Journal ArticleDOI
Texture analysis using gray level run lengths
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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Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
Kevin M. Boehm,Emily A. Aherne,Lora H. Ellenson,Ines Nikolovski,Mohammed Alghamdi,Ignacio Vázquez-García,Dmitriy Zamarin,Kara Long Roche,Ying Li,Druvesh Patel,Andrew Aukerman,Arfath Pasha,Doori Rose,Pier Selenica,Pamela Ines Causa Andrieu,Chris Fong,Marinela Capanu,Jorge S. Reis-Filho,Rami Vanguri,Harini Veeraraghavan,Natalie Gangai,Ramon E. Sosa,Samantha Leung,Andrew McPherson,Jianjiong Gao,Yulia Lakhman,Sohrab P Shah +26 more
TL;DR: In this article , a multimodal dataset of 444 patients with high grade serous ovarian cancer was assembled and compared with histopathological, radiologic, and clinicogenomic features.
Journal ArticleDOI
Improved automated detection of glaucoma from fundus image using hybrid structural and textural features
TL;DR: A reliable computer-aided diagnosis system is proposed based on a novel combination of hybrid structural and textural features that has given exceptional results with 100% accuracy for glaucoma referral.
Journal ArticleDOI
A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors
Xiaopan Xu,Huanjun Wang,Peng Du,Fan Zhang,Shurong Li,Zhongwei Zhang,Jing Yuan,Zhengrong Liang,Xi Zhang,Yan Guo,Yang Liu,Hongbing Lu +11 more
TL;DR: This data indicates that preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients and should be considered for clinical practice.
A Review of Medical Image Classification Techniques
TL;DR: Two Medical image Classification can play an important role in diagnostic and teaching purposes in medicine and many classifications created for medical images using both grey-scale and color medical images.
Journal ArticleDOI
Magnetic Resonance Imaging-Based Grading of Cartilaginous Bone Tumors: Added Value of Quantitative Texture Analysis.
Benjamin Fritz,Daniel Müller,Reto Sutter,Moritz C. Wurnig,Matthias W. Wagner,Christian W. A. Pfirrmann,Michael A. Fischer +6 more
TL;DR: Texture analysis improves diagnostic accuracy for differentiation of benign and malignant as well as for benign and low-grade cartilaginous lesions when compared with morphologic MRI analysis.
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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