Lung Nodule Classification With Multilevel Patch-Based Context Analysis - 2014 PROJECT TITLE : Lung Nodule Classification With Multilevel Patch-Based Context Analysis - 2014 ABSTRACT: In this paper, we propose a unique classification technique for the four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural, and pleural-tail, in low dose computed tomography scans. The proposed technique relies on contextual analysis by combining the lung nodule and surrounding anatomical structures, and has three main stages: an adaptive patch-primarily based division is employed to construct concentric multilevel partition; then, a brand new feature set is intended to include intensity, texture, and gradient information for image patch feature description, and then a contextual latent semantic analysis-primarily based classifier is designed to calculate the probabilistic estimations for the relevant images. Our proposed method was evaluated on a publicly on the market dataset and clearly demonstrated promising classification performance. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Probability Diseases Feature Extraction Image Texture Medical Image Processing Image Classification Computerised Tomography Lung Semantic Networks Human Detection By Quadratic Classification On Subspace Of Extended Histogram Of Gradients - 2014 Application Of Cross Wavelet Transform For Ecg Pattern Analysis And Classification - 2014
