Journal ArticleDOI
Texture analysis using gray level run lengths
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TLDR
In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.About:
This article is published in Computer Graphics and Image Processing.The article was published on 1975-06-01. It has received 1848 citations till now. The article focuses on the topics: Image texture & Texture (geology).read more
Citations
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Journal ArticleDOI
Radiomics in paediatric neuro-oncology: A multicentre study on MRI texture analysis.
Ahmed E. Fetit,Ahmed E. Fetit,Jan Novak,Jan Novak,Daniel Rodriguez,Dorothee P. Auer,Chris A. Clark,Richard Grundy,Andrew C. Peet,Andrew C. Peet,Theodoros N. Arvanitis,Theodoros N. Arvanitis,Theodoros N. Arvanitis +12 more
TL;DR: Radiomics, the high‐throughput extraction and analysis of quantitative image features (e.g. texture), offers potential solutions for tumour characterization and decision support.
Journal ArticleDOI
Machine Learning and Imaging Informatics in Oncology.
TL;DR: This review will review example applications of ML in oncology from the literature, identify current challenges and highlight future potentials, and provide an overview of ML methodologies and imaging informatics techniques and their recent application in modern oncologists.
Journal ArticleDOI
[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab.
Damijan Valentinuzzi,Damijan Valentinuzzi,Martina Vrankar,Nina Boc,Valentina Ahac,Ziga Zupancic,Mojca Unk,Katja Skalic,Ivana Zagar,Andrej Studen,Andrej Studen,U Simoncic,U Simoncic,Jens C. Eickhoff,Robert Jeraj,Robert Jeraj,Robert Jeraj +16 more
TL;DR: Multivariate iRADIOMICS was identified as a promising imaging biomarker, which could improve management of metastatic NSCLC patients treated with pembrolizumab and could be offered other treatment options to improve their overall survival.
Journal ArticleDOI
Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.
TL;DR: DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and is demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation.
Journal ArticleDOI
Neural network based texture analysis of CT images for fatty and cirrhosis liver classification
TL;DR: Experimental results show that PNN is a good classifier, giving an accuracy of 95% for classifying fatty and cirrhosis liver using wavelet based statistical texture features.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Gray-Level Manipulation Experiments for Texture Analysis
TL;DR: Some gray-level manipulation techniques are described, the first of which involves changing thegray-level distribution within the picture, and a method for extracting relatively noise-free objects from a noisy background is described.
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