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Texture classification

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https://www.readbyqxmd.com/read/28429195/computer-assisted-diagnosis-system-for-breast-cancer-in-computed-tomography-laser-mammography-ctlm
#1
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
#2
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
https://www.readbyqxmd.com/read/28426133/quantitative-phase-microscopy-spatial-signatures-of-cancer-cells
#3
Darina Roitshtain, Lauren Wolbromsky, Evgeny Bal, Hayit Greenspan, Lisa L Satterwhite, Natan T Shaked
We present cytometric classification of live healthy and cancerous cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cells and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow...
April 20, 2017: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/28420197/a-novel-auto-sorting-system-for-chinese-cabbage-seeds
#4
Kuo-Yi Huang, Jian-Feng Cheng
This paper presents a novel machine vision-based auto-sorting system for Chinese cabbage seeds. The system comprises an inlet-outlet mechanism, machine vision hardware and software, and control system for sorting seed quality. The proposed method can estimate the shape, color, and textural features of seeds that are provided as input neurons of neural networks in order to classify seeds as "good" and "not good" (NG). The results show the accuracies of classification to be 91.53% and 88.95% for good and NG seeds, respectively...
April 18, 2017: Sensors
https://www.readbyqxmd.com/read/28414731/a-hyper-temporal-remote-sensing-protocol-for-high-resolution-mapping-of-ecological-sites
#5
Jonathan J Maynard, Jason W Karl
Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties...
2017: PloS One
https://www.readbyqxmd.com/read/28414380/iterative-reconstruction-of-scene-depth-with-fidelity-based-on-light-field-data
#6
Chang Liu, Jun Qiu, Songnian Zhao
Depth reconstruction from the light field, as a depth extracting approach, is a vibrant research field in computational imaging. In this paper, depth reconstruction from the light field was approached as an optimization problem by analyzing the forward and inverse models. The forward and inverse models present the connection between the light field and the depth of the scene. We proposed an iterative method for scene depth reconstruction with fidelity from 4D light field data. The objective function of the optimization problem entails three terms, of which the matching term is used as the fidelity term, while the gradient term and classification term constitute the penalty terms...
April 10, 2017: Applied Optics
https://www.readbyqxmd.com/read/28413203/nuclear-morphometry-and-texture-analysis-on-cytological-smears-of-thyroid-neoplasms-a-study-of-50-cases
#7
L Deka, S Gupta, R Gupta, K Gupta, C J Kaur, S Singh S
BACKGROUND: Fine needle aspiration cytology (FNAC) is a reliable and reproducible diagnostic technique for thyroid lesions with certain limitations. Computed morphometric methods have been introduced with a view to improve the diagnostic yield of thyroid aspirates. However, a review of the existing literature revealed conflicting reports regarding morphometric parameters in thyroid neoplasms. MATERIALS AND METHODS: This study included 50 cases of thyroid lesions (20 cases of colloid goitre, 15 of follicular adenoma, 5 of follicular carcinoma and 10 papillary carcinomas)...
April 2017: Malaysian Journal of Pathology
https://www.readbyqxmd.com/read/28409834/fully-automatic-and-accurate-detection-of-lung-nodules-in-ct-images-using-a-hybrid-feature-set
#8
Furqan Shaukat, Gulistan Raja, Ali Gooya, Alejandro F Frangi
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantly. METHOD: The proposed method starts with pre-processing, removing any present noise from input images, followed by lung segmentation using optimal thresholding. Then the image is enhanced using multi scale dot enhancement filtering prior to nodule detection and feature extraction...
April 13, 2017: Medical Physics
https://www.readbyqxmd.com/read/28391186/dual-channel-active-contour-model-for-megakaryocytic-cell-segmentation-in-bone-marrow-trephine-histology-images
#9
Tzu-Hsi Song, Victor Sanchez, Hesham ElDaly, Nasir Rajpoot
Assessment of morphological features of megakaryocytes (special kind of cells) in bone marrow trephine biopsies play an important role in the classification of different subtypes of Philadelphia-chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs). In order to aid hematopathologists in the study of megakaryocytes, we propose a novel framework that can efficiently delineate the nuclei and cytoplasm of these cells in digitized images of bone marrow trephine biopsies. The framework first employs a supervised machine learning approach that utilizes color and texture features to delineate megakaryocytic nuclei...
April 4, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28386175/quantitative-evaluation-methods-of-skin-condition-based-on-texture-feature-parameters
#10
Hui Pang, Tianhua Chen, Xiaoyi Wang, Zhineng Chang, Siqi Shao, Jing Zhao
In order to quantitatively evaluate the improvement of the skin condition after using skin care products and beauty, a quantitative evaluation method for skin surface state and texture is presented, which is convenient, fast and non-destructive. Human skin images were collected by image sensors. Firstly, the median filter of the 3 × 3 window is used and then the location of the hairy pixels on the skin is accurately detected according to the gray mean value and color information. The bilinear interpolation is used to modify the gray value of the hairy pixels in order to eliminate the negative effect of noise and tiny hairs on the texture...
March 2017: Saudi Journal of Biological Sciences
https://www.readbyqxmd.com/read/28385875/correlates-of-perceptual-orientation-biases-in-human-primary-visual-cortex
#11
Matthew L Patten, Damien J Mannion, Colin W G Clifford
Vision can be considered as a process of probabilistic inference. In a Bayesian framework, perceptual estimates from sensory information are combined with prior knowledge, with a stronger influence of the prior when the sensory evidence is less certain. Here, we explored the behavioral and neural consequences of manipulating stimulus certainty in the context of orientation processing. First, we asked participants to judge whether a stimulus was oriented closer to vertical or the clockwise primary oblique (45°) for two stimulus types (spatially-filtered noise textures, sinusoidal gratings) and three manipulations of certainty (orientation bandwidth, contrast, duration)...
April 6, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28382315/supervised-classification-of-etoposide-treated-in-vitro-adherent-cells-based-on-noninvasive-imaging-morphology
#12
Anna Leida Mölder, Johan Persson, Zahra El-Schich, Silvester Czanner, Anette Gjörloff-Wingren
Single-cell studies using noninvasive imaging is a challenging, yet appealing way to study cellular characteristics over extended periods of time, for instance to follow cell interactions and the behavior of different cell types within the same sample. In some cases, e.g., transplantation culturing, real-time cellular monitoring, stem cell studies, in vivo studies, and embryo growth studies, it is also crucial to keep the sample intact and invasive imaging using fluorophores or dyes is not an option. Computerized methods are needed to improve throughput of image-based analysis and for use with noninvasive microscopy such methods are poorly developed...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28376281/differentiation-of-fat-poor-angiomyolipoma-from-clear-cell-renal-cell-carcinoma-in-contrast-enhanced-mdct-images-using-quantitative-feature-classification
#13
Han Sang Lee, Helen Hong, Dae Chul Jung, Seunghyun Park, Junmo Kim
PURPOSE: To develop a computer-aided classification system to differentiate benign fat-poor angiomyolipoma (fp-AML) from malignant clear-cell renal cell carcinoma (ccRCC) using quantitative feature classification on histogram and texture patterns from contrast-enhanced multi-detector computer tomography (CE MDCT) images. METHODS: A dataset including 50 CE MDCT images of 25 fp-AML and 25 ccRCC patients was used. From these images, the tumors were manually segmented by an expert radiologist to define the regions of interest (ROI)...
April 4, 2017: Medical Physics
https://www.readbyqxmd.com/read/28372789/automatic-media-adventitia-ivus-image-segmentation-based-on-sparse-representation-framework-and-dynamic-directional-active-contour-model
#14
Fahimeh Sadat Zakeri, Seyed Kamaledin Setarehdan, Somayye Norouzi
Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market...
March 25, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28370776/texture-analysis-of-parasitological-liver-fibrosis-images
#15
Luminiţa Moraru, Simona Moldovanu, Anisia-Luiza Culea-Florescu, Dorin Bibicu, Amira S Ashour, Nilanjan Dey
Liver fibrosis accurate staging is vital to define the state of the Schistosomiasis disease for further treatment. The present work analyzed the microscopic liver images to identify and to differentiate between healthy, cellular, fibrocellular, and fibrous liver pathologies by proposing a fast, robust, and highly discriminative method based on texture analysis. The multiclass classification based on the "one-versus- all" method that built a voting rule approach to classify the liver images based on the liver state...
March 29, 2017: Microscopy Research and Technique
https://www.readbyqxmd.com/read/28368841/kernel-embedding-multiorientation-local-pattern-for-image-representation
#16
Yu-Feng Yu, Chuan-Xian Ren, Dao-Qing Dai, Ke-Kun Huang
Local feature descriptor plays a key role in different image classification applications. Some of these methods such as local binary pattern and image gradient orientations have been proven effective to some extent. However, such traditional descriptors which only utilize single-type features, are deficient to capture the edges and orientations information and intrinsic structure information of images. In this paper, we propose a kernel embedding multiorientation local pattern (MOLP) to address this problem...
March 28, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28368819/selective-convolutional-descriptor-aggregation-for-fine-grained-image-retrieval
#17
Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu, Zhi-Hua Zhou
Deep convolutional neural network models pretrained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, let alone the unsupervised retrieval task. We propose the Selective Convolutional Descriptor Aggregation (SCDA) method...
March 27, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28342697/recent-publications-from-the-alzheimer-s-disease-neuroimaging-initiative-reviewing-progress-toward-improved-ad-clinical-trials
#18
REVIEW
Michael W Weiner, Dallas P Veitch, Paul S Aisen, Laurel A Beckett, Nigel J Cairns, Robert C Green, Danielle Harvey, Clifford R Jack, William Jagust, John C Morris, Ronald C Petersen, Andrew J Saykin, Leslie M Shaw, Arthur W Toga, John Q Trojanowski
INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the 450+ publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses...
March 22, 2017: Alzheimer's & Dementia: the Journal of the Alzheimer's Association
https://www.readbyqxmd.com/read/28333649/multi-scale-rotation-invariant-convolutional-neural-networks-for-lung-texture-classification
#19
Qiangchang Wang, Yuanjie Zheng, Gongping Yang, Weidong Jin, Xinjian Chen, Yilong Yin
We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches...
March 21, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28331793/texture-analysis-of-abnormal-cell-images-for-predicting-the-continuum-of-colorectal-cancer
#20
Ahmad Chaddad, Camel Tanougast
Abnormal cell (ABC) is a markedly heterogeneous tissue area and can be categorized into three main types: benign hyperplasia (BH), carcinoma (Ca), and intraepithelial neoplasia (IN) or precursor cancerous lesion. In this study, the goal is to determine and characterize the continuum of colorectal cancer by using a 3D-texture approach. ABC was segmented in preprocessing step using an active contour segmentation technique. Cell types were analyzed based on textural features extracted from the gray level cooccurrence matrices (GLCMs)...
2017: Analytical Cellular Pathology (Amsterdam)
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