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Karaciğer Kist Hidatiğinin Doku Analizi Texture Analysis of Liver Hydatid Cyst
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TLDR
The results indicate that the texture features computed from the gray level cooccurrence matrix do not constitute a good candidate to be used in classification and/or diagnosis of liver tissue as normal or cystic, due to homogeneity of these two tissue types within themselves.Abstract:
Images which are obtained in clinical radiology are generally evaluated visually. Some information which is available in the images, but not possible to be seen visually can be useful for diagnosis of some diseases. Cyst hydatid which is a parasitic liver disease is still an important health problem in countries where animal breeding is widespread. In this study, we aimed at producing some objective measures using image analysis, which will be of assistance in the diagnosis of cyst hydatid. The prevalence rate of this condition is relatively high in Turkey. In order to differentiate between regions of liver with cyst hydatid and healthy parenchymal tissues, we have used second order texture features computed from gray level cooccurrence matrix of liver CT images. We have then used these features from the two groups in designing a classifier using probabilistic neural network. Our results indicate that the texture features computed from the gray level cooccurrence matrix do not constitute a good candidate to be used in classification and/or diagnosis of liver tissue as normal or cystic. This must be due to homogeneity of these two tissue types within themselves. 1. Giris |, ( , ), (, ) , ( , ) }read more
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
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
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Probabilistic neural networks
TL;DR: A probabilistic neural network that can compute nonlinear decision boundaries which approach the Bayes optimal is formed, and a fourlayer neural network of the type proposed can map any input pattern to any number of classifications.
Book
Texture analysis
Mihran Tuceryan,Anil K. Jain +1 more
TL;DR: The geometric, random field, fractal, and signal processing models of texture are presented and major classes of texture processing such as segmentation, classification, and shape from texture are discussed.
Book
Handbook of Pattern Recognition and Computer Vision
TL;DR: This book provides the latest advances on pattern recognition and computer vision along with their many applications and features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers.
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