Read by QxMD icon Read

Texture classification

Irene Fondón, Auxiliadora Sarmiento, Ana Isabel García, María Silvestre, Catarina Eloy, António Polónia, Paulo Aguiar
Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images...
March 8, 2018: Computers in Biology and Medicine
Daniel Hamill, Daniel Buscombe, Joseph M Wheaton
Side scan sonar in low-cost 'fishfinder' systems has become popular in aquatic ecology and sedimentology for imaging submerged riverbed sediment at coverages and resolutions sufficient to relate bed texture to grain-size. Traditional methods to map bed texture (i.e. physical samples) are relatively high-cost and low spatial coverage compared to sonar, which can continuously image several kilometers of channel in a few hours. Towards a goal of automating the classification of bed habitat features, we investigate relationships between substrates and statistical descriptors of bed textures in side scan sonar echograms of alluvial deposits...
2018: PloS One
Wenjuan Ma, Yumei Zhao, Yu Ji, Xinpeng Guo, Xiqi Jian, Peifang Liu, Shandong Wu
RATIONALE AND OBJECTIVES: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions...
March 8, 2018: Academic Radiology
Bente Nyvad, Vibeke Baelum
The Nyvad classification is a visual-tactile caries classification system devised to enable the detection of the activity and severity of caries lesions with special focus on low-caries populations. The criteria behind the classification reflect the entire continuum of caries, ranging from clinically sound surfaces through noncavitated and microcavitated caries lesions in enamel, to frank cavitation into the dentin. Lesion activity at each severity stage is discriminated by differences in surface topography and lesion texture...
March 5, 2018: Caries Research
Chih-Yang Hsu, Mike Doubrovin, Chia-Ho Hua, Omar Mohammed, Barry L Shulkin, Sue Kaste, Sara Federico, Monica Metzger, Matthew Krasin, Christopher Tinkle, Thomas E Merchant, John T Lucas
Identification of FDGavid- neoplasms may be obscured by high-uptake normal tissues, thus limiting inferences about the natural history of disease. We introduce a FDG-PET radiomics tissue classifier for differentiating FDGavid- normal tissues from tumor. Thirty-three scans from 15 patients with Hodgkin lymphoma and 68 scans from 23 patients with Ewing sarcoma treated on two prospective clinical trials were retrospectively analyzed. Disease volumes were manually segmented on FDG-PET and CT scans. Brain, heart, kidneys and bladder and tumor volumes were automatically segmented on PET images...
March 2, 2018: Scientific Reports
Yi Zhao, Jiale Ma, Xiaohui Li, Jie Zhang
An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients...
February 27, 2018: Sensors
Syed Muhammad Naqi, Muhammad Sharif, Mussarat Yasmin
PURPOSE: Lung cancer detection at its initial stages increases the survival chances of patients. Automatic detection of lung nodules facilitates radiologists during the diagnosis. However, there is a challenge of false positives in automated systems which may lead to wrong findings. Precise segmentation facilitates to accurately extract nodules from lung CT images in order to improve performance of the diagnostic method. METHODS: A multistage segmentation model is presented in this study...
February 28, 2018: International Journal of Computer Assisted Radiology and Surgery
Marcel Lenz, Robin Krug, Christopher Dillmann, Ralf Stroop, Nils C Gerhardt, Hubert Welp, Kirsten Schmieder, Martin R Hofmann
Brain tissue analysis is highly desired in neurosurgery, such as tumor resection. To guarantee best life quality afterward, exact navigation within the brain during the surgery is essential. So far, no method has been established that perfectly fulfills this need. Optical coherence tomography (OCT) is a promising three-dimensional imaging tool to support neurosurgical resections. We perform a preliminary study toward in vivo brain tumor removal assistance by investigating meningioma, healthy white, and healthy gray matter...
February 2018: Journal of Biomedical Optics
Piyush Samant, Ravinder Agarwal
BACKGROUND AND OBJECTIVE: Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods...
April 2018: Computer Methods and Programs in Biomedicine
Hansang Lee, Helen Hong, Junmo Kim, Dae Chul Jung
PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. METHODS: A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers...
February 23, 2018: Medical Physics
Cassia F Read, David H Duncan, Chiu Yee Catherine Ho, Matt White, Peter A Vesk
Plant ecologists require spatial information on functional soil properties but are often faced with soil classifications that are not directly interpretable or useful for statistical models. Sand and clay content are important soil properties because they indicate soil water-holding capacity and nutrient content, yet these data are not available for much of the landscape. Remotely sensed soil radiometric data offer promise for developing statistical models of functional soil properties applicable over large areas...
February 2018: Ecology and Evolution
Muhammad Nasir, Muhammad Attique Khan, Muhammad Sharif, Ikram Ullah Lali, Tanzila Saba, Tassawar Iqbal
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...
February 21, 2018: Microscopy Research and Technique
W Sun, W-Y Yu, D-J Yu, T-L Zhao, L-J Wu, W-Y Han
OBJECTIVE: To explore the effects of recombinant human growth hormone (rHGH) on the survival of the mouse slender narrow pedicle flap and the expressions of vascular endothelial growth factor (VEGF) and classification determinant 34 (CD34). MATERIALS AND METHODS: A total of 20 BALB/c mice were randomly divided into the experimental group (n=10) and control group (n=10). The flaps were transplanted for mice in two groups respectively. 6 h after the operation, the mice in the experimental group were administrated with rHGH via local subcutaneous injection, while the mice in the control group were injected with the same amount of normal saline...
February 2018: European Review for Medical and Pharmacological Sciences
Wookjin Choi, Jung Hun Oh, Sadegh Riyahi, Chia-Ju Liu, Feng Jiang, Wengen Chen, Charles White, Andreas Rimner, James G Mechalakos, Joseph O Deasy, Wei Lu
PURPOSE: To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer. METHODS: We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). 103 CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method...
February 19, 2018: Medical Physics
Min Liu, Xueping Wang, Hongzhong Zhang
BACKGROUND AND OBJECTIVE: In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. METHODS: We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks...
March 2018: Computer Methods and Programs in Biomedicine
Hongming Xu, Cheng Lu, Richard Berendt, Naresh Jha, Mrinal Mandal
This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed...
February 6, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Manoj Mannil, Jochen von Spiczak, Robert Manka, Hatem Alkadhi
OBJECTIVES: The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls...
February 8, 2018: Investigative Radiology
Hongzhe Jiang, Seung-Chul Yoon, Hong Zhuang, Wei Wang, Kurt C Lawrence, Yi Yang
The aim of this study was to classify and visualize tenderness of intact fresh broiler breast fillets using hyperspectral imaging (HSI) technique. A total of 75 chicken fillets were scanned by HSI system of 400-1000nm in reflectance mode. Warner-Bratzler shear force (WBSF) value was used as reference tenderness indicator and fillets were grouped into least, moderately and very tender categories accordingly. To extract additional image textural features, principal component analysis (PCA) transform of images were conducted and gray level co-occurrence matrix (GLCM) analysis was implemented in region of interests (ROIs) on first three PC score images...
January 30, 2018: Meat Science
Mohammed Gagaoua, Muriel Bonnet, Marie-Pierre Ellies-Oury, Leanne De Koning, Brigitte Picard
The validation of biomarkers and tools for the prediction of beef texture remains a challenging task. In this study, reverse phase protein arrays (RPPA) quantified 29 protein biomarkers in the m. Longissimus thoracis of Charolais cattle sampled early post-mortem. Myosin heavy chain 1 (MHC1, slow-oxidative fibers) and Retinal dehydrogenase 1 (ALDH1A1, oxidative enzyme) discriminated between tender and juicy vs. tough meat with residues classes and are validated as prime biomarkers of beef texture. Several proteins belonging to energy metabolism, heat shock and oxidative stress, cytoskeletal, cell signaling and apoptosis were related with tenderness...
June 1, 2018: Food Chemistry
Muthu Subash Kavitha, Takio Kurita, Byeong-Cheol Ahn
The objectives of this study are to assess various automated texture features obtained from the segmented colony regions of induced pluripotent stem cells (iPSCs) and confirm their potential for characterizing the colonies using different machine learning techniques. One hundred and fifty-one features quantified using shape-based, moment-based, statistical and spectral texture feature groups are extracted from phase-contrast microscopic colony images of iPSCs. The forward stepwise regression model is implemented to select the most appropriate features required for categorizing the colonies...
January 25, 2018: Computers in Biology and Medicine
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"