keyword
MENU ▼
Read by QxMD icon Read
search

Texture classification

keyword
https://www.readbyqxmd.com/read/27917577/inter-and-intra-specific-diversity-in-cistus-l-cistaceae-seeds-analyzed-by-computer-vision-techniques
#1
Marisol Lo Bianco, Oscar Grillo, Eva Cañadas, Gianfranco Venora, Gianluigi Bacchetta
This work aims to discriminate among different species of the genus Cistus, using seeds parameters and following the scientific plant names included as accepted in The Plant List. Also the intra-specific phenotypic differentiation of C. creticus, through the comparison of three subspecies (C. creticus subsp. creticus, C. c. subsp. eriocephalus and C. c. subsp. corsicus) and the inter-population variability among five C. creticus subsp. eriocephalus populations, were evaluated. Seed mean weight and 137 morpho-colorimetric quantitative variables, describing shape, size, color and textural seed traits, were measured using image analysis techniques...
December 5, 2016: Plant Biology
https://www.readbyqxmd.com/read/27915126/stroke-risk-stratification-and-its-validation-using-ultrasonic-echolucent-carotid-wall-plaque-morphology-a-machine-learning-paradigm
#2
Tadashi Araki, Pankaj K Jain, Harman S Suri, Narendra D Londhe, Nobutaka Ikeda, Ayman El-Baz, Vimal K Shrivastava, Luca Saba, Andrew Nicolaides, Shoaib Shafique, John R Laird, Ajay Gupta, Jasjit S Suri
Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque...
November 27, 2016: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/27913337/melanoma-classification-on-dermoscopy-images-using-a-neural-network-ensemble-model
#3
Feng-Ying Xie, Haidi Fan, Li Yang, Zhi-Guo Jiang, Ru-Song Meng, Alan Bovik
We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, features descriptive of tumor color, texture and border are extracted; and third, lesion objects are classified using a classifier based on a neural network ensemble model. In clinical situations, lesions occur that are too large to be entirely contained within the dermoscopy image...
December 1, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/27911353/computer-aided-classification-of-mammographic-masses-using-visually-sensitive-image-features
#4
Yunzhi Wang, Faranak Aghaei, Ali Zarafshani, Yuchen Qiu, Wei Qian, Bin Zheng
PURPOSE: To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. METHODS: An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms...
November 30, 2016: Journal of X-ray Science and Technology
https://www.readbyqxmd.com/read/27908167/computer-aided-prognosis-for-cell-death-categorization-and-prediction-in-vivo-using-quantitative-ultrasound-and-machine-learning-techniques
#5
M J Gangeh, A Hashim, A Giles, L Sannachi, G J Czarnota
PURPOSE: At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here, the authors have developed, for the first time, a complete computer-aided-prognosis (CAP) system based on multiparametric quantitative ultrasound (QUS) spectroscopy methods in association with texture descriptors and advanced machine learning techniques...
December 2016: Medical Physics
https://www.readbyqxmd.com/read/27908158/similarity-measurement-of-lung-masses-for-medical-image-retrieval-using-kernel-based-semisupervised-distance-metric
#6
Guohui Wei, He Ma, Wei Qian, Min Qiu
PURPOSE: To develop a new algorithm to measure the similarity between the query lung mass and reference lung mass data set for content-based medical image retrieval (CBMIR). METHODS: A lung mass data set including 746 mass regions of interest (ROIs) was assembled. Among them, 375 ROIs depicted malignant lesions and 371 depicted benign lesions. Each mass ROI is represented by a vector of 26 texture features. A kernel function was employed to map the original data in input space to a feature space...
December 2016: Medical Physics
https://www.readbyqxmd.com/read/27895962/texture-analysis-of-supraspinatus-ultrasound-image-for-computer-aided-diagnostic-system
#7
Byung Eun Park, Won Seuk Jang, Sun Kook Yoo
OBJECTIVES: In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS: First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest. Our results show that first order statistics (mean, skewness, entropy, energy, smoothness), GLCM (correlation, contrast, energy, entropy, difference entropy, homogeneity, maximum probability, sum average, sum entropy), and GLRLM features are helpful to distinguish a normal supraspinatus tendon and an abnormal supraspinatus tendon...
October 2016: Healthcare Informatics Research
https://www.readbyqxmd.com/read/27893380/area-determination-of-diabetic-foot-ulcer-images-using-a-cascaded-two-stage-svm-based-classification
#8
Lei Wang, Peder Pedersen, Emmanuel Agu, Diane Strong, Bengisu Tulu
It is standard practice for clinicians and nurses to primarily assess patients' wounds via visual examination. This subjective method can be inaccurate in wound assessment and also represents a significant clinical workload. Hence, computer-based systems, especially implemented on mobile devices, can provide automatic, quantitative wound assessment and can thus be valuable for accurately monitoring wound healing status. Out of all wound assessment parameters, the measurement of the wound area is the most suitable for automated analysis...
November 23, 2016: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/27886713/automated-classification-of-pap-smear-images-to-detect-cervical-dysplasia
#9
Kangkana Bora, Manish Chowdhury, Lipi B Mahanta, Malay Kumar Kundu, Anup Kumar Das
BACKGROUND AND OBJECTIVES: The present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades. METHODS: The classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix...
January 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/27875222/locally-supervised-deep-hybrid-model-for-scene-recognition
#10
Sheng Guo, Weilin Huang, Limin Wang, Yu Qiao
Convolutional neural networks (CNN) have recently achieved remarkable successes in various image classification and understanding tasks. The deep features obtained at the top fully-connected layer of the CNN (FC-features) exhibit rich global semantic information and are extremely effective in image classification. On the other hand, the convolutional features in the middle layers of the CNN also contain meaningful local information, but are not fully explored for image representation. In this paper, we propose a novel Locally-Supervised Deep Hybrid Model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition...
November 16, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27872871/microscopic-medical-image-classification-framework-via-deep-learning-and-shearlet-transform
#11
Hadi Rezaeilouyeh, Ali Mollahosseini, Mohammad H Mahoor
Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computer-aided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape...
October 2016: Journal of Medical Imaging
https://www.readbyqxmd.com/read/27867227/a-pattern-based-definition-of-urban-context-using-remote-sensing-and-gis
#12
Magdalena Benza, John R Weeks, Douglas A Stow, David López-Carr, Keith C Clarke
In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context...
September 15, 2016: Remote Sensing of Environment
https://www.readbyqxmd.com/read/27853751/quantitative-mri-radiomics-in-the-prediction-of-molecular-classifications-of-breast-cancer-subtypes-in-the-tcga-tcia-data-set
#13
Hui Li, Yitan Zhu, Elizabeth S Burnside, Erich Huang, Karen Drukker, Katherine A Hoadley, Cheng Fan, Suzanne D Conzen, Margarita Zuley, Jose M Net, Elizabeth Sutton, Gary J Whitman, Elizabeth Morris, Charles M Perou, Yuan Ji, Maryellen L Giger
Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute's multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like)...
2016: NPJ Breast Cancer
https://www.readbyqxmd.com/read/27852213/bioimage-classification-with-subcategory-discriminant-transform-of-high-dimensional-visual-descriptors
#14
Yang Song, Weidong Cai, Heng Huang, Dagan Feng, Yue Wang, Mei Chen
BACKGROUND: Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference...
November 16, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/27849525/adaptive-estimation-of-active-contour-parameters-using-convolutional-neural-networks-and-texture-analysis
#15
Assaf Hoogi, Arjun Subramaniam, Rishi Veerapaneni, Daniel Rubin
In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process...
November 11, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/27846217/feature-learning-based-random-walk-for-liver-segmentation
#16
Yongchang Zheng, Danni Ai, Pan Zhang, Yefei Gao, Likun Xia, Shunda Du, Xinting Sang, Jian Yang
Liver segmentation is a significant processing technique for computer-assisted diagnosis. This method has attracted considerable attention and achieved effective result. However, liver segmentation using computed tomography (CT) images remains a challenging task because of the low contrast between the liver and adjacent organs. This paper proposes a feature-learning-based random walk method for liver segmentation using CT images. Four texture features were extracted and then classified to determine the classification probability corresponding to the test images...
2016: PloS One
https://www.readbyqxmd.com/read/27845677/multimodal-feature-based-surface-material-classification
#17
Matti Strese, Clemens Schuwerk, Albert Iepure, Eckehard Steinbach
When a tool is tapped on or dragged over an object surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the surfaces. We present an approach for tool-mediated surface classification that combines these signals and demonstrate that the proposed method is robust against variable scan-time parameters...
November 7, 2016: IEEE Transactions on Haptics
https://www.readbyqxmd.com/read/27831881/a-bottom-up-approach-for-pancreas-segmentation-using-cascaded-superpixels-and-deep-image-patch-labeling
#18
Amal Farag, Le Lu, Holger R Roth, Jiamin Liu, Evrim Turkbey, Ronald M Summers
Robust organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis, pathology detection and surgical assistance. For organs with high anatomical variability (e.g., the pancreas), previous segmentation approaches report low accuracies, compared to well studied organs, such as the liver or heart.We present an automated bottomup approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method generates a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels...
November 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27818583/potential-of-hybrid-adaptive-filtering-in-inflammatory-lesion-detection-from-capsule-endoscopy-images
#19
REVIEW
Vasileios S Charisis, Leontios J Hadjileontiadis
A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn's disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLac) analysis was applied for feature extraction from the HAF-filtered images...
October 21, 2016: World Journal of Gastroenterology: WJG
https://www.readbyqxmd.com/read/27818565/multi-scale-learning-based-segmentation-of-glands-in-digital-colonrectal-pathology-images
#20
Yi Gao, William Liu, Shipra Arjun, Liangjia Zhu, Vadim Ratner, Tahsin Kurc, Joel Saltz, Allen Tannenbaum
Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation...
February 2016: Proceedings of SPIE
keyword
keyword
90848
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"