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
Brain tumor classification using the fused features extracted from expanded tumor region
TL;DR: In this paper , a brain tumor classification method using the fusion of deep and shallow features is proposed to distinguish between meningioma, glioma and pituitary tumor types and to predict the 1p/19q co-deletion status of LGG tumors.
Book ChapterDOI
Artificial life feature selection techniques for prostrate cancer diagnosis using TRUS images
TL;DR: When Support Vector Machine (SVM) classifier is applied for the purpose of tissue characterization, the features obtained using the PSO and ACO outperforms the features obtaining using the Genetic Algorithm, i.e., they are capable of discriminating between suspicious cancerous and non-cancerous in a better accuracy.
Journal ArticleDOI
Classification of left ventricular thrombi by their history of systemic embolization using pattern recognition of two-dimensional echocardiograms
TL;DR: 2DE and clinical variables that included texture features in the 2DE of 38 patients whose 2DE had LV thrombi were looked at and classification of patients into emboli versus no emboli groups was achieved, while using a minimal number of variables.
Journal ArticleDOI
The Recent Progress in Quantitative Medical Image Analysis for Computer Aided Diagnosis Systems
TL;DR: The main objective of this review is to attempt to introduce the diverse methods used for quantitative image analysis, and to provide a guide for clinicians.
Journal ArticleDOI
Brain tumor classification using the fused features extracted from expanded tumor region
TL;DR: In this paper, a brain tumor classification method using the fusion of deep and shallow features is proposed to distinguish between meningioma, glioma and pituitary tumor types and to predict the 1p/19q co-deletion status of LGG tumors.
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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