Proceedings ArticleDOI
The Role of the Multiresolution Textural Features in Improving the Characterization and Recognition of the Liver Tumors, Based on Ultrasound Images
Delia Mitrea,Sergiu Nedevschi,Radu Badea +2 more
- pp 192-199
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TLDR
The role that some multiresolution textural features have in improving the liver tumors' diagnosis accuracy is analyzed, and features derived from the second and superior order GLCM and edge-based statistics, all computed after applying the Wavelet transform are added.Abstract:
The malignant tumors are complex structures, which evolve chaotically, invading the entire human body. The gold standard for cancer diagnosis is the biopsy, but this is invasive, dangerous. We elaborated non-invasive, computerized methods, for tumor characterization, based on ultrasound images. We defined the textural model of the malignant tumors, consisting of the relevant textural features, able to distinguish these structures from similar tissues, and of the specific values associated to the relevant features [1]. In this paper, we analyzed the role that some multiresolution textural features have in improving the liver tumors' diagnosis accuracy. In the new attribute set we added features derived from the second and superior order GLCM and edge-based statistics, all computed after applying the Wavelet transform. The experiments were performed on ultrasound images of patients suffering from hepatocellular carcinoma and from benign liver tumors, considering also the aspect of the cirrhotic parenchyma where the tumors evolve.read more
Citations
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Book ChapterDOI
Statistical Pattern Recognition
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
Journal ArticleDOI
The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization.
TL;DR: Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.
Journal ArticleDOI
Liver cancer detection and classification based on optimum hierarchical feature fusion with PeSOA and PNN classifier
TL;DR: A new optimum hierarchical feature fusion based on Penguin Search Optimization Algorithm (PeSOA) which is used by a Probabilistic Neural Network (PNN) which classifies the liver cancer tissues and demonstrates that the proposed technique obtained superior results than the existing strategies.
Proceedings ArticleDOI
Advanced Texture Analysis Techniques for Building Textural Models, with Applications in the Study of the Pathology Evolution Stages, based on Ultrasound Images.
TL;DR: This chapter describes specific, texture-based methods for the detection, characterization and recognition of some severe affections and of their evolution phases, using only information from ultrasound images.
Proceedings ArticleDOI
Filtering Techniques for Noise Reduction in Liver Ultrasound Images
Budi Utami Fahnun,A. Benny Mutiara,Eri Prasetyo Wibowo,Johan Harlan,Apriyadi Abdullah,Muhammad Abdul Latief +5 more
TL;DR: From the trial results, the best method for handling salt and pepper noise is the 3D median filter with MSE and RMSE values approaching 0 then having PSNR greater than 30 dB of all filter, and the wiener filter is thebest method to overcome Gaussian and speckle noise.
References
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Benchmarking attribute selection techniques for discrete class data mining
Mark Hall,Geoffrey Holmes +1 more
TL;DR: A benchmark comparison of several attribute selection methods for supervised classification by cross-validating the attribute rankings with respect to a classification learner to find the best attributes.
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Statistical Pattern Recognition
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
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An analysis of co-occurrence texture statistics as a function of grey level quantization
TL;DR: In this article, the effect of grey level quantization on the ability of co-occurrence probability statistics to classify natural textures is studied and the preferred statistics set (contrast, correlation, and entropy) is demonstrated to be an improvement over using single statistics or using the entire set of statistics.
Journal ArticleDOI
Wavelets for computer graphics: a primer.1
TL;DR: People working in computer graphics with some intuition for what wavelets are are provided, as well as to present the mathematical foundations necessary for studying and using them.