MR images plays an important role in longitudinal investigation of developmental, aging, disease progression changes in the cerebral cortex. However, most existing brain segmentation methods consider multiple time-point images individually and thus cannot achieve longitudinal consistency. For example, cortical thickness measured from the segmented image will contain unnecessary temporal variations, which will affect the time related change pattern and eventually reduce the statistical power of analysis. In this paper, we propose a 4D segmentation framework for the adult brain MR images with the constraint of cortical thickness variations. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness being within a reasonable range, and temporal cortical thickness variation constraint in neighboring time-points to suppress the artificial variations. The proposed method has been tested on BLSA dataset and ADNI dataset with promising results. Both qualitative and quantitative experimental results demonstrate the advantage of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.

heuristically improved bayesian segmentation of brain mr images

African Journals Online (AJOL)

Brainweb as a simulated brain MRI dataset is used in evaluating the proposed algorithm. ..... neighboring system can improve the segmentation power of the algorithm. ... tuning and learning of fuzzy knowledge bases, World Scientific. Pub Co ...

Computer-aided liver volumetry: performance of a fully-automated, prototype post-processing solution for whole-organ and lobar segmentation based on MDCT imaging.

Science.gov (United States)

Fananapazir, Ghaneh; Bashir, Mustafa R; Marin, Daniele; Boll, Daniel T

2015-06-01

To evaluate the performance of a prototype, fully-automated post-processing solution for whole-liver and lobar segmentation based on MDCT datasets. A polymer liver phantom was used to assess accuracy of post-processing applications comparing phantom volumes determined via Archimedes' principle with MDCT segmented datasets. For the IRB-approved, HIPAA-compliant study, 25 patients were enrolled. Volumetry performance compared the manual approach with the automated prototype, assessing intraobserver variability, and interclass correlation for whole-organ and lobar segmentation using ANOVA comparison. Fidelity of segmentation was evaluated qualitatively. Phantom volume was 1581.0 ± 44.7 mL, manually segmented datasets estimated 1628.0 ± 47.8 mL, representing a mean overestimation of 3.0%, automatically segmented datasets estimated 1601.9 ± 0 mL, representing a mean overestimation of 1.3%. Whole-liver and segmental volumetry demonstrated no significant intraobserver variability for neither manual nor automated measurements. For whole-liver volumetry, automated measurement repetitions resulted in identical values; reproducible whole-organ volumetry was also achieved with manual segmentation, p(ANOVA) 0.98. For lobar volumetry, automated segmentation improved reproducibility over manual approach, without significant measurement differences for either methodology, p(ANOVA) 0.95-0.99. Whole-organ and lobar segmentation results from manual and automated segmentation showed no significant differences, p(ANOVA) 0.96-1.00. Assessment of segmentation fidelity found that segments I-IV/VI showed greater segmentation inaccuracies compared to the remaining right hepatic lobe segments. Automated whole-liver segmentation showed non-inferiority of fully-automated whole-liver segmentation compared to manual approaches with improved reproducibility and post-processing duration; automated dual-seed lobar segmentation showed slight tendencies for underestimating the right hepatic lobe

Segmentation of histological images and fibrosis identification with a convolutional neural network.

Science.gov (United States)

Fu, Xiaohang; Liu, Tong; Xiong, Zhaohan; Smaill, Bruce H; Stiles, Martin K; Zhao, Jichao

2018-05-16

Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises 11 successive convolutional - rectified linear unit - batch normalization layers. It outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300,000) trainable parameters than the state-of-the-art, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored to the problem of concern, and may potentially be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment. Copyright © 2018. Published by Elsevier Ltd.

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

Science.gov (United States)

Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

2018-04-01

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

Connecting textual segments

DEFF Research Database (Denmark)

Brügger, Niels

2017-01-01

history than just the years of the emergence of the web, the chapter traces the history of how segments of text have deliberately been connected to each other by the use of specific textual and media features, from clay tablets, manuscripts on parchment, and print, among others, to hyperlinks on stand......In “Connecting textual segments: A brief history of the web hyperlink” Niels Brügger investigates the history of one of the most fundamental features of the web: the hyperlink. Based on the argument that the web hyperlink is best understood if it is seen as another step in a much longer and broader...

Compresso: Efficient Compression of Segmentation Data for Connectomics

KAUST Repository

Matejek, Brian; Haehn, Daniel; Lekschas, Fritz; Mitzenmacher, Michael; Pfister, Hanspeter

2017-01-01

Recent advances in segmentation methods for connectomics and biomedical imaging produce very large datasets with labels that assign object classes to image pixels. The resulting label volumes are bigger than the raw image data and need compression

Segmentation of Clinical Endoscopic Images Based on the Classification of Topological Vector Features

Directory of Open Access Journals (Sweden)

O. A. Dunaeva

2013-01-01

Full Text Available In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.

Hierarchical image segmentation for learning object priors

Energy Technology Data Exchange (ETDEWEB)

Prasad, Lakshman [Los Alamos National Laboratory; Yang, Xingwei [TEMPLE UNIV.; Latecki, Longin J [TEMPLE UNIV.; Li, Nan [TEMPLE UNIV.

2010-11-10

The proposed segmentation approach naturally combines experience based and image based information. The experience based information is obtained by training a classifier for each object class. For a given test image, the result of each classifier is represented as a probability map. The final segmentation is obtained with a hierarchial image segmentation algorithm that considers both the probability maps and the image features such as color and edge strength. We also utilize image region hierarchy to obtain not only local but also semi-global features as input to the classifiers. Moreover, to get robust probability maps, we take into account the region context information by averaging the probability maps over different levels of the hierarchical segmentation algorithm. The obtained segmentation results are superior to the state-of-the-art supervised image segmentation algorithms.

AISLE: an automatic volumetric segmentation method for the study of lung allometry.

Science.gov (United States)

Ren, Hongliang; Kazanzides, Peter

2011-01-01

We developed a fully automatic segmentation method for volumetric CT (computer tomography) datasets to support construction of a statistical atlas for the study of allometric laws of the lung. The proposed segmentation method, AISLE (Automated ITK-Snap based on Level-set), is based on the level-set implementation from an existing semi-automatic segmentation program, ITK-Snap. AISLE can segment the lung field without human interaction and provide intermediate graphical results as desired. The preliminary experimental results show that the proposed method can achieve accurate segmentation, in terms of volumetric overlap metric, by comparing with the ground-truth segmentation performed by a radiologist.

Automatic segmentation of tumor-laden lung volumes from the LIDC database

Science.gov (United States)

O'Dell, Walter G.

2012-03-01

The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

Figure-ground segmentation based on class-independent shape priors

Science.gov (United States)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

Compresso: Efficient Compression of Segmentation Data for Connectomics

KAUST Repository

Matejek, Brian

2017-09-03

Recent advances in segmentation methods for connectomics and biomedical imaging produce very large datasets with labels that assign object classes to image pixels. The resulting label volumes are bigger than the raw image data and need compression for efficient storage and transfer. General-purpose compression methods are less effective because the label data consists of large low-frequency regions with structured boundaries unlike natural image data. We present Compresso, a new compression scheme for label data that outperforms existing approaches by using a sliding window to exploit redundancy across border regions in 2D and 3D. We compare our method to existing compression schemes and provide a detailed evaluation on eleven biomedical and image segmentation datasets. Our method provides a factor of 600–2200x compression for label volumes, with running times suitable for practice.

BlobContours: adapting Blobworld for supervised color- and texture-based image segmentation

Science.gov (United States)

Vogel, Thomas; Nguyen, Dinh Quyen; Dittmann, Jana

2006-01-01

Extracting features is the first and one of the most crucial steps in recent image retrieval process. While the color features and the texture features of digital images can be extracted rather easily, the shape features and the layout features depend on reliable image segmentation. Unsupervised image segmentation, often used in image analysis, works on merely syntactical basis. That is, what an unsupervised segmentation algorithm can segment is only regions, but not objects. To obtain high-level objects, which is desirable in image retrieval, human assistance is needed. Supervised image segmentations schemes can improve the reliability of segmentation and segmentation refinement. In this paper we propose a novel interactive image segmentation technique that combines the reliability of a human expert with the precision of automated image segmentation. The iterative procedure can be considered a variation on the Blobworld algorithm introduced by Carson et al. from EECS Department, University of California, Berkeley. Starting with an initial segmentation as provided by the Blobworld framework, our algorithm, namely BlobContours, gradually updates it by recalculating every blob, based on the original features and the updated number of Gaussians. Since the original algorithm has hardly been designed for interactive processing we had to consider additional requirements for realizing a supervised segmentation scheme on the basis of Blobworld. Increasing transparency of the algorithm by applying usercontrolled iterative segmentation, providing different types of visualization for displaying the segmented image and decreasing computational time of segmentation are three major requirements which are discussed in detail.

Image fusion using sparse overcomplete feature dictionaries

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Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Segmented trapped vortex cavity

Science.gov (United States)

Grammel, Jr., Leonard Paul (Inventor); Pennekamp, David Lance (Inventor); Winslow, Jr., Ralph Henry (Inventor)

2010-01-01

An annular trapped vortex cavity assembly segment comprising includes a cavity forward wall, a cavity aft wall, and a cavity radially outer wall there between defining a cavity segment therein. A cavity opening extends between the forward and aft walls at a radially inner end of the assembly segment. Radially spaced apart pluralities of air injection first and second holes extend through the forward and aft walls respectively. The segment may include first and second expansion joint features at distal first and second ends respectively of the segment. The segment may include a forward subcomponent including the cavity forward wall attached to an aft subcomponent including the cavity aft wall. The forward and aft subcomponents include forward and aft portions of the cavity radially outer wall respectively. A ring of the segments may be circumferentially disposed about an axis to form an annular segmented vortex cavity assembly.

Comparison of CORA and EN4 in-situ datasets validation methods, toward a better quality merged dataset.

Science.gov (United States)

Szekely, Tanguy; Killick, Rachel; Gourrion, Jerome; Reverdin, Gilles

2017-04-01

CORA and EN4 are both global delayed time mode validated in-situ ocean temperature and salinity datasets distributed by the Met Office (http://www.metoffice.gov.uk/) and Copernicus (www.marine.copernicus.eu). A large part of the profiles distributed by CORA and EN4 in recent years are Argo profiles from the ARGO DAC, but profiles are also extracted from the World Ocean Database and TESAC profiles from GTSPP. In the case of CORA, data coming from the EUROGOOS Regional operationnal oserving system( ROOS) operated by European institutes no managed by National Data Centres and other datasets of profiles povided by scientific sources can also be found (Sea mammals profiles from MEOP, XBT datasets from cruises ...). (EN4 also takes data from the ASBO dataset to supplement observations in the Arctic). First advantage of this new merge product is to enhance the space and time coverage at global and european scales for the period covering 1950 till a year before the current year. This product is updated once a year and T&S gridded fields are alos generated for the period 1990-year n-1. The enhancement compared to the revious CORA product will be presented Despite the fact that the profiles distributed by both datasets are mostly the same, the quality control procedures developed by the Met Office and Copernicus teams differ, sometimes leading to different quality control flags for the same profile. Started in 2016 a new study started that aims to compare both validation procedures to move towards a Copernicus Marine Service dataset with the best features of CORA and EN4 validation.A reference data set composed of the full set of in-situ temperature and salinity measurements collected by Coriolis during 2015 is used. These measurements have been made thanks to wide range of instruments (XBTs, CTDs, Argo floats, Instrumented sea mammals,...), covering the global ocean. The reference dataset has been validated simultaneously by both teams.An exhaustive comparison of the

Performance Analysis of Segmentation of Hyperspectral Images Based on Color Image Segmentation

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Praveen Agarwal

2017-06-01

Full Text Available Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified with some mathematical equations. The spatial constraint allows taking into account the inherent spatial relationships of any image and its color. This approach provides effective PSNR for the segmented image. These results have the better performance as the segmented images are compared with Watershed & Region Growing Algorithm and provide effective segmentation for the Spectral Images & Medical Images.

Segmentation in reading and film comprehension.

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Zacks, Jeffrey M; Speer, Nicole K; Reynolds, Jeremy R

2009-05-01

When reading a story or watching a film, comprehenders construct a series of representations in order to understand the events depicted. Discourse comprehension theories and a recent theory of perceptual event segmentation both suggest that comprehenders monitor situational features such as characters' goals, to update these representations at natural boundaries in activity. However, the converging predictions of these theories had previously not been tested directly. Two studies provided evidence that changes in situational features such as characters, their locations, their interactions with objects, and their goals are related to the segmentation of events in both narrative texts and films. A 3rd study indicated that clauses with event boundaries are read more slowly than are other clauses and that changes in situational features partially mediate this relation. A final study suggested that the predictability of incoming information influences reading rate and possibly event segmentation. Taken together, these results suggest that processing situational changes during comprehension is an important determinant of how one segments ongoing activity into events and that this segmentation is related to the control of processing during reading. (c) 2009 APA, all rights reserved.

3D liver segmentation using multiple region appearances and graph cuts

Energy Technology Data Exchange (ETDEWEB)

Peng, Jialin, E-mail: 2004pjl@163.com; Zhang, Hongbo [College of Computer Science and Technology, Huaqiao University, Xiamen 361021 (China); Hu, Peijun; Lu, Fang; Kong, Dexing [College of Mathematics, Zhejiang University, Hangzhou 310027 (China); Peng, Zhiyi [Department of Radiology, First Affiliated Hospital, Zhejiang University, Hangzhou 310027 (China)

2015-12-15

Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets

Chest wall segmentation in automated 3D breast ultrasound scans.

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Tan, Tao; Platel, Bram; Mann, Ritse M; Huisman, Henkjan; Karssemeijer, Nico

2013-12-01

In this paper, we present an automatic method to segment the chest wall in automated 3D breast ultrasound images. Determining the location of the chest wall in automated 3D breast ultrasound images is necessary in computer-aided detection systems to remove automatically detected cancer candidates beyond the chest wall and it can be of great help for inter- and intra-modal image registration. We show that the visible part of the chest wall in an automated 3D breast ultrasound image can be accurately modeled by a cylinder. We fit the surface of our cylinder model to a set of automatically detected rib-surface points. The detection of the rib-surface points is done by a classifier using features representing local image intensity patterns and presence of rib shadows. Due to attenuation of the ultrasound signal, a clear shadow is visible behind the ribs. Evaluation of our segmentation method is done by computing the distance of manually annotated rib points to the surface of the automatically detected chest wall. We examined the performance on images obtained with the two most common 3D breast ultrasound devices in the market. In a dataset of 142 images, the average mean distance of the annotated points to the segmented chest wall was 5.59 ± 3.08 mm. Copyright © 2012 Elsevier B.V. All rights reserved.

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

Science.gov (United States)

Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

2015-01-01

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning

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Cukierski William J

2012-09-01

Full Text Available Abstract Background Correct segmentation is critical to many applications within automated microscopy image analysis. Despite the availability of advanced segmentation algorithms, variations in cell morphology, sample preparation, and acquisition settings often lead to segmentation errors. This manuscript introduces a ranked-retrieval approach using logistic regression to automate selection of accurately segmented nuclei from a set of candidate segmentations. The methodology is validated on an application of spatial gene repositioning in breast cancer cell nuclei. Gene repositioning is analyzed in patient tissue sections by labeling sequences with fluorescence in situ hybridization (FISH, followed by measurement of the relative position of each gene from the nuclear center to the nuclear periphery. This technique requires hundreds of well-segmented nuclei per sample to achieve statistical significance. Although the tissue samples in this study contain a surplus of available nuclei, automatic identification of the well-segmented subset remains a challenging task. Results Logistic regression was applied to features extracted from candidate segmented nuclei, including nuclear shape, texture, context, and gene copy number, in order to rank objects according to the likelihood of being an accurately segmented nucleus. The method was demonstrated on a tissue microarray dataset of 43 breast cancer patients, comprising approximately 40,000 imaged nuclei in which the HES5 and FRA2 genes were labeled with FISH probes. Three trained reviewers independently classified nuclei into three classes of segmentation accuracy. In man vs. machine studies, the automated method outperformed the inter-observer agreement between reviewers, as measured by area under the receiver operating characteristic (ROC curve. Robustness of gene position measurements to boundary inaccuracies was demonstrated by comparing 1086 manually and automatically segmented nuclei. Pearson

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Science.gov (United States)

Avendi, M R; Kheradvar, Arash; Jafarkhani, Hamid

2016-05-01

Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic LV segmentation tool from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are used to infer the LV shape. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81 mm and 0.86, versus those of 79.2-95.62%, 0.87-0.9, 1.76-2.97 mm and 0.67-0.78, obtained by other methods, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES

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A. Sharma

2015-03-01

Full Text Available Saliency gives the way as humans see any image and saliency based segmentation can be eventually helpful in Psychovisual image interpretation. Keeping this in view few saliency models are used along with segmentation algorithm and only the salient segments from image have been extracted. The work is carried out for terrestrial images as well as for satellite images. The methodology used in this work extracts those segments from segmented image which are having higher or equal saliency value than a threshold value. Salient and non salient regions of image become foreground and background respectively and thus image gets separated. For carrying out this work a dataset of terrestrial images and Worldview 2 satellite images (sample data are used. Results show that those saliency models which works better for terrestrial images are not good enough for satellite image in terms of foreground and background separation. Foreground and background separation in terrestrial images is based on salient objects visible on the images whereas in satellite images this separation is based on salient area rather than salient objects.

An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

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Dong, Zhen; Yang, Bisheng; Hu, Pingbo; Scherer, Sebastian

2018-03-01

Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., http://SEMANTIC3D.NET)

User-guided segmentation for volumetric retinal optical coherence tomography images

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Yin, Xin; Chao, Jennifer R.; Wang, Ruikang K.

2014-01-01

Abstract. Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method. PMID:25147962

Medical Image Segmentation by Combining Graph Cut and Oriented Active Appearance Models

Science.gov (United States)

Chen, Xinjian; Udupa, Jayaram K.; Bağcı, Ulaş; Zhuge, Ying; Yao, Jianhua

2017-01-01

In this paper, we propose a novel 3D segmentation method based on the effective combination of the active appearance model (AAM), live wire (LW), and graph cut (GC). The proposed method consists of three main parts: model building, initialization, and segmentation. In the model building part, we construct the AAM and train the LW cost function and GC parameters. In the initialization part, a novel algorithm is proposed for improving the conventional AAM matching method, which effectively combines the AAM and LW method, resulting in Oriented AAM (OAAM). A multi-object strategy is utilized to help in object initialization. We employ a pseudo-3D initialization strategy, and segment the organs slice by slice via multi-object OAAM method. For the segmentation part, a 3D shape constrained GC method is proposed. The object shape generated from the initialization step is integrated into the GC cost computation, and an iterative GC-OAAM method is used for object delineation. The proposed method was tested in segmenting the liver, kidneys, and spleen on a clinical CT dataset and also tested on the MICCAI 2007 grand challenge for liver segmentation training dataset. The results show the following: (a) An overall segmentation accuracy of true positive volume fraction (TPVF) > 94.3%, false positive volume fraction (FPVF) wordpress.com/research/. PMID:22311862

An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

DEFF Research Database (Denmark)

Lyksborg, Mark; Puonti, Oula; Agn, Mikael

2015-01-01

Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images....... The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally......, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation...

Joint shape segmentation with linear programming

KAUST Repository

Huang, Qixing

2011-01-01

We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem. The program optimizes over possible segmentations of individual shapes as well as over possible correspondences between segments from multiple shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton segmentation benchmark and show that joint shape segmentation significantly outperforms single-shape segmentation techniques. © 2011 ACM.

Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset

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Amir Ahmad

2016-01-01

Full Text Available The early diagnosis of breast cancer is an important step in a fight against the disease. Machine learning techniques have shown promise in improving our understanding of the disease. As medical datasets consist of data points which cannot be precisely assigned to a class, fuzzy methods have been useful for studying of these datasets. Sometimes breast cancer datasets are described by categorical features. Many fuzzy clustering algorithms have been developed for categorical datasets. However, in most of these methods Hamming distance is used to define the distance between the two categorical feature values. In this paper, we use a probabilistic distance measure for the distance computation among a pair of categorical feature values. Experiments demonstrate that the distance measure performs better than Hamming distance for Wisconsin breast cancer data.

GPU accelerated fuzzy connected image segmentation by using CUDA.

Science.gov (United States)

Zhuge, Ying; Cao, Yong; Miller, Robert W

2009-01-01

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets with small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the sequential implementation of fuzzy connected image segmentation algorithm on CPU.

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

Science.gov (United States)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

Automatic segmentation of lumbar vertebrae in CT images

Science.gov (United States)

Kulkarni, Amruta; Raina, Akshita; Sharifi Sarabi, Mona; Ahn, Christine S.; Babayan, Diana; Gaonkar, Bilwaj; Macyszyn, Luke; Raghavendra, Cauligi

2017-03-01

Lower back pain is one of the most prevalent disorders in the developed/developing world. However, its etiology is poorly understood and treatment is often determined subjectively. In order to quantitatively study the emergence and evolution of back pain, it is necessary to develop consistently measurable markers for pathology. Imaging based measures offer one solution to this problem. The development of imaging based on quantitative biomarkers for the lower back necessitates automated techniques to acquire this data. While the problem of segmenting lumbar vertebrae has been addressed repeatedly in literature, the associated problem of computing relevant biomarkers on the basis of the segmentation has not been addressed thoroughly. In this paper, we propose a Random-Forest based approach that learns to segment vertebral bodies in CT images followed by a biomarker evaluation framework that extracts vertebral heights and widths from the segmentations obtained. Our dataset consists of 15 CT sagittal scans obtained from General Electric Healthcare. Our main approach is divided into three parts: the first stage is image pre-processing which is used to correct for variations in illumination across all the images followed by preparing the foreground and background objects from images; the next stage is Machine Learning using Random-Forests, which distinguishes the interest-point vectors between foreground or background; and the last step is image post-processing, which is crucial to refine the results of classifier. The Dice coefficient was used as a statistical validation metric to evaluate the performance of our segmentations with an average value of 0.725 for our dataset.

HIGH QUALITY FACADE SEGMENTATION BASED ON STRUCTURED RANDOM FOREST, REGION PROPOSAL NETWORK AND RECTANGULAR FITTING

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K. Rahmani

2018-05-01

Full Text Available In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF, Region Proposal Network (RPN based on a Convolutional Neural Network (CNN as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF.We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

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Xin Wang

2018-02-01

Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

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Carolina Wählby

2002-01-01

Full Text Available Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

Iatrogenic injury in the lateral segment of the liver after pancreatoduodenectomy: Early follow up CT features and clinical implications

Energy Technology Data Exchange (ETDEWEB)

Kim, Yaena; Yu, Jeong Sik; Chung, Jae Joon; Kim, Joo Hee; Cho, Eun Suk; Ahn, Jhii Hyun; Kim, Ki Whang [Yonsei Univ. College of Medicine, Seoul (Korea, Republic of)

2012-05-15

To investigate the incidence, predisposing factors and image features of iatrogenically induced focal parenchymal changes in the lateral segment of the liver after a pancreatoduodenectomy. A follow up CT taken on the seventh day after an uneventful pancreatoduodenectomy were retrospectively reviewed for 123 patients for newly developed focal hepatic lesions. The location, size, and shape of the lesions were analyzed along with preoperative anatomic variation of the hepatic artery, for the degree of intrahepatic bile duct dilatation and procedure duration. Other than two patients with hepatic metastases, 13 (10.6%) patients showed newly developed irregular (n = 9), linear (n = 2) or wedge like (n = 2) hypovascular areas (1.4-8.5 cm; mean, 2.8 cm) in the posterior subcapsular portion of the lateral segment. There were only two patients (15.4%) with an aberrant origin of the segmental hepatic artery from the left gastric artery, and the degree of bile duct dilatation was nonspecific for the 13 subjected patients. Mean procedure time was not significantly different between the subjected patients and the others (541 min vs. 507 min; p = 0.160). Focal iatrogenic injury in the lateral segment after a pancreatoduodenectomy would not be a common event regardless of preoperative vascular anatomic variation, bile duct dilatation, or procedure duration.

Iatrogenic injury in the lateral segment of the liver after pancreatoduodenectomy: Early follow up CT features and clinical implications

International Nuclear Information System (INIS)

Kim, Yaena; Yu, Jeong Sik; Chung, Jae Joon; Kim, Joo Hee; Cho, Eun Suk; Ahn, Jhii Hyun; Kim, Ki Whang

2012-01-01

To investigate the incidence, predisposing factors and image features of iatrogenically induced focal parenchymal changes in the lateral segment of the liver after a pancreatoduodenectomy. A follow up CT taken on the seventh day after an uneventful pancreatoduodenectomy were retrospectively reviewed for 123 patients for newly developed focal hepatic lesions. The location, size, and shape of the lesions were analyzed along with preoperative anatomic variation of the hepatic artery, for the degree of intrahepatic bile duct dilatation and procedure duration. Other than two patients with hepatic metastases, 13 (10.6%) patients showed newly developed irregular (n = 9), linear (n = 2) or wedge like (n = 2) hypovascular areas (1.4-8.5 cm; mean, 2.8 cm) in the posterior subcapsular portion of the lateral segment. There were only two patients (15.4%) with an aberrant origin of the segmental hepatic artery from the left gastric artery, and the degree of bile duct dilatation was nonspecific for the 13 subjected patients. Mean procedure time was not significantly different between the subjected patients and the others (541 min vs. 507 min; p = 0.160). Focal iatrogenic injury in the lateral segment after a pancreatoduodenectomy would not be a common event regardless of preoperative vascular anatomic variation, bile duct dilatation, or procedure duration

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

Science.gov (United States)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations.

Directory of Open Access Journals (Sweden)

Miha Amon

Full Text Available Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping, thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB. As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3 classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The

3D automatic segmentation method for retinal optical coherence tomography volume data using boundary surface enhancement

Directory of Open Access Journals (Sweden)

Yankui Sun

2016-03-01

Full Text Available With the introduction of spectral-domain optical coherence tomography (SD-OCT, much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of three-dimensional (3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of datasets contains 97 OCT B-Scan images with a resolution of 496×512 (each B-Scan comprising 512 A-Scans containing 496 pixels; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.

Assessing the scale of tumor heterogeneity by complete hierarchical segmentation of MRI.

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Gensheimer, Michael F; Hawkins, Douglas S; Ermoian, Ralph P; Trister, Andrew D

2015-02-07

In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment.

Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

Directory of Open Access Journals (Sweden)

R. V. V. Krishna

2016-10-01

Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

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Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qian, Wei; Zheng, Bin

2018-03-01

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p breast cancer detected in the next subsequent mammography screening.

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Directory of Open Access Journals (Sweden)

Margarita Gamarra

2017-08-01

Full Text Available Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

Influence of nuclei segmentation on breast cancer malignancy classification

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Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

Segmentation of the Himalayas as revealed by arc-parallel gravity anomalies

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Hetényi, György; Cattin, Rodolphe; Berthet, Théo; Le Moigne, Nicolas; Chophel, Jamyang; Lechmann, Sarah; Hammer, Paul; Drukpa, Dowchu; Sapkota, Soma Nath; Gautier, Stéphanie; Thinley, Kinzang

2016-09-01

Lateral variations along the Himalayan arc are suggested by an increasing number of studies and carry important information about the orogen’s segmentation. Here we compile the hitherto most complete land gravity dataset in the region which enables the currently highest resolution plausible analysis. To study lateral variations in collisional structure we compute arc-parallel gravity anomalies (APaGA) by subtracting the average arc-perpendicular profile from our dataset; we compute likewise for topography (APaTA). We find no direct correlation between APaGA, APaTA and background seismicity, as suggested in oceanic subduction context. In the Himalayas APaTA mainly reflect relief and erosional effects, whereas APaGA reflect the deep structure of the orogen with clear lateral boundaries. Four segments are outlined and have disparate flexural geometry: NE India, Bhutan, Nepal & India until Dehradun, and NW India. The segment boundaries in the India plate are related to inherited structures, and the boundaries of the Shillong block are highlighted by seismic activity. We find that large earthquakes of the past millennium do not propagate across the segment boundaries defined by APaGA, therefore these seem to set limits for potential rupture of megathrust earthquakes.

Epidermal segmentation in high-definition optical coherence tomography.

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Li, Annan; Cheng, Jun; Yow, Ai Ping; Wall, Carolin; Wong, Damon Wing Kee; Tey, Hong Liang; Liu, Jiang

2015-01-01

Epidermis segmentation is a crucial step in many dermatological applications. Recently, high-definition optical coherence tomography (HD-OCT) has been developed and applied to imaging subsurface skin tissues. In this paper, a novel epidermis segmentation method using HD-OCT is proposed in which the epidermis is segmented by 3 steps: the weighted least square-based pre-processing, the graph-based skin surface detection and the local integral projection-based dermal-epidermal junction detection respectively. Using a dataset of five 3D volumes, we found that this method correlates well with the conventional method of manually marking out the epidermis. This method can therefore serve to effectively and rapidly delineate the epidermis for study and clinical management of skin diseases.

A new and fast image feature selection method for developing an optimal mammographic mass detection scheme.

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Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-08-01

Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Among these four methods, SFFS has highest efficacy, which takes 3%-5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC results of the ANNs optimized

Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation.

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Tobon-Gomez, Catalina; Sukno, Federico M; Butakoff, Constantine; Huguet, Marina; Frangi, Alejandro F

2012-07-07

Training active shape models requires collecting manual ground-truth meshes in a large image database. While shape information can be reused across multiple imaging modalities, intensity information needs to be imaging modality and protocol specific. In this context, this study has two main purposes: (1) to test the potential of using intensity models learned from MRI simulated datasets and (2) to test the potential of including a measure of reliability during the matching process to increase robustness. We used a population of 400 virtual subjects (XCAT phantom), and two clinical populations of 40 and 45 subjects. Virtual subjects were used to generate simulated datasets (MRISIM simulator). Intensity models were trained both on simulated and real datasets. The trained models were used to segment the left ventricle (LV) and right ventricle (RV) from real datasets. Segmentations were also obtained with and without reliability information. Performance was evaluated with point-to-surface and volume errors. Simulated intensity models obtained average accuracy comparable to inter-observer variability for LV segmentation. The inclusion of reliability information reduced volume errors in hypertrophic patients (EF errors from 17 ± 57% to 10 ± 18%; LV MASS errors from -27 ± 22 g to -14 ± 25 g), and in heart failure patients (EF errors from -8 ± 42% to -5 ± 14%). The RV model of the simulated images needs further improvement to better resemble image intensities around the myocardial edges. Both for real and simulated models, reliability information increased segmentation robustness without penalizing accuracy.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

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Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

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Juan-Albarracín, Javier; Fuster-Garcia, Elies; Manjón, José V.; Robles, Montserrat; Aparici, F.; Martí-Bonmatí, L.; García-Gómez, Juan M.

2015-01-01

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation. PMID:25978453

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

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Roth, Holger; Oda, Masahiro; Shimizu, Natsuki; Oda, Hirohisa; Hayashi, Yuichiro; Kitasaka, Takayuki; Fujiwara, Michitaka; Misawa, Kazunari; Mori, Kensaku

2018-03-01

Pancreas segmentation in computed tomography imaging has been historically difficult for automated methods because of the large shape and size variations between patients. In this work, we describe a custom-build 3D fully convolutional network (FCN) that can process a 3D image including the whole pancreas and produce an automatic segmentation. We investigate two variations of the 3D FCN architecture; one with concatenation and one with summation skip connections to the decoder part of the network. We evaluate our methods on a dataset from a clinical trial with gastric cancer patients, including 147 contrast enhanced abdominal CT scans acquired in the portal venous phase. Using the summation architecture, we achieve an average Dice score of 89.7 +/- 3.8 (range [79.8, 94.8])% in testing, achieving the new state-of-the-art performance in pancreas segmentation on this dataset.

Open-source software platform for medical image segmentation applications

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Namías, R.; D'Amato, J. P.; del Fresno, M.

2017-11-01

Segmenting 2D and 3D images is a crucial and challenging problem in medical image analysis. Although several image segmentation algorithms have been proposed for different applications, no universal method currently exists. Moreover, their use is usually limited when detection of complex and multiple adjacent objects of interest is needed. In addition, the continually increasing volumes of medical imaging scans require more efficient segmentation software design and highly usable applications. In this context, we present an extension of our previous segmentation framework which allows the combination of existing explicit deformable models in an efficient and transparent way, handling simultaneously different segmentation strategies and interacting with a graphic user interface (GUI). We present the object-oriented design and the general architecture which consist of two layers: the GUI at the top layer, and the processing core filters at the bottom layer. We apply the framework for segmenting different real-case medical image scenarios on public available datasets including bladder and prostate segmentation from 2D MRI, and heart segmentation in 3D CT. Our experiments on these concrete problems show that this framework facilitates complex and multi-object segmentation goals while providing a fast prototyping open-source segmentation tool.

SAR image classification based on CNN in real and simulation datasets

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Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation.

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Xue, Yuan; Xu, Tao; Zhang, Han; Long, L Rodney; Huang, Xiaolei

2018-05-03

Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L 1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels. In our SegAN framework, the segmentor and critic networks are trained in an alternating fashion in a min-max game: The critic is trained by maximizing a multi-scale loss function, while the segmentor is trained with only gradients passed along by the critic, with the aim to minimize the multi-scale loss function. We show that such a SegAN framework is more effective and stable for the segmentation task, and it leads to better performance than the state-of-the-art U-net segmentation method. We tested our SegAN method using datasets from the MICCAI BRATS brain tumor segmentation challenge. Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.

Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces

KAUST Repository

Khan, Naeemullah

2017-11-09

We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions. The energy is formulated by integrating a continuum of scales from a scale space computed from the heat equation within regions. We show that the energy can be optimized without computing a continuum of scales, but instead from a single scale. This makes the method computationally efficient in comparison to energies using a discrete set of scales. We apply our method to texture and motion segmentation. Experiments on benchmark datasets show that a continuum of scales leads to better segmentation accuracy over discrete scales and other competing methods.

Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces

KAUST Repository

Khan, Naeemullah; Hong, Byung-Woo; Yezzi, Anthony; Sundaramoorthi, Ganesh

2017-01-01

We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions. The energy is formulated by integrating a continuum of scales from a scale space computed from the heat equation within regions. We show that the energy can be optimized without computing a continuum of scales, but instead from a single scale. This makes the method computationally efficient in comparison to energies using a discrete set of scales. We apply our method to texture and motion segmentation. Experiments on benchmark datasets show that a continuum of scales leads to better segmentation accuracy over discrete scales and other competing methods.

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.

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Dolz, Jose; Laprie, Anne; Ken, Soléakhéna; Leroy, Henri-Arthur; Reyns, Nicolas; Massoptier, Laurent; Vermandel, Maximilien

2016-01-01

To constrain the risk of severe toxicity in radiotherapy and radiosurgery, precise volume delineation of organs at risk is required. This task is still manually performed, which is time-consuming and prone to observer variability. To address these issues, and as alternative to atlas-based segmentation methods, machine learning techniques, such as support vector machines (SVM), have been recently presented to segment subcortical structures on magnetic resonance images (MRI). SVM is proposed to segment the brainstem on MRI in multicenter brain cancer context. A dataset composed by 14 adult brain MRI scans is used to evaluate its performance. In addition to spatial and probabilistic information, five different image intensity values (IIVs) configurations are evaluated as features to train the SVM classifier. Segmentation accuracy is evaluated by computing the Dice similarity coefficient (DSC), absolute volumes difference (AVD) and percentage volume difference between automatic and manual contours. Mean DSC for all proposed IIVs configurations ranged from 0.89 to 0.90. Mean AVD values were below 1.5 cm(3), where the value for best performing IIVs configuration was 0.85 cm(3), representing an absolute mean difference of 3.99% with respect to the manual segmented volumes. Results suggest consistent volume estimation and high spatial similarity with respect to expert delineations. The proposed approach outperformed presented methods to segment the brainstem, not only in volume similarity metrics, but also in segmentation time. Preliminary results showed that the approach might be promising for adoption in clinical use.

Spinal cord grey matter segmentation challenge.

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Prados, Ferran; Ashburner, John; Blaiotta, Claudia; Brosch, Tom; Carballido-Gamio, Julio; Cardoso, Manuel Jorge; Conrad, Benjamin N; Datta, Esha; Dávid, Gergely; Leener, Benjamin De; Dupont, Sara M; Freund, Patrick; Wheeler-Kingshott, Claudia A M Gandini; Grussu, Francesco; Henry, Roland; Landman, Bennett A; Ljungberg, Emil; Lyttle, Bailey; Ourselin, Sebastien; Papinutto, Nico; Saporito, Salvatore; Schlaeger, Regina; Smith, Seth A; Summers, Paul; Tam, Roger; Yiannakas, Marios C; Zhu, Alyssa; Cohen-Adad, Julien

2017-05-15

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Hysteretic features of Ising-type segmented nanostructure with alternating magnetic wires

International Nuclear Information System (INIS)

Kantar, Ersin

2016-01-01

In the present study, a theoretical approach to investigate the hysteresis behaviors in segmented nanowires is described and applied to spin-1/2 and spin-1 hexagonal nanowire. The hysteresis loop, coercive field and remanent magnetization of a segmented Ising nanowire (SIN) are obtained by using the effective-field theory with correlations. The effects of the temperature, crystal field and geometrical parameters of nanowires on the hysteresis behaviors of the system are investigated. A number of characteristic behaviors are found, such as the occurrence of single and triple hysteresis loops for appropriate values of the crystal field. The hysteresis behaviors are also strongly dependent on geometrical parameters. Comparisons between the obtained theoretical results and some experimental works of segmented nanowire arrays with hysteresis behaviors are made and a very good agreement is obtained. - Highlights: • The hysteresis behaviors of a segmented Ising nanowire are obtained. • The effective-field theory with correlations are used to calculations. • The effects of the temperature and crystal field on the system are investigated. • The geometrical parameters have a significant effect on the system are observed. • The single and triple loops for appropriate values of the crystal field are obtained.

Blood vessels segmentation of hatching eggs based on fully convolutional networks

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Geng, Lei; Qiu, Ling; Wu, Jun; Xiao, Zhitao

2018-04-01

FCN, trained end-to-end, pixels-to-pixels, predict result of each pixel. It has been widely used for semantic segmentation. In order to realize the blood vessels segmentation of hatching eggs, a method based on FCN is proposed in this paper. The training datasets are composed of patches extracted from very few images to augment data. The network combines with lower layer and deconvolution to enables precise segmentation. The proposed method frees from the problem that training deep networks need large scale samples. Experimental results on hatching eggs demonstrate that this method can yield more accurate segmentation outputs than previous researches. It provides a convenient reference for fertility detection subsequently.

A web-based procedure for liver segmentation in CT images

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Yuan, Rong; Luo, Ming; Wang, Luyao; Xie, Qingguo

2015-03-01

Liver segmentation in CT images has been acknowledged as a basic and indispensable part in systems of computer aided liver surgery for operation design and risk evaluation. In this paper, we will introduce and implement a web-based procedure for liver segmentation to help radiologists and surgeons get an accurate result efficiently and expediently. Several clinical datasets are used to evaluate the accessibility and the accuracy. This procedure seems a promising approach for extraction of liver volumetry of various shapes. Moreover, it is possible for user to access the segmentation wherever the Internet is available without any specific machine.

Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial.

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Geraghty, John P; Grogan, Garry; Ebert, Martin A

2013-04-30

This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients ('benchmarking cases'), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 "RADAR" trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation algorithms based on image-registration as in iPlan, it is apparent that agreement between observer and automatic segmentation will be a function of patient-specific image characteristics, particularly for anatomy with poor contrast definition. For this reason, it is suggested that automatic registration based on transformation of a single reference dataset

Malignant pleural mesothelioma segmentation for photodynamic therapy planning.

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Brahim, Wael; Mestiri, Makram; Betrouni, Nacim; Hamrouni, Kamel

2018-04-01

Medical imaging modalities such as computed tomography (CT) combined with computer-aided diagnostic processing have already become important part of clinical routine specially for pleural diseases. The segmentation of the thoracic cavity represents an extremely important task in medical imaging for different reasons. Multiple features can be extracted by analyzing the thoracic cavity space and these features are signs of pleural diseases including the malignant pleural mesothelioma (MPM) which is the main focus of our research. This paper presents a method that detects the MPM in the thoracic cavity and plans the photodynamic therapy in the preoperative phase. This is achieved by using a texture analysis of the MPM region combined with a thoracic cavity segmentation method. The algorithm to segment the thoracic cavity consists of multiple stages. First, the rib cage structure is segmented using various image processing techniques. We used the segmented rib cage to detect feature points which represent the thoracic cavity boundaries. Next, the proposed method segments the structures of the inner thoracic cage and fits 2D closed curves to the detected pleural cavity features in each slice. The missing bone structures are interpolated using a prior knowledge from manual segmentation performed by an expert. Next, the tumor region is segmented inside the thoracic cavity using a texture analysis approach. Finally, the contact surface between the tumor region and the thoracic cavity curves is reconstructed in order to plan the photodynamic therapy. Using the adjusted output of the thoracic cavity segmentation method and the MPM segmentation method, we evaluated the contact surface generated from these two steps by comparing it to the ground truth. For this evaluation, we used 10 CT scans with pathologically confirmed MPM at stages 1 and 2. We obtained a high similarity rate between the manually planned surface and our proposed method. The average value of Jaccard index

Cross-Cultural Concept Mapping of Standardized Datasets

DEFF Research Database (Denmark)

Kano Glückstad, Fumiko

2012-01-01

This work compares four feature-based similarity measures derived from cognitive sciences. The purpose of the comparative analysis is to verify the potentially most effective model that can be applied for mapping independent ontologies in a culturally influenced domain [1]. Here, datasets based...

Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.

Science.gov (United States)

Pavliha, Denis; Mušič, Maja M; Serša, Gregor; Miklavčič, Damijan

2013-01-01

Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.

Shape-Tailored Features and their Application to Texture Segmentation

KAUST Repository

Khan, Naeemullah

2014-01-01

Texture Segmentation is one of the most challenging areas of computer vision. One reason for this difficulty is the huge variety and variability of textures occurring in real world, making it very difficult to quantitatively study textures. One

Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

Directory of Open Access Journals (Sweden)

I. Cruz-Aceves

2013-01-01

Full Text Available This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

Assessing the scale of tumor heterogeneity by complete hierarchical segmentation of MRI

International Nuclear Information System (INIS)

Gensheimer, Michael F; Ermoian, Ralph P; Hawkins, Douglas S; Trister, Andrew D

2015-01-01

In many cancers, intratumoral heterogeneity has been found in histology, genetic variation and vascular structure. We developed an algorithm to interrogate different scales of heterogeneity using clinical imaging. We hypothesize that heterogeneity of perfusion at coarse scale may correlate with treatment resistance and propensity for disease recurrence. The algorithm recursively segments the tumor image into increasingly smaller regions. Each dividing line is chosen so as to maximize signal intensity difference between the two regions. This process continues until the tumor has been divided into single voxels, resulting in segments at multiple scales. For each scale, heterogeneity is measured by comparing each segmented region to the adjacent region and calculating the difference in signal intensity histograms. Using digital phantom images, we showed that the algorithm is robust to image artifacts and various tumor shapes. We then measured the primary tumor scales of contrast enhancement heterogeneity in MRI of 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival. Coarser scale of maximum signal intensity heterogeneity was prognostic of shorter survival (p = 0.05). By contrast, two fractal parameters and three Haralick texture features were not prognostic. In summary, our algorithm produces a biologically motivated segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. If validated on a larger dataset, this prognostic imaging biomarker could be useful to identify patients at higher risk for recurrence and candidates for alternative treatment. (paper)

Comparison of features response in texture-based iris segmentation

CSIR Research Space (South Africa)

Bachoo, A

2009-03-01

Full Text Available the Fisher linear discriminant and the iris region of interest is extracted. Four texture description methods are compared for segmenting iris texture using a region based pattern classification approach: Grey Level Co-occurrence Matrix (GLCM), Discrete...

Segmentation of liver tumors on CT images

International Nuclear Information System (INIS)

Pescia, D.

2011-01-01

This thesis is dedicated to 3D segmentation of liver tumors in CT images. This is a task of great clinical interest since it allows physicians benefiting from reproducible and reliable methods for segmenting such lesions. Accurate segmentation would indeed help them during the evaluation of the lesions, the choice of treatment and treatment planning. Such a complex segmentation task should cope with three main scientific challenges: (i) the highly variable shape of the structures being sought, (ii) their similarity of appearance compared with their surrounding medium and finally (iii) the low signal to noise ratio being observed in these images. This problem is addressed in a clinical context through a two step approach, consisting of the segmentation of the entire liver envelope, before segmenting the tumors which are present within the envelope. We begin by proposing an atlas-based approach for computing pathological liver envelopes. Initially images are pre-processed to compute the envelopes that wrap around binary masks in an attempt to obtain liver envelopes from estimated segmentation of healthy liver parenchyma. A new statistical atlas is then introduced and used to segmentation through its diffeomorphic registration to the new image. This segmentation is achieved through the combination of image matching costs as well as spatial and appearance prior using a multi-scale approach with MRF. The second step of our approach is dedicated to lesions segmentation contained within the envelopes using a combination of machine learning techniques and graph based methods. First, an appropriate feature space is considered that involves texture descriptors being determined through filtering using various scales and orientations. Then, state of the art machine learning techniques are used to determine the most relevant features, as well as the hyper plane that separates the feature space of tumoral voxels to the ones corresponding to healthy tissues. Segmentation is then

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

Science.gov (United States)

Dalmış, Mehmet Ufuk; Litjens, Geert; Holland, Katharina; Setio, Arnaud; Mann, Ritse; Karssemeijer, Nico; Gubern-Mérida, Albert

2017-02-01

Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep-learning approach known as "U-net." We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in-house developed workstation. We experimented with the application of U-net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U-nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3-class (3C) U-net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas-based method and a sheetness-based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed

Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

Science.gov (United States)

Tan, Li Kuo; Liew, Yih Miin; Lim, Einly; McLaughlin, Robert A

2017-07-01

Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation. Copyright © 2017 Elsevier B.V. All rights reserved.

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

Science.gov (United States)

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Directory of Open Access Journals (Sweden)

Ahmad Jalal

2017-08-01

Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

Deep-learning derived features for lung nodule classification with limited datasets

Science.gov (United States)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

A hybrid organic-inorganic perovskite dataset

Science.gov (United States)

Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi; Ramprasad, Rampi

2017-05-01

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository (http://khazana.uconn.edu/), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found.

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

Science.gov (United States)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Integration of heterogeneous features for remote sensing scene classification

Science.gov (United States)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Science.gov (United States)

Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

2018-04-01

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

Pleural effusion segmentation in thin-slice CT

Science.gov (United States)

Donohue, Rory; Shearer, Andrew; Bruzzi, John; Khosa, Huma

2009-02-01

A pleural effusion is excess fluid that collects in the pleural cavity, the fluid-filled space that surrounds the lungs. Surplus amounts of such fluid can impair breathing by limiting the expansion of the lungs during inhalation. Measuring the fluid volume is indicative of the effectiveness of any treatment but, due to the similarity to surround regions, fragments of collapsed lung present and topological changes; accurate quantification of the effusion volume is a difficult imaging problem. A novel code is presented which performs conditional region growth to accurately segment the effusion shape across a dataset. We demonstrate the applicability of our technique in the segmentation of pleural effusion and pulmonary masses.

An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets

Directory of Open Access Journals (Sweden)

Kang Zhang

2014-01-01

Full Text Available Clustering has been widely used in different fields of science, technology, social science, and so forth. In real world, numeric as well as categorical features are usually used to describe the data objects. Accordingly, many clustering methods can process datasets that are either numeric or categorical. Recently, algorithms that can handle the mixed data clustering problems have been developed. Affinity propagation (AP algorithm is an exemplar-based clustering method which has demonstrated good performance on a wide variety of datasets. However, it has limitations on processing mixed datasets. In this paper, we propose a novel similarity measure for mixed type datasets and an adaptive AP clustering algorithm is proposed to cluster the mixed datasets. Several real world datasets are studied to evaluate the performance of the proposed algorithm. Comparisons with other clustering algorithms demonstrate that the proposed method works well not only on mixed datasets but also on pure numeric and categorical datasets.

Congenital segmental dilatation of the colon

African Journals Online (AJOL)

Congenital segmental dilatation of the colon is a rare cause of intestinal obstruction in neonates. We report a case of congenital segmental dilatation of the colon and highlight the clinical, radiological, and histopathological features of this entity. Proper surgical treatment was initiated on the basis of preoperative radiological ...

Solar Features

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Collection includes a variety of solar feature datasets contributed by a number of national and private solar observatories located worldwide.

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features.

Science.gov (United States)

Dao, Duy; Salehizadeh, S M A; Noh, Yeonsik; Chong, Jo Woon; Cho, Chae Ho; McManus, Dave; Darling, Chad E; Mendelson, Yitzhak; Chon, Ki H

2017-09-01

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach

Exploring massive, genome scale datasets with the genometricorr package

KAUST Repository

Favorov, Alexander; Mularoni, Loris; Cope, Leslie M.; Medvedeva, Yulia; Mironov, Andrey A.; Makeev, Vsevolod J.; Wheelan, Sarah J.

2012-01-01

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. © 2012 Favorov et al.

Exploring massive, genome scale datasets with the genometricorr package

KAUST Repository

Favorov, Alexander

2012-05-31

We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. © 2012 Favorov et al.

Shape-tailored local descriptors and their application to segmentation and tracking

KAUST Repository

Khan, Naeemullah; Algarni, Marei Saeed Mohammed; Yezzi, Anthony; Sundaramoorthi, Ganesh

2015-01-01

We propose new dense descriptors for texture segmentation. Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients. These scale spaces are defined by Poisson-like partial differential equations. A key property of our new descriptors is that they do not aggregate image data across the boundary of the region, in contrast to existing descriptors based on aggregation of oriented gradients. As an example, we show how the descriptor can be incorporated in a Mumford-Shah energy for texture segmentation. We test our method on several challenging datasets for texture segmentation and textured object tracking. Experiments indicate that our descriptors lead to more accurate segmentation than non-shape dependent descriptors and the state-of-the-art in texture segmentation.

Shape-tailored local descriptors and their application to segmentation and tracking

KAUST Repository

Khan, Naeemullah

2015-06-07

We propose new dense descriptors for texture segmentation. Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients. These scale spaces are defined by Poisson-like partial differential equations. A key property of our new descriptors is that they do not aggregate image data across the boundary of the region, in contrast to existing descriptors based on aggregation of oriented gradients. As an example, we show how the descriptor can be incorporated in a Mumford-Shah energy for texture segmentation. We test our method on several challenging datasets for texture segmentation and textured object tracking. Experiments indicate that our descriptors lead to more accurate segmentation than non-shape dependent descriptors and the state-of-the-art in texture segmentation.

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules.

Science.gov (United States)

Feng, Xinyang; Yang, Jie; Laine, Andrew F; Angelini, Elsa D

2017-09-01

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.

Automatic data-driven real-time segmentation and recognition of surgical workflow.

Science.gov (United States)

Dergachyova, Olga; Bouget, David; Huaulmé, Arnaud; Morandi, Xavier; Jannin, Pierre

2016-06-01

With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection. The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision. On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases. Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.

Exploring massive, genome scale datasets with the GenometriCorr package.

Directory of Open Access Journals (Sweden)

Alexander Favorov

2012-05-01

Full Text Available We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features, for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets.The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.

PTBS segmentation scheme for synthetic aperture radar

Science.gov (United States)

Friedland, Noah S.; Rothwell, Brian J.

1995-07-01

The Image Understanding Group at Martin Marietta Technologies in Denver, Colorado has developed a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system using an integrated resource architecture (IRA). IRA, an adaptive Markov random field (MRF) environment, utilizes information from image, model, and neighborhood resources to create a discrete, 2D feature-based world description (FBWD). The IRA FBWD features are peak, target, background and shadow (PTBS). These features have been shown to be very useful for target discrimination. The FBWD is used to accrue evidence over a model hypothesis set. This paper presents the PTBS segmentation process utilizing two IRA resources. The image resource (IR) provides generic (the physics of image formation) and specific (the given image input) information. The neighborhood resource (NR) provides domain knowledge of localized FBWD site behaviors. A simulated annealing optimization algorithm is used to construct a `most likely' PTBS state. Results on simulated imagery illustrate the power of this technique to correctly segment PTBS features, even when vehicle signatures are immersed in heavy background clutter. These segmentations also suppress sidelobe effects and delineate shadows.

Temporal Feature Integration for Music Organisation

DEFF Research Database (Denmark)

Meng, Anders

2006-01-01

This Ph.D. thesis focuses on temporal feature integration for music organisation. Temporal feature integration is the process of combining all the feature vectors of a given time-frame into a single new feature vector in order to capture relevant information in the frame. Several existing methods...... for handling sequences of features are formulated in the temporal feature integration framework. Two datasets for music genre classification have been considered as valid test-beds for music organisation. Human evaluations of these, have been obtained to access the subjectivity on the datasets. Temporal...... ranking' approach is proposed for ranking the short-time features at larger time-scales according to their discriminative power in a music genre classification task. The multivariate AR (MAR) model has been proposed for temporal feature integration. It effectively models local dynamical structure...

Current segmented gamma-ray scanner technology

International Nuclear Information System (INIS)

Bjork, C.W.

1987-01-01

A new generation of segmented gamma-ray scanners has been developed at Los Alamos for scrap and waste measurements at the Savannah River Plant and the Los Alamos Plutonium Facility. The new designs are highly automated and exhibit special features such as good segmentation and thorough shielding to improve performance

PROVIDING GEOGRAPHIC DATASETS AS LINKED DATA IN SDI

Directory of Open Access Journals (Sweden)

E. Hietanen

2016-06-01

Full Text Available In this study, a prototype service to provide data from Web Feature Service (WFS as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF data format. Next, a Web Ontology Language (OWL ontology is created to describe the dataset information content using the Open Geospatial Consortium’s (OGC GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID. The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

Radiographic features of congenital segmental dilation of the intestine in a german shepherd dog.

Science.gov (United States)

Johnson, Laura; Simone, Kristina; Cullen, John; Talley, Ashley; Cohen, Eli B

2017-12-04

A 10-week-old spayed female German Shepherd Dog was presented for acute vomiting and diarrhea. There was no reported foreign body or toxin ingestion. Radiographs showed a severely (∼11 × 7 cm), focally distended right abdominal intestinal segment containing gas and soft tissue material. Other small intestinal segments were segmentally gas distended. Celiotomy identified a ∼9 cm focally dilated segment at the jejunoileal junction with no aborad luminal obstruction. Resection and anastomosis of the dilation was performed. Histopathology showed mild mucosal inflammation, but otherwise normal wall layering and autonomic ganglia. Radiographic and histopathologic findings were consistent with congenital segmental dilation of the intestine. © 2017 American College of Veterinary Radiology.

Artificial intelligence (AI) systems for interpreting complex medical datasets.

Science.gov (United States)

Altman, R B

2017-05-01

Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

Segmentation and visual analysis of whole-body mouse skeleton microSPECT.

Directory of Open Access Journals (Sweden)

Artem Khmelinskii

Full Text Available Whole-body SPECT small animal imaging is used to study cancer, and plays an important role in the development of new drugs. Comparing and exploring whole-body datasets can be a difficult and time-consuming task due to the inherent heterogeneity of the data (high volume/throughput, multi-modality, postural and positioning variability. The goal of this study was to provide a method to align and compare side-by-side multiple whole-body skeleton SPECT datasets in a common reference, thus eliminating acquisition variability that exists between the subjects in cross-sectional and multi-modal studies. Six whole-body SPECT/CT datasets of BALB/c mice injected with bone targeting tracers (99mTc-methylene diphosphonate ((99mTc-MDP and (99mTc-hydroxymethane diphosphonate ((99mTc-HDP were used to evaluate the proposed method. An articulated version of the MOBY whole-body mouse atlas was used as a common reference. Its individual bones were registered one-by-one to the skeleton extracted from the acquired SPECT data following an anatomical hierarchical tree. Sequential registration was used while constraining the local degrees of freedom (DoFs of each bone in accordance to the type of joint and its range of motion. The Articulated Planar Reformation (APR algorithm was applied to the segmented data for side-by-side change visualization and comparison of data. To quantitatively evaluate the proposed algorithm, bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances between each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance decreased from 11.5±12.1 to 2.6±2.1 voxels. The proposed approach yielded satisfactory segmentation results with minimal user intervention. It proved to be robust for "incomplete" data (large chunks of skeleton missing and for an intuitive exploration and comparison of multi-modal SPECT

Sparse kernel orthonormalized PLS for feature extraction in large datasets

DEFF Research Database (Denmark)

Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai

2006-01-01

In this paper we are presenting a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm...... is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method...

BIA Indian Lands Dataset (Indian Lands of the United States)

Data.gov (United States)

Federal Geographic Data Committee — The American Indian Reservations / Federally Recognized Tribal Entities dataset depicts feature location, selected demographics and other associated data for the 561...

Tissue Feature-Based and Segmented Deformable Image Registration for Improved Modeling of Shear Movement of Lungs

International Nuclear Information System (INIS)

Xie Yaoqin; Chao Ming; Xing Lei

2009-01-01

Purpose: To report a tissue feature-based image registration strategy with explicit inclusion of the differential motions of thoracic structures. Methods and Materials: The proposed technique started with auto-identification of a number of corresponding points with distinct tissue features. The tissue feature points were found by using the scale-invariant feature transform method. The control point pairs were then sorted into different 'colors' according to the organs in which they resided and used to model the involved organs individually. A thin-plate spline method was used to register a structure characterized by the control points with a given 'color.' The proposed technique was applied to study a digital phantom case and 3 lung and 3 liver cancer patients. Results: For the phantom case, a comparison with the conventional thin-plate spline method showed that the registration accuracy was markedly improved when the differential motions of the lung and chest wall were taken into account. On average, the registration error and standard deviation of the 15 points against the known ground truth were reduced from 3.0 to 0.5 mm and from 1.5 to 0.2 mm, respectively, when the new method was used. A similar level of improvement was achieved for the clinical cases. Conclusion: The results of our study have shown that the segmented deformable approach provides a natural and logical solution to model the discontinuous organ motions and greatly improves the accuracy and robustness of deformable registration.

FRAMEWORK FOR COMPARING SEGMENTATION ALGORITHMS

Directory of Open Access Journals (Sweden)

G. Sithole

2015-05-01

Full Text Available The notion of a ‘Best’ segmentation does not exist. A segmentation algorithm is chosen based on the features it yields, the properties of the segments (point sets it generates, and the complexity of its algorithm. The segmentation is then assessed based on a variety of metrics such as homogeneity, heterogeneity, fragmentation, etc. Even after an algorithm is chosen its performance is still uncertain because the landscape/scenarios represented in a point cloud have a strong influence on the eventual segmentation. Thus selecting an appropriate segmentation algorithm is a process of trial and error. Automating the selection of segmentation algorithms and their parameters first requires methods to evaluate segmentations. Three common approaches for evaluating segmentation algorithms are ‘goodness methods’, ‘discrepancy methods’ and ‘benchmarks’. Benchmarks are considered the most comprehensive method of evaluation. This paper shortcomings in current benchmark methods are identified and a framework is proposed that permits both a visual and numerical evaluation of segmentations for different algorithms, algorithm parameters and evaluation metrics. The concept of the framework is demonstrated on a real point cloud. Current results are promising and suggest that it can be used to predict the performance of segmentation algorithms.

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

Science.gov (United States)

Souza, Roberto; Lucena, Oeslle; Garrafa, Julia; Gobbi, David; Saluzzi, Marina; Appenzeller, Simone; Rittner, Letícia; Frayne, Richard; Lotufo, Roberto

2018-04-15

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p-valuefield strength (p-value<0.001) have statistically significant impacts on skull stripping results. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparison of recent SnIa datasets

International Nuclear Information System (INIS)

Sanchez, J.C. Bueno; Perivolaropoulos, L.; Nesseris, S.

2009-01-01

We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization w(a) = w 0 +w 1 (1−a), according to their Figure of Merit (FoM), their consistency with the cosmological constant (ΛCDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U), (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with ΛCDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy (w 0 ,w 1 ) = (−1.4,2) crossing the phantom divide line w = −1 (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample

Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach

Directory of Open Access Journals (Sweden)

Daniel Peralta

2015-01-01

Full Text Available Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.

Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems.

Science.gov (United States)

Barreiros, Willian; Teodoro, George; Kurc, Tahsin; Kong, Jun; Melo, Alba C M A; Saltz, Joel

2017-09-01

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2×. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46× on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

Background based Gaussian mixture model lesion segmentation in PET

Energy Technology Data Exchange (ETDEWEB)

Soffientini, Chiara Dolores, E-mail: chiaradolores.soffientini@polimi.it; Baselli, Giuseppe [DEIB, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133 (Italy); De Bernardi, Elisabetta [Department of Medicine and Surgery, Tecnomed Foundation, University of Milano—Bicocca, Monza 20900 (Italy); Zito, Felicia; Castellani, Massimo [Nuclear Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milan 20122 (Italy)

2016-05-15

Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was

Site Features

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset consists of various site features from multiple Superfund sites in U.S. EPA Region 8. These data were acquired from multiple sources at different times...

Graph run-length matrices for histopathological image segmentation.

Science.gov (United States)

Tosun, Akif Burak; Gunduz-Demir, Cigdem

2011-03-01

The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation.

A NEW APPROACH TO SEGMENT HANDWRITTEN DIGITS

NARCIS (Netherlands)

Oliveira, L.S.; Lethelier, E.; Bortolozzi, F.; Sabourin, R.

2004-01-01

This article presents a new segmentation approach applied to unconstrained handwritten digits. The novelty of the proposed algorithm is based on the combination of two types of structural features in order to provide the best segmentation path between connected entities. In this article, we first

Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features

Directory of Open Access Journals (Sweden)

Zhang Ya-Nan

2012-05-01

Full Text Available Abstract Background Adenosine-5′-triphosphate (ATP is one of multifunctional nucleotides and plays an important role in cell biology as a coenzyme interacting with proteins. Revealing the binding sites between protein and ATP is significantly important to understand the functionality of the proteins and the mechanisms of protein-ATP complex. Results In this paper, we propose a novel framework for predicting the proteins’ functional residues, through which they can bind with ATP molecules. The new prediction protocol is achieved by combination of sequence evolutional information and bi-profile sampling of multi-view sequential features and the sequence derived structural features. The hypothesis for this strategy is single-view feature can only represent partial target’s knowledge and multiple sources of descriptors can be complementary. Conclusions Prediction performances evaluated by both 5-fold and leave-one-out jackknife cross-validation tests on two benchmark datasets consisting of 168 and 227 non-homologous ATP binding proteins respectively demonstrate the efficacy of the proposed protocol. Our experimental results also reveal that the residue structural characteristics of real protein-ATP binding sites are significant different from those normal ones, for example the binding residues do not show high solvent accessibility propensities, and the bindings prefer to occur at the conjoint points between different secondary structure segments. Furthermore, results also show that performance is affected by the imbalanced training datasets by testing multiple ratios between positive and negative samples in the experiments. Increasing the dataset scale is also demonstrated useful for improving the prediction performances.

Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach.

Science.gov (United States)

Avendi, Michael R; Kheradvar, Arash; Jafarkhani, Hamid

2017-12-01

This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end-diastolic volume (0.98), end-systolic volume (0.99), and ejection fraction (0.93) were observed. Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers. Magn Reson Med 78:2439-2448, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling

DEFF Research Database (Denmark)

Puonti, Oula; Iglesias, Juan Eugenio; Van Leemput, Koen

2016-01-01

the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable...

Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation.

Science.gov (United States)

Joukov, Vladimir; Bonnet, Vincent; Karg, Michelle; Venture, Gentiane; Kulic, Dana

2018-02-01

This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features, such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37%, respectively, estimates joint angles with 2.4° root mean squared error, and segments the motion into repetitions with 96% accuracy.

Multi scales based sparse matrix spectral clustering image segmentation

Science.gov (United States)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

An improved method for pancreas segmentation using SLIC and interactive region merging

Science.gov (United States)

Zhang, Liyuan; Yang, Huamin; Shi, Weili; Miao, Yu; Li, Qingliang; He, Fei; He, Wei; Li, Yanfang; Zhang, Huimao; Mori, Kensaku; Jiang, Zhengang

2017-03-01

Considering the weak edges in pancreas segmentation, this paper proposes a new solution which integrates more features of CT images by combining SLIC superpixels and interactive region merging. In the proposed method, Mahalanobis distance is first utilized in SLIC method to generate better superpixel images. By extracting five texture features and one gray feature, the similarity measure between two superpixels becomes more reliable in interactive region merging. Furthermore, object edge blocks are accurately addressed by re-segmentation merging process. Applying the proposed method to four cases of abdominal CT images, we segment pancreatic tissues to verify the feasibility and effectiveness. The experimental results show that the proposed method can make segmentation accuracy increase to 92% on average. This study will boost the application process of pancreas segmentation for computer-aided diagnosis system.

Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET

Science.gov (United States)

Tan, Shan; Li, Laquan; Choi, Wookjin; Kang, Min Kyu; D'Souza, Warren D.; Lu, Wei

2017-07-01

Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF  =  9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne’s adaptive thresholding method (DSI  =  0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.

Classification and Weakly Supervised Pain Localization using Multiple Segment Representation.

Science.gov (United States)

Sikka, Karan; Dhall, Abhinav; Bartlett, Marian Stewart

2014-10-01

Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target expression for a given frame is unknown, and (2) the time point and the duration of the pain expression event(s) in each video are unknown. To address these issues we propose a novel framework (referred to as MS-MIL) where each sequence is represented as a bag containing multiple segments, and multiple instance learning (MIL) is employed to handle this weakly labeled data in the form of sequence level ground-truth. These segments are generated via multiple clustering of a sequence or running a multi-scale temporal scanning window, and are represented using a state-of-the-art Bag of Words (BoW) representation. This work extends the idea of detecting facial expressions through 'concept frames' to 'concept segments' and argues through extensive experiments that algorithms such as MIL are needed to reap the benefits of such representation. The key advantages of our approach are: (1) joint detection and localization of painful frames using only sequence-level ground-truth, (2) incorporation of temporal dynamics by representing the data not as individual frames but as segments, and (3) extraction of multiple segments, which is well suited to signals with uncertain temporal location and duration in the video. Extensive experiments on UNBC-McMaster Shoulder Pain dataset highlight the effectiveness of the approach by achieving competitive results on both tasks of pain classification and localization in videos. We also empirically evaluate the contributions of different components of MS-MIL. The paper also includes the visualization of discriminative facial patches, important for pain detection, as discovered by our

Multi-output decision trees for lesion segmentation in multiple sclerosis

Science.gov (United States)

Jog, Amod; Carass, Aaron; Pham, Dzung L.; Prince, Jerry L.

2015-03-01

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

The Enhanced Segment Interconnect for FASTBUS data communications

International Nuclear Information System (INIS)

Machen, D.R.; Downing, R.W.; Kirsten, F.A.; Nelson, R.O.

1987-01-01

The Enhanced Segment Interconnect concept (ESI) for improved FASTBUS data communications is a development supported by the U.S. Department of Energy under the Small Business Innovation Research (SBIR) program. The ESI will contain both the Segment Interconnect (SI) Tyhpe S-1 and an optional buffered interconnect for store-and-forward data communications; fiber-optic-coupled serial ports will provide optional data paths. The ESI can be applied in large FASTBUS-implemented physics experiments whose data-set or data-transmission distance requirements dictate alternate approaches to data communications. This paper describes the functions of the ESI and the status of its development, now 25% complete

Method of manufacturing a large-area segmented photovoltaic module

Science.gov (United States)

Lenox, Carl

2013-11-05

One embodiment of the invention relates to a segmented photovoltaic (PV) module which is manufactured from laminate segments. The segmented PV module includes rectangular-shaped laminate segments formed from rectangular-shaped PV laminates and further includes non-rectangular-shaped laminate segments formed from rectangular-shaped and approximately-triangular-shaped PV laminates. The laminate segments are mechanically joined and electrically interconnected to form the segmented module. Another embodiment relates to a method of manufacturing a large-area segmented photovoltaic module from laminate segments of various shapes. Other embodiments relate to processes for providing a photovoltaic array for installation at a site. Other embodiments and features are also disclosed.

Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

DEFF Research Database (Denmark)

Puonti, Oula; Van Leemput, Koen

2016-01-01

In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional...... restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset...... in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations....

STUDY OF IMAGE SEGMENTATION TECHNIQUES ON RETINAL IMAGES FOR HEALTH CARE MANAGEMENT WITH FAST COMPUTING

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Srikanth Prabhu

2012-02-01

Full Text Available The role of segmentation in image processing is to separate foreground from background. In this process, the features become clearly visible when appropriate filters are applied on the image. In this paper emphasis has been laid on segmentation of biometric retinal images to filter out the vessels explicitly for evaluating the bifurcation points and features for diabetic retinopathy. Segmentation on images is performed by calculating ridges or morphology. Ridges are those areas in the images where there is sharp contrast in features. Morphology targets the features using structuring elements. Structuring elements are of different shapes like disk, line which is used for extracting features of those shapes. When segmentation was performed on retinal images problems were encountered during image pre-processing stage. Also edge detection techniques have been deployed to find out the contours of the retinal images. After the segmentation has been performed, it has been seen that artifacts of the retinal images have been minimal when ridge based segmentation technique was deployed. In the field of Health Care Management, image segmentation has an important role to play as it determines whether a person is normal or having any disease specially diabetes. During the process of segmentation, diseased features are classified as diseased one’s or artifacts. The problem comes when artifacts are classified as diseased ones. This results in misclassification which has been discussed in the analysis Section. We have achieved fast computing with better performance, in terms of speed for non-repeating features, when compared to repeating features.

Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection

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Jaesung Lee

2016-11-01

Full Text Available Multi-label feature selection is designed to select a subset of features according to their importance to multiple labels. This task can be achieved by ranking the dependencies of features and selecting the features with the highest rankings. In a multi-label feature selection problem, the algorithm may be faced with a dataset containing a large number of labels. Because the computational cost of multi-label feature selection increases according to the number of labels, the algorithm may suffer from a degradation in performance when processing very large datasets. In this study, we propose an efficient multi-label feature selection method based on an information-theoretic label selection strategy. By identifying a subset of labels that significantly influence the importance of features, the proposed method efficiently outputs a feature subset. Experimental results demonstrate that the proposed method can identify a feature subset much faster than conventional multi-label feature selection methods for large multi-label datasets.

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature.

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Shigeta, Hironori; Mashita, Tomohiro; Kikuta, Junichi; Seno, Shigeto; Takemura, Haruo; Ishii, Masaru; Matsuda, Hideo

2017-10-01

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

Hard exudates segmentation based on learned initial seeds and iterative graph cut.

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Kusakunniran, Worapan; Wu, Qiang; Ritthipravat, Panrasee; Zhang, Jian

2018-05-01

(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the

A reanalysis dataset of the South China Sea

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Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability. PMID:25977803

Measurement and genetics of human subcortical and hippocampal asymmetries in large datasets.

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Guadalupe, Tulio; Zwiers, Marcel P; Teumer, Alexander; Wittfeld, Katharina; Vasquez, Alejandro Arias; Hoogman, Martine; Hagoort, Peter; Fernandez, Guillen; Buitelaar, Jan; Hegenscheid, Katrin; Völzke, Henry; Franke, Barbara; Fisher, Simon E; Grabe, Hans J; Francks, Clyde

2014-07-01

Functional and anatomical asymmetries are prevalent features of the human brain, linked to gender, handedness, and cognition. However, little is known about the neurodevelopmental processes involved. In zebrafish, asymmetries arise in the diencephalon before extending within the central nervous system. We aimed to identify genes involved in the development of subtle, left-right volumetric asymmetries of human subcortical structures using large datasets. We first tested the feasibility of measuring left-right volume differences in such large-scale samples, as assessed by two automated methods of subcortical segmentation (FSL|FIRST and FreeSurfer), using data from 235 subjects who had undergone MRI twice. We tested the agreement between the first and second scan, and the agreement between the segmentation methods, for measures of bilateral volumes of six subcortical structures and the hippocampus, and their volumetric asymmetries. We also tested whether there were biases introduced by left-right differences in the regional atlases used by the methods, by analyzing left-right flipped images. While many bilateral volumes were measured well (scan-rescan r = 0.6-0.8), most asymmetries, with the exception of the caudate nucleus, showed lower repeatabilites. We meta-analyzed genome-wide association scan results for caudate nucleus asymmetry in a combined sample of 3,028 adult subjects but did not detect associations at genome-wide significance (P left-right patterning of the viscera. Our results provide important information for researchers who are currently aiming to carry out large-scale genome-wide studies of subcortical and hippocampal volumes, and their asymmetries. Copyright © 2013 Wiley Periodicals, Inc.

Online feature selection with streaming features.

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Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

Brain tumor segmentation using holistically nested neural networks in MRI images.

Science.gov (United States)

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of

Validation of Point Clouds Segmentation Algorithms Through Their Application to Several Case Studies for Indoor Building Modelling

Science.gov (United States)

Macher, H.; Landes, T.; Grussenmeyer, P.

2016-06-01

Laser scanners are widely used for the modelling of existing buildings and particularly in the creation process of as-built BIM (Building Information Modelling). However, the generation of as-built BIM from point clouds involves mainly manual steps and it is consequently time consuming and error-prone. Along the path to automation, a three steps segmentation approach has been developed. This approach is composed of two phases: a segmentation into sub-spaces namely floors and rooms and a plane segmentation combined with the identification of building elements. In order to assess and validate the developed approach, different case studies are considered. Indeed, it is essential to apply algorithms to several datasets and not to develop algorithms with a unique dataset which could influence the development with its particularities. Indoor point clouds of different types of buildings will be used as input for the developed algorithms, going from an individual house of almost one hundred square meters to larger buildings of several thousand square meters. Datasets provide various space configurations and present numerous different occluding objects as for example desks, computer equipments, home furnishings and even wine barrels. For each dataset, the results will be illustrated. The analysis of the results will provide an insight into the transferability of the developed approach for the indoor modelling of several types of buildings.

Image segmentation by hierarchial agglomeration of polygons using ecological statistics

Science.gov (United States)

Prasad, Lakshman; Swaminarayan, Sriram

2013-04-23

A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

Unsupervised Segmentation Methods of TV Contents

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Elie El-Khoury

2010-01-01

Full Text Available We present a generic algorithm to address various temporal segmentation topics of audiovisual contents such as speaker diarization, shot, or program segmentation. Based on a GLR approach, involving the ΔBIC criterion, this algorithm requires the value of only a few parameters to produce segmentation results at a desired scale and on most typical low-level features used in the field of content-based indexing. Results obtained on various corpora are of the same quality level than the ones obtained by other dedicated and state-of-the-art methods.

A Comparative Analysis of Classification Algorithms on Diverse Datasets

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M. Alghobiri

2018-04-01

Full Text Available Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

Automated segmentation of dental CBCT image with prior-guided sequential random forests

Energy Technology Data Exchange (ETDEWEB)

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 (United States); Chen, Ken-Chung; Tang, Zhen [Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030 (United States); Xia, James J., E-mail: dgshen@med.unc.edu, E-mail: JXia@HoustonMethodist.org [Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery, Shanghai Jiao Tong University School of Medicine, Shanghai Ninth People’s Hospital, Shanghai 200011 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu, E-mail: JXia@HoustonMethodist.org [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841 (Korea, Republic of)

2016-01-15

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

Automated segmentation of dental CBCT image with prior-guided sequential random forests

International Nuclear Information System (INIS)

Wang, Li; Gao, Yaozong; Shi, Feng; Li, Gang; Chen, Ken-Chung; Tang, Zhen; Xia, James J.; Shen, Dinggang

2016-01-01

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method

Automatic segmentation of male pelvic anatomy on computed tomography images: a comparison with multiple observers in the context of a multicentre clinical trial

International Nuclear Information System (INIS)

Geraghty, John P; Grogan, Garry; Ebert, Martin A

2013-01-01

This study investigates the variation in segmentation of several pelvic anatomical structures on computed tomography (CT) between multiple observers and a commercial automatic segmentation method, in the context of quality assurance and evaluation during a multicentre clinical trial. CT scans of two prostate cancer patients (‘benchmarking cases’), one high risk (HR) and one intermediate risk (IR), were sent to multiple radiotherapy centres for segmentation of prostate, rectum and bladder structures according to the TROG 03.04 “RADAR” trial protocol definitions. The same structures were automatically segmented using iPlan software for the same two patients, allowing structures defined by automatic segmentation to be quantitatively compared with those defined by multiple observers. A sample of twenty trial patient datasets were also used to automatically generate anatomical structures for quantitative comparison with structures defined by individual observers for the same datasets. There was considerable agreement amongst all observers and automatic segmentation of the benchmarking cases for bladder (mean spatial variations < 0.4 cm across the majority of image slices). Although there was some variation in interpretation of the superior-inferior (cranio-caudal) extent of rectum, human-observer contours were typically within a mean 0.6 cm of automatically-defined contours. Prostate structures were more consistent for the HR case than the IR case with all human observers segmenting a prostate with considerably more volume (mean +113.3%) than that automatically segmented. Similar results were seen across the twenty sample datasets, with disagreement between iPlan and observers dominant at the prostatic apex and superior part of the rectum, which is consistent with observations made during quality assurance reviews during the trial. This study has demonstrated quantitative analysis for comparison of multi-observer segmentation studies. For automatic segmentation

Lung tumor segmentation in PET images using graph cuts.

Science.gov (United States)

Ballangan, Cherry; Wang, Xiuying; Fulham, Michael; Eberl, Stefan; Feng, David Dagan

2013-03-01

The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Document flow segmentation for business applications

Science.gov (United States)

Daher, Hani; Belaïd, Abdel

2013-12-01

The aim of this paper is to propose a document flow supervised segmentation approach applied to real world heterogeneous documents. Our algorithm treats the flow of documents as couples of consecutive pages and studies the relationship that exists between them. At first, sets of features are extracted from the pages where we propose an approach to model the couple of pages into a single feature vector representation. This representation will be provided to a binary classifier which classifies the relationship as either segmentation or continuity. In case of segmentation, we consider that we have a complete document and the analysis of the flow continues by starting a new document. In case of continuity, the couple of pages are assimilated to the same document and the analysis continues on the flow. If there is an uncertainty on whether the relationship between the couple of pages should be classified as a continuity or segmentation, a rejection is decided and the pages analyzed until this point are considered as a "fragment". The first classification already provides good results approaching 90% on certain documents, which is high at this level of the system.

Semantic Road Segmentation Via Multi-Scale Ensembles of Learned Features

NARCIS (Netherlands)

Alvarez, J.M.; LeCun, Y.; Gevers, T.; Lopez, A.M.

2012-01-01

Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual

Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images With Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms.

Science.gov (United States)

Rashno, Abdolreza; Koozekanani, Dara D; Drayna, Paul M; Nazari, Behzad; Sadri, Saeed; Rabbani, Hossein; Parhi, Keshab K

2018-05-01

This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image is transformed into three sets: (true), (indeterminate) that represents noise, and (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set , and a new -correction operation is introduced to compute the set in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights . Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of minnesota (UMN) datasets, respectively.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

Science.gov (United States)

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

MIN-CUT BASED SEGMENTATION OF AIRBORNE LIDAR POINT CLOUDS

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S. Ural

2012-07-01

Full Text Available Introducing an organization to the unstructured point cloud before extracting information from airborne lidar data is common in many applications. Aggregating the points with similar features into segments in 3-D which comply with the nature of actual objects is affected by the neighborhood, scale, features and noise among other aspects. In this study, we present a min-cut based method for segmenting the point cloud. We first assess the neighborhood of each point in 3-D by investigating the local geometric and statistical properties of the candidates. Neighborhood selection is essential since point features are calculated within their local neighborhood. Following neighborhood determination, we calculate point features and determine the clusters in the feature space. We adapt a graph representation from image processing which is especially used in pixel labeling problems and establish it for the unstructured 3-D point clouds. The edges of the graph that are connecting the points with each other and nodes representing feature clusters hold the smoothness costs in the spatial domain and data costs in the feature domain. Smoothness costs ensure spatial coherence, while data costs control the consistency with the representative feature clusters. This graph representation formalizes the segmentation task as an energy minimization problem. It allows the implementation of an approximate solution by min-cuts for a global minimum of this NP hard minimization problem in low order polynomial time. We test our method with airborne lidar point cloud acquired with maximum planned post spacing of 1.4 m and a vertical accuracy 10.5 cm as RMSE. We present the effects of neighborhood and feature determination in the segmentation results and assess the accuracy and efficiency of the implemented min-cut algorithm as well as its sensitivity to the parameters of the smoothness and data cost functions. We find that smoothness cost that only considers simple distance

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images.

Science.gov (United States)

Gao, Han; Tang, Yunwei; Jing, Linhai; Li, Hui; Ding, Haifeng

2017-10-24

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

Directory of Open Access Journals (Sweden)

Han Gao

2017-10-01

Full Text Available The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA. Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

Active contour based segmentation of resected livers in CT images

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Oelmann, Simon; Oyarzun Laura, Cristina; Drechsler, Klaus; Wesarg, Stefan

2015-03-01

The majority of state of the art segmentation algorithms are able to give proper results in healthy organs but not in pathological ones. However, many clinical applications require an accurate segmentation of pathological organs. The determination of the target boundaries for radiotherapy or liver volumetry calculations are examples of this. Volumetry measurements are of special interest after tumor resection for follow up of liver regrow. The segmentation of resected livers presents additional challenges that were not addressed by state of the art algorithms. This paper presents a snakes based algorithm specially developed for the segmentation of resected livers. The algorithm is enhanced with a novel dynamic smoothing technique that allows the active contour to propagate with different speeds depending on the intensities visible in its neighborhood. The algorithm is evaluated in 6 clinical CT images as well as 18 artificial datasets generated from additional clinical CT images.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

Science.gov (United States)

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Isomap transform for segmenting human body shapes.

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Cerveri, P; Sarro, K J; Marchente, M; Barros, R M L

2011-09-01

Segmentation of the 3D human body is a very challenging problem in applications exploiting volume capture data. Direct clustering in the Euclidean space is usually complex or even unsolvable. This paper presents an original method based on the Isomap (isometric feature mapping) transform of the volume data-set. The 3D articulated posture is mapped by Isomap in the pose of Da Vinci's Vitruvian man. The limbs are unrolled from each other and separated from the trunk and pelvis, and the topology of the human body shape is recovered. In such a configuration, Hoshen-Kopelman clustering applied to concentric spherical shells is used to automatically group points into the labelled principal curves. Shepard interpolation is utilised to back-map points of the principal curves into the original volume space. The experimental results performed on many different postures have proved the validity of the proposed method. Reliability of less than 2 cm and 3° in the location of the joint centres and direction axes of rotations has been obtained, respectively, which qualifies this procedure as a potential tool for markerless motion analysis.

RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets.

Science.gov (United States)

Del Carratore, Francesco; Jankevics, Andris; Eisinga, Rob; Heskes, Tom; Hong, Fangxin; Breitling, Rainer

2017-09-01

The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based P -value estimation methods have been replaced by exact methods, providing faster and more accurate results. RankProd 2.0 is available at Bioconductor ( https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html ) and as part of the mzMatch pipeline ( http://www.mzmatch.sourceforge.net ). rainer.breitling@manchester.ac.uk. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Joint level-set and spatio-temporal motion detection for cell segmentation.

Science.gov (United States)

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan

Texture Segmentation Based on Wavelet and Kohonen Network for Remotely Sensed Images

NARCIS (Netherlands)

Chen, Z.; Feng, T.J.; Feng, T.J.; Houkes, Z.

1999-01-01

In this paper, an approach based on wavelet decomposition and Kohonen's self-organizing map is developed for image segmentation. After performing the 2D wavelet transform of image, some features are extracted for texture segmentation, and the Kohonen neural network is used to accomplish feature

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

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Mehmet Nergiz

2017-11-01

Full Text Available Retinal vessel segmentation is one of the preliminary tasks for developing diagnosis software systems related to various retinal diseases. In this study, a fully automated vessel segmentation system is proposed. Firstly, the vessels are enhanced using a Frangi Filter. Afterwards, Structure Tensor is applied to the response of the Frangi Filter and a 4-D tensor field is obtained. After decomposing the Eigenvalues of the tensor field, the anisotropy between the principal Eigenvalues are enhanced exponentially. Furthermore, this 4-D tensor field is converted to the 3-D space which is composed of energy, anisotropy and orientation and then a Contrast Limited Adaptive Histogram Equalization algorithm is applied to the energy space. Later, the obtained energy space is multiplied by the enhanced mean surface curvature of itself and the modified 3-D space is converted back to the 4-D tensor field. Lastly, the vessel segmentation is performed by using Otsu algorithm and tensor coloring method which is inspired by the ellipsoid tensor visualization technique. Finally, some post-processing techniques are applied to the segmentation result. In this study, the proposed method achieved mean sensitivity of 0.8123, 0.8126, 0.7246 and mean specificity of 0.9342, 0.9442, 0.9453 as well as mean accuracy of 0.9183, 0.9442, 0.9236 for DRIVE, STARE and CHASE_DB1 datasets, respectively. The mean execution time of this study is 6.104, 6.4525 and 18.8370 s for the aforementioned three datasets respectively.

MO-DE-207B-08: Radiomic CT Features Complement Semantic Annotations to Predict EGFR Mutations in Lung Adenocarcinomas

Energy Technology Data Exchange (ETDEWEB)

Rios Velazquez, E; Parmar, C; Narayan, V; Aerts, H [Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (United States); Liu, Y; Gillies, R [H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL (United States)

2016-06-15

Purpose: To compare the complementary value of quantitative radiomic features to that of radiologist-annotated semantic features in predicting EGFR mutations in lung adenocarcinomas. Methods: Pre-operative CT images of 258 lung adenocarcinoma patients were available. Tumors were segmented using the sing-click ensemble segmentation algorithm. A set of radiomic features was extracted using 3D-Slicer. Test-retest reproducibility and unsupervised dimensionality reduction were applied to select a subset of reproducible and independent radiomic features. Twenty semantic annotations were scored by an expert radiologist, describing the tumor, surrounding tissue and associated findings. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative radiomic and semantic features in 172 patients (training-set, temporal split). Radiomic, semantic and combined radiomic-semantic logistic regression models to predict EGFR mutations were evaluated in and independent validation dataset of 86 patients using the area under the receiver operating curve (AUC). Results: EGFR mutations were found in 77/172 (45%) and 39/86 (45%) of the training and validation sets, respectively. Univariate AUCs showed a similar range for both feature types: radiomics median AUC = 0.57 (range: 0.50 – 0.62); semantic median AUC = 0.53 (range: 0.50 – 0.64, Wilcoxon p = 0.55). After MRMR feature selection, the best-performing radiomic, semantic, and radiomic-semantic logistic regression models, for EGFR mutations, showed a validation AUC of 0.56 (p = 0.29), 0.63 (p = 0.063) and 0.67 (p = 0.004), respectively. Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative radiologist annotations. The prognostic value of informative qualitative semantic features such as cavitation and lobulation is increased with the addition of quantitative textural features from the tumor region.

MO-DE-207B-08: Radiomic CT Features Complement Semantic Annotations to Predict EGFR Mutations in Lung Adenocarcinomas

International Nuclear Information System (INIS)

Rios Velazquez, E; Parmar, C; Narayan, V; Aerts, H; Liu, Y; Gillies, R

2016-01-01

Purpose: To compare the complementary value of quantitative radiomic features to that of radiologist-annotated semantic features in predicting EGFR mutations in lung adenocarcinomas. Methods: Pre-operative CT images of 258 lung adenocarcinoma patients were available. Tumors were segmented using the sing-click ensemble segmentation algorithm. A set of radiomic features was extracted using 3D-Slicer. Test-retest reproducibility and unsupervised dimensionality reduction were applied to select a subset of reproducible and independent radiomic features. Twenty semantic annotations were scored by an expert radiologist, describing the tumor, surrounding tissue and associated findings. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative radiomic and semantic features in 172 patients (training-set, temporal split). Radiomic, semantic and combined radiomic-semantic logistic regression models to predict EGFR mutations were evaluated in and independent validation dataset of 86 patients using the area under the receiver operating curve (AUC). Results: EGFR mutations were found in 77/172 (45%) and 39/86 (45%) of the training and validation sets, respectively. Univariate AUCs showed a similar range for both feature types: radiomics median AUC = 0.57 (range: 0.50 – 0.62); semantic median AUC = 0.53 (range: 0.50 – 0.64, Wilcoxon p = 0.55). After MRMR feature selection, the best-performing radiomic, semantic, and radiomic-semantic logistic regression models, for EGFR mutations, showed a validation AUC of 0.56 (p = 0.29), 0.63 (p = 0.063) and 0.67 (p = 0.004), respectively. Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative radiologist annotations. The prognostic value of informative qualitative semantic features such as cavitation and lobulation is increased with the addition of quantitative textural features from the tumor region.

A density-based segmentation for 3D images, an application for X-ray micro-tomography

International Nuclear Information System (INIS)

Tran, Thanh N.; Nguyen, Thanh T.; Willemsz, Tofan A.; Kessel, Gijs van; Frijlink, Henderik W.; Voort Maarschalk, Kees van der

2012-01-01

Highlights: ► We revised the DBSCAN algorithm for segmentation and clustering of large 3D image dataset and classified multivariate image. ► The algorithm takes into account the coordinate system of the image data to improve the computational performance. ► The algorithm solved the instability problem in boundaries detection of the original DBSCAN. ► The segmentation results were successfully validated with synthetic 3D image and 3D XMT image of a pharmaceutical powder. - Abstract: Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised classification algorithm which has been widely used in many areas with its simplicity and its ability to deal with hidden clusters of different sizes and shapes and with noise. However, the computational issue of the distance table and the non-stability in detecting the boundaries of adjacent clusters limit the application of the original algorithm to large datasets such as images. In this paper, the DBSCAN algorithm was revised and improved for image clustering and segmentation. The proposed clustering algorithm presents two major advantages over the original one. Firstly, the revised DBSCAN algorithm made it applicable for large 3D image dataset (often with millions of pixels) by using the coordinate system of the image data. Secondly, the revised algorithm solved the non-stability issue of boundary detection in the original DBSCAN. For broader applications, the image dataset can be ordinary 3D images or in general, it can also be a classification result of other type of image data e.g. a multivariate image.

Assessment of the Log-Euclidean Metric Performance in Diffusion Tensor Image Segmentation

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Mostafa Charmi

2010-06-01

Full Text Available Introduction: Appropriate definition of the distance measure between diffusion tensors has a deep impact on Diffusion Tensor Image (DTI segmentation results. The geodesic metric is the best distance measure since it yields high-quality segmentation results. However, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. The main goal of this paper is to assess the possible substitution of the geodesic metric with the Log-Euclidean one to reduce the computational cost of a statistical surface evolution algorithm. Materials and Methods: We incorporated the Log-Euclidean metric in the statistical surface evolution algorithm framework. To achieve this goal, the statistics and gradients of diffusion tensor images were defined using the Log-Euclidean metric. Numerical implementation of the segmentation algorithm was performed in the MATLAB software using the finite difference techniques. Results: In the statistical surface evolution framework, the Log-Euclidean metric was able to discriminate the torus and helix patterns in synthesis datasets and rat spinal cords in biological phantom datasets from the background better than the Euclidean and J-divergence metrics. In addition, similar results were obtained with the geodesic metric. However, the main advantage of the Log-Euclidean metric over the geodesic metric was the dramatic reduction of computational cost of the segmentation algorithm, at least by 70 times. Discussion and Conclusion: The qualitative and quantitative results have shown that the Log-Euclidean metric is a good substitute for the geodesic metric when using a statistical surface evolution algorithm in DTIs segmentation.

Ultrasonographic features of normal lower ureters

International Nuclear Information System (INIS)

Kim, Young Soon; Bae, M. Y.; Park, K. J.; Jeon, H. S.; Lee, J. H.

1990-01-01

Although ultrasonographic evaluation of the normal ureters is difficult due to bowel gas, the lower segment of the normal ureters can be visualized using the urinary bladder as an acoustic window. Authors prospetively performed ultrasonography with the standard suprapubic technique and analyzed the ultrasonographic features of normal lower ureters in 79 cases(77%). Length of visualized segment of the distal ureter ranged frp, 1.5cm to 7.2 cm and the visualized segment did not exceed 3.9mm in maximum diameter. Knowledge of sonographic features of the normal lower ureters can be helpful in the evaluation of pathologic or suspected pathologic conditions of the lower ureters

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Real-time object detection and semantic segmentation for autonomous driving

Science.gov (United States)

Li, Baojun; Liu, Shun; Xu, Weichao; Qiu, Wei

2018-02-01

In this paper, we proposed a Highly Coupled Network (HCNet) for joint objection detection and semantic segmentation. It follows that our method is faster and performs better than the previous approaches whose decoder networks of different tasks are independent. Besides, we present multi-scale loss architecture to learn better representation for different scale objects, but without extra time in the inference phase. Experiment results show that our method achieves state-of-the-art results on the KITTI datasets. Moreover, it can run at 35 FPS on a GPU and thus is a practical solution to object detection and semantic segmentation for autonomous driving.

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

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Tomoaki Nakamura

2017-12-01

Full Text Available Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM, the emission distributions of which are Gaussian processes (GPs. Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods.

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization