Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

Term extraction using machine learning

In this term paper I motivate and describe a monolingual term extraction method using the Ripper machine learning algorithm and linguistic and statistical features of n-grams extracted from a patent text corpus. The n-grams are labeled as terms or non-terms using a manually validated term list based on the same patent text corpus. The results for experiments conducted show promise for further r...

Intravascular Ultrasound Images Vessel Characterization Using AdaBoost

This paper presents a method for accurate location of the vessel borders based on boosting of classifiers and feature selection. Intravascular Ultrasound Images (IVUS) are an excellent tool for direct visualization of vascular pathologies and evaluation of the lumen and plaque in coronary arteries. Nowadays, the most common methods to separate the tissue from the lumen are based on gray levels ...

Maximum Margin Clustering Using Extreme Learning Machine

Maximum margin clustering (MMC) is a newly proposed clustering method, which extends large margin computation of support vector machine (SVM) to unsupervised learning. But in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maxi...

Named Entity Extraction using AdaBoost

Automated blood vessel extraction using local features on retinal images

An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak...