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Texture classification

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https://www.readbyqxmd.com/read/28543071/differentiating-enhancing-multiple-sclerosis-lesions-glioblastoma-and-lymphoma-with-dynamic-texture-parameters-analysis-dtpa-a-feasibility-study
#1
Rajeev K Verma, Roland Wiest, C Locher, Mirjam R Heldner, Phillip Schucht, Andreas Raabe, Jan Gralla, Christian Philipp Kamm, Johannes Slotboom, Frauke Kellner-Weldon
PURPOSE: MR-imaging hallmarks of glioblastoma (GB), cerebral lymphoma (CL), and demyelinating lesions are gadolinium (Gd) uptake due to blood brain barrier disruption. Thus, initial diagnosis may be difficult based on conventional Gd enhanced MRI alone. Here, the added value of a dynamic texture parameter analysis (DTPA) in the differentiation between these three entities is examined. DTPA is an in-house software tool that incorporates the analysis of quantitative texture parameters extracted from dynamic susceptibility contrast enhanced (DSCE) images...
May 19, 2017: Medical Physics
https://www.readbyqxmd.com/read/28541903/alzheimer-s-disease-classification-based-on-individual-hierarchical-networks-constructed-with-3d-texture-features
#2
Jin Liu, Jianxin Wang, Bin Hu, Fang-Xiang Wu, Yi Pan
Brain network plays an important role in representing abnormalities in Alzheimers disease (AD) and mild cognitive impairment (MCI), which includes MCIc (MCI converted to AD) and MCInc (MCI not converted to AD). In our previous study, we proposed an AD classification approach based on individual hierarchical networks constructed with 3D texture features of brain images. However, we only used edge features of the networks without node features of the networks. In this study, we propose a framework of the combination of multiple kernels to combine edge features and node features for AD classification...
May 23, 2017: IEEE Transactions on Nanobioscience
https://www.readbyqxmd.com/read/28541229/deeppap-deep-convolutional-networks-for-cervical-cell-classification
#3
Ling Zhang, Le Lu, Isabella Nogues, Ronald Summers, Shaoxiong Liu, Jianhua Yao
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture...
May 19, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28539228/soft-surface-irregularity-of-malignant-perihilar-biliary-strictures-in-cholangiography-as-a-risk-factor-for-early-dysfunction-of-multiple-metal-stents
#4
Yoshihide Kanno, Kei Ito, Shinsuke Koshita, Takahisa Ogawa, Kaori Masu, Hiroaki Kusunose, Toshitaka Sakai, Toji Murabayashi, Sho Hasegawa, Fumisato Kozakai, Yutaka Noda
BACKGROUND: Multiple metal stents (multi-MS) in the perihilar bile duct often develop dysfunction in an unexpectedly short period. AIMS: This study is aimed to identify the risk factors for shorter patency of multi-MS. METHODS: Of 97 patients who underwent multi-MS placement, 68 patients were followed-up for >28 days were retrospectively analyzed. Univariate analyses with the log-rank test was performed on 20 factors, including two newly defined classifications of cholangiography: the R classification, which classifies the rough image (localized type [R1] or spreading type [R2]); and the S classification, which classifies the surface texture (soft irregularity [S1], solid irregularity [S2], or smooth [S3])...
May 1, 2017: Digestive and Liver Disease
https://www.readbyqxmd.com/read/28534775/learning-rotation-invariant-local-binary-descriptor
#5
Yueqi Duan, Jiwen Lu, Jianjiang Feng, Jie Zhou
In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors such as LBP and its variants which require strong prior knowledge, local binary feature learning methods are more efficient and dataadaptive. Unlike existing learning-based local binary descriptors such as compact binary face descriptor (CBFD) and simultaneous local binary feature learning and encoding (SLBFLE) which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain rotation-invariant local binary descriptors...
May 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28533671/consequences-of-metabolic-syndrome-on-postoperative-outcomes-after-pancreaticoduodenectomy
#6
Alban Zarzavadjian Le Bian, David Fuks, Sophie Chopinet, Sébastien Gaujoux, Manuela Cesaretti, Renato Costi, Ajay P Belgaumkar, Claude Smadja, Brice Gayet
AIM: To analyze immediate postoperative outcomes after pancreaticoduodenectomy regarding metabolic syndrome. METHODS: In two academic centers, postoperative outcomes of patients undergoing pancreaticoduodenectomy from 2002 to 2014 were prospectively recorded. Patients presenting with metabolic syndrome [defined as at least three criteria among overweight (BMI ≥ 28 kg/m²), diabetes mellitus, arterial hypertension and dyslipidemia] were compared to patients without metabolic syndrome...
May 7, 2017: World Journal of Gastroenterology: WJG
https://www.readbyqxmd.com/read/28526968/computer-aided-diagnosis-of-lung-nodules-in-computed-tomography-by-using-phylogenetic-diversity-genetic-algorithm-and-svm
#7
Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass
Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes...
May 19, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28521821/intratumoral-and-peritumoral-radiomics-for-the-pretreatment-prediction-of-pathological-complete-response-to-neoadjuvant-chemotherapy-based-on-breast-dce-mri
#8
Nathaniel M Braman, Maryam Etesami, Prateek Prasanna, Christina Dubchuk, Hannah Gilmore, Pallavi Tiwari, Donna Pletcha, Anant Madabhushi
BACKGROUND: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). METHODS: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases...
May 18, 2017: Breast Cancer Research: BCR
https://www.readbyqxmd.com/read/28513853/association-between-textural-and-morphological-tumor-indices-on-baseline-pet-ct-and-early-metabolic-response-on-interim-pet-ct-in-bulky-malignant-lymphomas
#9
Fayçal Ben Bouallègue, Yassine Al Tabaa, Marilyne Kafrouni, Guillaume Cartron, Fabien Vauchot, Denis Mariano-Goulart
PURPOSE: We investigated whether metabolic, textural and morphological tumoral indices evaluated on baseline PET-CT were predictive of early metabolic response on interim PET-CT in a cohort of patients with bulky Hodgkin and non-Hodgkin malignant lymphomas. METHODS: This retrospective study included 57 patients referred for initial PET-CT examination. In-house dedicated software was used to delineate tumor contours using a fixed 30% threshold of SUV max and then to compute tumoral metabolic parameters (SUV max, mean, peak, standard deviation, skewness and kurtosis, metabolic tumoral volume (MTV), total lesion glycolysis, and area under the curve of the cumulative histogram), textural parameters (Moran's and Geary's indices, energy, entropy, contrast, correlation derived from the gray-level co-occurrence matrix, area under the curve of the power spectral density, auto-correlation distance, and granularity), and shape parameters (surface, asphericity, convexity, surfacic extension, and 2D and 3D fractal dimensions)...
May 17, 2017: Medical Physics
https://www.readbyqxmd.com/read/28512610/melanoma-is-skin-deep-a-3d-reconstruction-technique-for-computerized-dermoscopic-skin-lesion-classification
#10
T Y Satheesha, D Satyanarayana, M N Giri Prasad, Kashyap D Dhruve
Melanoma mortality rates are the highest amongst skin cancer patients. Melanoma is life threating when it grows beyond the dermis of the skin. Hence, depth is an important factor to diagnose melanoma. This paper introduces a non-invasive computerized dermoscopy system that considers the estimated depth of skin lesions for diagnosis. A 3-D skin lesion reconstruction technique using the estimated depth obtained from regular dermoscopic images is presented. On basis of the 3-D reconstruction, depth and 3-D shape features are extracted...
2017: IEEE Journal of Translational Engineering in Health and Medicine
https://www.readbyqxmd.com/read/28500002/cross-label-suppression-a-discriminative-and-fast-dictionary-learning-with-group-regularization
#11
Xiudong Wang, Yuantao Gu
This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar...
May 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28480118/predictive-nuclear-chromatin-characteristics-of-melanoma-and-dysplastic-nevi
#12
Matthew G Hanna, Chi Liu, Gustavo K Rohde, Rajendra Singh
BACKGROUND: The diagnosis of malignant melanoma (MM) is among the diagnostic challenges pathologists encounter on a routine basis. Melanoma may arise in patients with preexisting dysplastic nevi (DN) and it is still the cause of 1.7% of all cancer-related deaths. Melanomas often have overlapping histological features with DN, especially those with severe dysplasia. Nucleotyping for identifying nuclear textural features can analyze nuclear DNA structure and organization. The aim of this study is to differentiate MM and DN using these methodologies...
2017: Journal of Pathology Informatics
https://www.readbyqxmd.com/read/28477395/quantitative-comparison-of-clustered-microcalcifications-in-for-presentation-and-for-processing-mammograms-in-full-field-digital-mammography
#13
Juan Wang, Robert M Nishikawa, Yongyi Yang
PURPOSE: Mammograms acquired with full-field digital mammography (FFDM) systems are provided in both "for-processing" and "for-presentation" image formats. For-presentation images are traditionally intended for visual assessment by the radiologists. In this study, we investigate the feasibility of using for-presentation images in computerized analysis and diagnosis of microcalcification (MC) lesions. METHODS: We make use of a set of 188 matched mammogram image pairs of MC lesions from 95 cases (biopsy proven), in which both for-presentation and for-processing images are provided for each lesion...
May 6, 2017: Medical Physics
https://www.readbyqxmd.com/read/28465724/feature-analysis-for-classification-of-trace-fluorescent-labeled-protein-crystallization-images
#14
Madhav Sigdel, Imren Dinc, Madhu S Sigdel, Semih Dinc, Marc L Pusey, Ramazan S Aygun
BACKGROUND: Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated...
2017: BioData Mining
https://www.readbyqxmd.com/read/28459688/a-framework-for-classification-and-segmentation-of-branch-retinal-artery-occlusion-in-sd-oct
#15
Jingyun Guo, Weifang Zhu, Fei Shi, Dehui Xiang, Haoyu Chen, Xinjian Chen
Branch retinal artery occlusion (BRAO) is an ocular emergency which could lead to blindness. Quantitative analysis of BRAO region in the retina is necessary for assessment of the severity of retinal ischemia. In this paper, a fully automatic framework was proposed to segment BRAO regions based on 3D spectral-domain optical coherence tomography (SD-OCT) images. To the best of our knowledge, this is the first automatic 3D BRAO segmentation framework. First, the input 3D image is automatically classified into BRAO of acute phase, BRAO of chronic phase or normal retina using AdaBoost classifier based on combining local structural, intensity, textural features with our new feature distribution analyzing strategy...
April 25, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28450433/differentiation-of-enhancing-glioma-and-primary-central-nervous-system-lymphoma-by-texture-based-machine-learning
#16
P Alcaide-Leon, P Dufort, A F Geraldo, L Alshafai, P J Maralani, J Spears, A Bharatha
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma...
April 27, 2017: AJNR. American Journal of Neuroradiology
https://www.readbyqxmd.com/read/28436405/a-comparison-of-methods-for-three-class-mammograms-classification
#17
Marina Milosevic, Zeljko Jovanovic, Dragan Jankovic
BACKGROUND: Mammography is considered the gold standard for early breast cancer detection but it is very difficult to interpret mammograms for many reason. Computer aided diagnosis (CAD) is an important development that may help to improve the performance in breast cancer detection. OBJECTIVE: We present a CAD system based on feature extraction techniques for detecting abnormal patterns in digital mammograms. METHODS: Computed features based on gray-level co-occurrence matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions...
April 14, 2017: Technology and Health Care: Official Journal of the European Society for Engineering and Medicine
https://www.readbyqxmd.com/read/28432822/focal-liver-lesions-segmentation-and-classification-in-nonenhanced-t2-weighted-mri
#18
Ilias Gatos, Stavros Tsantis, Maria Karamesini, Stavros Spiliopoulos, Dimitris Karnabatidis, John D Hazle, George C Kagadis
PURPOSE: To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. METHODS: 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction...
April 22, 2017: Medical Physics
https://www.readbyqxmd.com/read/28429195/computer-assisted-diagnosis-system-for-breast-cancer-in-computed-tomography-laser-mammography-ctlm
#19
Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M Iqbal Saripan, Abdul Rahman Ramli
Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images...
April 20, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28426134/metastasis-detection-from-whole-slide-images-using-local-features-and-random-forests
#20
Mira Valkonen, Kimmo Kartasalo, Kaisa Liimatainen, Matti Nykter, Leena Latonen, Pekka Ruusuvuori
Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model...
April 20, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
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