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https://www.readbyqxmd.com/read/29790102/classification-of-malignant-and-benign-lung-nodules-using-taxonomic-diversity-index-and-phylogenetic-distance
#1
Robherson Wector de Sousa Costa, Giovanni Lucca França da Silva, Antonio Oseas de Carvalho Filho, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass
Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model...
May 23, 2018: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29769456/machine-learning-based-texture-analysis-of-contrast-enhanced-mr-imaging-to-differentiate-between-glioblastoma-and-primary-central-nervous-system-lymphoma
#2
Akira Kunimatsu, Natsuko Kunimatsu, Koichiro Yasaka, Hiroyuki Akai, Kouhei Kamiya, Takeyuki Watadani, Harushi Mori, Osamu Abe
PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T1 -weighted images. METHODS: This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods...
May 16, 2018: Magnetic Resonance in Medical Sciences: MRMS
https://www.readbyqxmd.com/read/29769042/mpcad-a-multi-scale-radiomics-driven-framework-for-automated-prostate-cancer-localization-and-detection
#3
Farzad Khalvati, Junjie Zhang, Audrey G Chung, Mohammad Javad Shafiee, Alexander Wong, Masoom A Haider
BACKGROUND: Quantitative radiomic features provide a plethora of minable data extracted from multi-parametric magnetic resonance imaging (MP-MRI) which can be used for accurate detection and localization of prostate cancer. While most cancer detection algorithms utilize either voxel-based or region-based feature models, the complexity of prostate tumour phenotype in MP-MRI requires a more sophisticated framework to better leverage available data and exploit a priori knowledge in the field...
May 16, 2018: BMC Medical Imaging
https://www.readbyqxmd.com/read/29761357/classifying-brain-metastases-by-their-primary-site-of-origin-using-a-radiomics-approach-based-on-texture-analysis-a-feasibility-study
#4
Rafael Ortiz-Ramón, Andrés Larroza, Silvia Ruiz-España, Estanislao Arana, David Moratal
OBJECTIVE: To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. METHODS: Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization...
May 14, 2018: European Radiology
https://www.readbyqxmd.com/read/29758038/novel-high-resolution-computed-tomography-based-radiomic-classifier-for-screen-identified-pulmonary-nodules-in-the-national-lung-screening-trial
#5
Tobias Peikert, Fenghai Duan, Srinivasan Rajagopalan, Ronald A Karwoski, Ryan Clay, Richard A Robb, Ziling Qin, JoRean Sicks, Brian J Bartholmai, Fabien Maldonado
PURPOSE: Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules. MATERIAL AND METHODS: Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408)...
2018: PloS One
https://www.readbyqxmd.com/read/29748206/deep-learning-convolutional-neural-networks-accurately-classify-genetic-mutations-in-gliomas
#6
P Chang, J Grinband, B D Weinberg, M Bardis, M Khy, G Cadena, M-Y Su, S Cha, C G Filippi, D Bota, P Baldi, L M Poisson, R Jain, D Chow
BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation...
May 10, 2018: AJNR. American Journal of Neuroradiology
https://www.readbyqxmd.com/read/29745561/-image-segmentation-and-classification-of-cytological-cells-based-on-multi-features-clustering-and-chain-splitting-model
#7
Pin Wang, Qianqian Liu, Lirui Wang, Yongming Li, Shujun Liu, Fang Yan
The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness...
August 1, 2017: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29735443/-differential-diagnosis-of-hepatocellular-carcinoma-and-hepatic-hemangiomas-based-on-radiomic-features-of-gadoxetate-disodium-enhanced-magnetic-resonance-imaging
#8
Mao-Dong Chen, Jing Zhang, Gui-Xiang Yang, Jie-Min Lin, Yan-Qiu Feng
OBJECTIVE: To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE). METHODS: Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE...
April 20, 2018: Nan Fang Yi Ke da Xue Xue Bao, Journal of Southern Medical University
https://www.readbyqxmd.com/read/29728249/false-positive-reduction-in-computer-aided-mass-detection-using-mammographic-texture-analysis-and-classification
#9
Sami Dhahbi, Walid Barhoumi, Jaroslaw Kurek, Bartosz Swiderski, Michal Kruk, Ezzeddine Zagrouba
BACKGROUND AND OBJECTIVE: The aim of computer-aided-detection (CAD) systems for mammograms is to assist radiologists by marking region of interest (ROIs) depicting abnormalities. However, the confusing appearance of some normal tissues that visually look like masses results in a large proportion of marked ROIs with normal tissues. This paper copes with this problem and proposes a framework to reduce false positive masses detected by CAD. METHODS: To avoid the error induced by the segmentation step, we proposed a segmentation-free framework with particular attention to improve feature extraction and classification steps...
July 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29727291/machine-learning-based-automatic-neovascularization-detection-on-optic-disc-region
#10
Shuang Yu, Di Xiao, Yogesan Kanagasingam
In this paper, the automatic detection of neovascularization in the optic disc region (NVD) for color fundus retinal image is presented. NV is the indicator for the onset of proliferative diabetic retinopathy and it is featured by the presence of new vessels in the retina. The new vessels are fragile and pose a high risk for sudden vision loss. Therefore, the importance of accurate and timely detection of NV cannot be underestimated. We propose an automatic image processing procedure for NVD detection that involves vessel segmentation using multilevel Gabor filtering, feature extraction of vessel morphological features and texture features, and image classification with support vector machine...
May 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29727280/automated-chest-x-ray-screening-can-lung-region-symmetry-help-detect-pulmonary-abnormalities
#11
K C Santosh, Sameer Antani
Our primary motivator is the need for screening HIV+ populations in resource-constrained regions for exposure to Tuberculosis, using posteroanterior chest radiographs (CXRs). The proposed method is motivated by the observation that radiological examinations routinely conduct bilateral comparisons of the lung field. In addition, the abnormal CXRs tend to exhibit changes in the lung shape, size, and content (textures), and in overall, reflection symmetry between them. We analyze the lung region symmetry using multi-scale shape features, and edge plus texture features...
May 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/29721616/development-of-a-robust-algorithm-for-detection-of-nuclei-and-classification-of-white-blood-cells-in-peripheral-blood-smear-images
#12
Roopa B Hegde, Keerthana Prasad, Harishchandra Hebbar, Brij Mohan Kumar Singh
Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei...
May 2, 2018: Journal of Medical Systems
https://www.readbyqxmd.com/read/29721517/automated-erythrocyte-detection-and-classification-from-whole-slide-images
#13
Darshana Govind, Brendon Lutnick, John E Tomaszewski, Pinaki Sarder
Blood smear is a crucial diagnostic aid. Quantification of both solitary and overlapping erythrocytes within these smears, directly from their whole slide images (WSIs), remains a challenge. Existing software designed to accomplish the computationally extensive task of hematological WSI analysis is too expensive and is widely unavailable. We have thereby developed a fully automated software targeted for erythrocyte detection and quantification from WSIs. We define an optimal region within the smear, which contains cells that are neither too scarce/damaged nor too crowded...
April 2018: Journal of Medical Imaging
https://www.readbyqxmd.com/read/29707381/comparing-pixel-and-object-based-approaches-in-effectively-classifying-wetland-dominated-landscapes
#14
Tedros M Berhane, Charles R Lane, Qiusheng Wu, Oleg A Anenkhonov, Victor V Chepinoga, Bradley C Autrey, Hongxing Liu
Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km2 ) in the Lake Baikal, Russia, drainage basin...
2018: Remote Sensing
https://www.readbyqxmd.com/read/29691123/a-computer-aided-detection-of-the-architectural-distortion-in-digital-mammograms-using-the-fractal-dimension-measurements-of-bemd
#15
Imad Zyout, Roberto Togneri
Achieving a high performance for the detection and characterization of architectural distortion in screening mammograms is important for an efficient breast cancer early detection. Viewing a mammogram image as a rough surface that can be described using the fractal theory is a well-recognized approach. This paper presents a new fractal-based computer-aided detection (CAD) algorithm for characterizing various breast tissues in screening mammograms with a particular focus on distinguishing between architectural distortion and normal breast parenchyma...
April 3, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29682411/pre-trained-convolutional-neural-networks-as-feature-extractors-toward-improved-malaria-parasite-detection-in-thin-blood-smear-images
#16
Sivaramakrishnan Rajaraman, Sameer K Antani, Mahdieh Poostchi, Kamolrat Silamut, Md A Hossain, Richard J Maude, Stefan Jaeger, George R Thoma
Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI)...
2018: PeerJ
https://www.readbyqxmd.com/read/29680688/absolute-cosine-based-svm-rfe-feature-selection-method-for-prostate-histopathological-grading
#17
Shahnorbanun Sahran, Dheeb Albashish, Azizi Abdullah, Nordashima Abd Shukor, Suria Hayati Md Pauzi
OBJECTIVE: Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components...
April 18, 2018: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/29670857/radiomics-evaluation-of-histological-heterogeneity-using-multiscale-textures-derived-from-3d-wavelet-transformation-of-multispectral-images
#18
Ahmad Chaddad, Paul Daniel, Tamim Niazi
Purpose: Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention. Methods: This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter...
2018: Frontiers in Oncology
https://www.readbyqxmd.com/read/29667885/radiomics-approach-to-prediction-of-occult-mediastinal-lymph-node-metastasis-of-lung-adenocarcinoma
#19
Yan Zhong, Mei Yuan, Teng Zhang, Yu-Dong Zhang, Hai Li, Tong-Fu Yu
OBJECTIVE: The purpose of this study was to evaluate the prognostic impact of radiomic features from CT scans in predicting occult mediastinal lymph node (LN) metastasis of lung adenocarcinoma. MATERIALS AND METHODS: A total of 492 patients with lung adenocarcinoma who underwent preoperative unenhanced chest CT were enrolled in the study. A total of 300 radiomics features quantifying tumor intensity, texture, and wavelet were extracted from the segmented entire-tumor volume of interest of the primary tumor...
April 18, 2018: AJR. American Journal of Roentgenology
https://www.readbyqxmd.com/read/29665725/epileptic-eeg-identification-via-lbp-operators-on-wavelet-coefficients
#20
Qi Yuan, Weidong Zhou, Fangzhou Xu, Yan Leng, Dongmei Wei
The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals...
March 19, 2018: International Journal of Neural Systems
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