Literature DB >> 24108747

Advanced statistical matrices for texture characterization: application to cell classification.

Guillaume Thibault, Jesús Angulo, Fernand Meyer.   

Abstract

This paper presents new structural statistical matrices which are gray level size zone matrix (SZM) texture descriptor variants. The SZM is based on the cooccurrences of size/intensity of each flat zone (connected pixels with the same gray level). The first improvement increases the information processed by merging multiple gray-level quantizations and reduces the required parameter numbers. New improved descriptors were especially designed for supervised cell texture classification. They are illustrated thanks to two different databases built from quantitative cell biology. The second alternative characterizes the DNA organization during the mitosis, according to zone intensities radial distribution. The third variant is a matrix structure generalization for the fibrous texture analysis, by changing the intensity/size pair into the length/orientation pair of each region.

Mesh:

Year:  2013        PMID: 24108747     DOI: 10.1109/TBME.2013.2284600

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  56 in total

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Journal:  Eur Radiol       Date:  2015-05-21       Impact factor: 5.315

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4.  A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors.

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Journal:  J Magn Reson Imaging       Date:  2019-04-13       Impact factor: 4.813

5.  Spatial Bayesian modeling of GLCM with application to malignant lesion characterization.

Authors:  Xiao Li; Michele Guindani; Chaan S Ng; Brian P Hobbs
Journal:  J Appl Stat       Date:  2018-05-15       Impact factor: 1.404

6.  Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions.

Authors:  Chunfeng Lian; Su Ruan; Thierry Denoeux; Hua Li; Pierre Vera
Journal:  IEEE Trans Image Process       Date:  2018-10-05       Impact factor: 10.856

7.  Treatment Outcome Prediction for Cancer Patients based on Radiomics and Belief Function Theory.

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8.  Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes.

Authors:  Baderaldeen A Altazi; Daniel C Fernandez; Geoffrey G Zhang; Samuel Hawkins; Syeda M Naqvi; Youngchul Kim; Dylan Hunt; Kujtim Latifi; Matthew Biagioli; Puja Venkat; Eduardo G Moros
Journal:  Phys Med       Date:  2018-02-21       Impact factor: 2.685

Review 9.  The developing role of FDG PET imaging for prognostication and radiotherapy target volume delineation in non-small cell lung cancer.

Authors:  Tom Konert; Jeroen B van de Kamer; Jan-Jakob Sonke; Wouter V Vogel
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Review 10.  Machine Learning and Imaging Informatics in Oncology.

Authors:  Huan-Hsin Tseng; Lise Wei; Sunan Cui; Yi Luo; Randall K Ten Haken; Issam El Naqa
Journal:  Oncology       Date:  2018-11-23       Impact factor: 2.935

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