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Texture classification

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https://www.readbyqxmd.com/read/29144395/real-time-indoor-scene-description-for-the-visually-impaired-using-autoencoder-fusion-strategies-with-visible-cameras
#1
Salim Malek, Farid Melgani, Mohamed Lamine Mekhalfi, Yakoub Bazi
This paper describes three coarse image description strategies, which are meant to promote a rough perception of surrounding objects for visually impaired individuals, with application to indoor spaces. The described algorithms operate on images (grabbed by the user, by means of a chest-mounted camera), and provide in output a list of objects that likely exist in his context across the indoor scene. In this regard, first, different colour, texture, and shape-based feature extractors are generated, followed by a feature learning step by means of AutoEncoder (AE) models...
November 16, 2017: Sensors
https://www.readbyqxmd.com/read/29142740/combined-empirical-mode-decomposition-and-texture-features-for-skin-lesion-classification-using-quadratic-support-vector-machine
#2
Maram A Wahba, Amira S Ashour, Sameh A Napoleon, Mustafa M Abd Elnaby, Yanhui Guo
Purpose: Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. Methods: In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal...
December 2017: Health Information Science and Systems
https://www.readbyqxmd.com/read/29140606/mr-textural-analysis-on-t2-flair-images-for-the-prediction-of-true-oligodendroglioma-by-the-2016-who-genetic-classification
#3
Wenting Rui, Yan Ren, Yin Wang, Xinyi Gao, Xiao Xu, Zhenwei Yao
BACKGROUND: The genetic status of 1p/19q is important for differentiating oligodendroglioma, isocitrate-dehydrogenase (IDH)-mutant, and 1p/19q-codeleted from diffuse astrocytoma, IDH-mutant according to the 2016 World Health Organization (WHO) criteria. PURPOSE: To assess the value of magnetic resonance textural analysis (MRTA) on T2 fluid-attenuated inversion recovery (FLAIR) images for making a genetically integrated diagnosis of true oligodendroglioma by WHO guidelines...
November 15, 2017: Journal of Magnetic Resonance Imaging: JMRI
https://www.readbyqxmd.com/read/29140113/prediction-of-pancreatic-neuroendocrine-tumor-grade-based-on-ct-features-and-texture-analysis
#4
Rodrigo Canellas, Kristine S Burk, Anushri Parakh, Dushyant V Sahani
OBJECTIVE: The purposes of this study were to assess whether CT texture analysis and CT features are predictive of pancreatic neuroendocrine tumor (PNET) grade based on the World Health Organization (WHO) classification and to identify features related to disease progression after surgery. MATERIALS AND METHODS: Preoperative contrast-enhanced CT images of 101 patients with PNETs were assessed. The images were evaluated for tumor location, tumor size, tumor pattern, predominantly solid or cystic composition, presence of calcification, presence of heterogeneous enhancement on contrast-enhanced images, presence of pancreatic duct dilatation, presence of pancreatic atrophy, presence of vascular involvement by the tumor, and presence of lymphadenopathy...
November 15, 2017: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/29126070/deep-neural-networks-for-texture-classification-a-theoretical-analysis
#5
Saikat Basu, Supratik Mukhopadhyay, Manohar Karki, Robert DiBiano, Sangram Ganguly, Ramakrishna Nemani, Shreekant Gayaka
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate...
October 23, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29124453/a-machine-learning-ensemble-classifier-for-early-prediction-of-diabetic-retinopathy
#6
Somasundaram S K, Alli P
The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed...
November 9, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29112948/using-quantitative-features-extracted-from-t2-weighted-mri-to-improve-breast-mri-computer-aided-diagnosis-cad
#7
Cristina Gallego-Ortiz, Anne L Martel
Computer-aided diagnosis (CAD) has been proposed for breast MRI as a tool to standardize evaluation, to automate time-consuming analysis, and to aid the diagnostic decision process by radiologists. T2w MRI findings are diagnostically complementary to T1w DCE-MRI findings in the breast and prior research showed that measuring the T2w intensity of a lesion relative to a tissue of reference improves diagnostic accuracy. The diagnostic value of this information in CAD has not been yet quantified. This study proposes an automatic method of assessing relative T2w lesion intensity without the need to select a reference region...
2017: PloS One
https://www.readbyqxmd.com/read/29077737/a-medical-imaging-analysis-system-for-trigger-finger-using-an-adaptive-texture-based-active-shape-model-atasm-in-ultrasound-images
#8
Bo-I Chuang, Li-Chieh Kuo, Tai-Hua Yang, Fong-Chin Su, I-Ming Jou, Wei-Jr Lin, Yung-Nien Sun
Trigger finger has become a prevalent disease that greatly affects occupational activity and daily life. Ultrasound imaging is commonly used for the clinical diagnosis of trigger finger severity. Due to image property variations, traditional methods cannot effectively segment the finger joint's tendon structure. In this study, an adaptive texture-based active shape model method is used for segmenting the tendon and synovial sheath. Adapted weights are applied in the segmentation process to adjust the contribution of energy terms depending on image characteristics at different positions...
2017: PloS One
https://www.readbyqxmd.com/read/29077040/fast-detection-of-striped-stem-borer-chilo-suppressalis-walker-infested-rice-seedling-based-on-visible-near-infrared-hyperspectral-imaging-system
#9
Yangyang Fan, Tao Wang, Zhengjun Qiu, Jiyu Peng, Chu Zhang, Yong He
Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days...
October 27, 2017: Sensors
https://www.readbyqxmd.com/read/29075939/web-enabled-distributed-health-care-framework-for-automated-malaria-parasite-classification-an-e-health-approach
#10
Maitreya Maity, Dhiraj Dhane, Tushar Mungle, A K Maiti, Chandan Chakraborty
Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance...
October 26, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29073725/radiomics-in-paediatric-neuro-oncology-a-multicentre-study-on-mri-texture-analysis
#11
Ahmed E Fetit, Jan Novak, Daniel Rodriguez, Dorothee P Auer, Christopher A Clark, Richard G Grundy, Andrew C Peet, Theodoros N Arvanitis
Brain tumours are the most common solid cancers in children in the UK and are the most common cause of cancer deaths in this age group. Despite current advances in MRI, non-invasive diagnosis of paediatric brain tumours has yet to find its way into routine clinical practice. Radiomics, the high-throughput extraction and analysis of quantitative image features (e.g. texture), offers potential solutions for tumour characterization and decision support. In the search for diagnostic oncological markers, the primary aim of this work was to study the application of MRI texture analysis (TA) for the classification of paediatric brain tumours...
October 26, 2017: NMR in Biomedicine
https://www.readbyqxmd.com/read/29065605/feature-extraction-and-classification-on-esophageal-x-ray-images-of-xinjiang-kazak-nationality
#12
Fang Yang, Murat Hamit, Chuan B Yan, Juan Yao, Abdugheni Kutluk, Xi M Kong, Sui X Zhang
Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29060843/automatic-diagnosis-of-melanoma-using-linear-and-nonlinear-features-from-digital-image
#13
Tamanna Tabassum Khan Munia, Md Nafiul Alam, Jeremiah Neubert, Reza Fazel-Rezai
Melanoma is the most serious type of skin cancer and causes more deaths than other forms of skin cancer. It is a tiny small malignant mole that is usually black or brown but also appears in other color patterns. Early detection of melanoma is key as this is the time period when it is most likely to be cured. Due to the advancement of smartphone technology, automatic and efficient detection of melanoma mole using a smartphone is an active area of research. In this study, we developed an automatic melanoma diagnosis system using images captured from the digital camera...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060752/content-based-retrieval-for-lung-nodule-diagnosis-using-learned-distance-metric
#14
Guohui Wei, He Ma, Wei Qian, Hongyang Jiang, Xinzhuo Zhao
Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060568/automated-angiodysplasia-detection-from-wireless-capsule-endoscopy
#15
F Noya, M A Alvarez-Gonzalez, R Benitez
We present a novel system for the automatic detection of angiodysplasia lesions from capsule endoscopy images. The approach identifies potential regions of interest and classifies them using a combination of color-based, texture, statistical and morphological features. A boosted decision tree classification method is used in order to overcome the problem of unbalanced sampling between pathological and non-pathological regions. The lesion detection method has been designed and validated using a lesion database labelled by an expert...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060283/deep-scattering-convolution-network-based-features-for-ultrasonic-fatty-liver-tissue-characterization
#16
R Bharath, P Rajalakshmi
Accumulation of excess fat in the liver tissue is the leading cause for dysfunction of liver, which can lead to the diseases from fibrosis to end stage cirrhosis. Hence, early detection of fatty liver becomes crucial in avoiding the liver from permanent failure. Depending on the concentration of fat in the tissue, the liver is classified as Normal, Grade 1, Grade 2 and Grade 3 respectively. The texture of liver tissue in ultrasound image is so specific to the concentration of fat, hence classifying the fatty liver is formulated as a texture discrimination problem...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059949/application-of-mri-texture-analysis-in-the-study-of-the-posterior-fossa-tumors-growing-trend-in-children
#17
Mengmeng Li, Zhigang Shang, Yonghui Dong, Yong Zhang, Ya Li
In order to analyze the growing trend of the posterior fossa tumor in children and provide assistant basis for the treatment or surgery of tumors, a variety of texture analysis methods were comprehensive used to analyze and identify three kinds of brain tissues, tumor region, tumor diffusion region and normal brain tissue region. The MRIs of tumor patients were collected to extract texture features. Then feature selection method CFS and feature compression method partial least squares regression (PLSR) were used to process these feature space...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059917/a-radiomics-evaluation-of-2d-and-3d-mri-texture-features-to-classify-brain-metastases-from-lung-cancer-and-melanoma
#18
Rafael Ortiz-Ramon, Andres Larroza, Estanislao Arana, David Moratal
Brain metastases are occasionally detected before diagnosing their primary site of origin. In these cases, simple visual examination of medical images of the metastases is not enough to identify the primary cancer, so an extensive evaluation is needed. To avoid this procedure, a radiomics approach on magnetic resonance (MR) images of the metastatic lesions is proposed to classify two of the most frequent origins (lung cancer and melanoma). In this study, 50 T1-weighted MR images of brain metastases from 30 patients were analyzed: 27 of lung cancer and 23 of melanoma origin...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29057290/feature-analysis-of-cell-nuclear-chromatin-distribution-in-support-of-cervical-cytology
#19
Hideki Komagata, Takaya Ichimura, Yasuka Matsuta, Masahiro Ishikawa, Kazuma Shinoda, Naoki Kobayashi, Atsushi Sasaki
Cytology, a method of estimating cancer or cellular atypia from microscopic images of scraped specimens, is used according to the pathologist's experience to diagnose cases based on the degree of structural changes and atypia. Several methods of cell feature quantification, including nuclear size, nuclear shape, cytoplasm size, and chromatin texture, have been studied. We focus on chromatin distribution in the cell nucleus and propose new feature values that indicate the chromatin complexity, spreading, and bias, including convex hull ratio on multiple binary images, intensity distribution from the gravity center, and tangential component intensity and texture biases...
October 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29051570/prediction-of-recurrence-in-early-stage-non-small-cell-lung-cancer-using-computer-extracted-nuclear-features-from-digital-h-e-images
#20
Xiangxue Wang, Andrew Janowczyk, Yu Zhou, Rajat Thawani, Pingfu Fu, Kurt Schalper, Vamsidhar Velcheti, Anant Madabhushi
Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, nā€‰=ā€‰70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence...
October 19, 2017: Scientific Reports
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