Improved pulmonary nodule classification utilizing quantitative lung parenchyma features

Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.

Current computer-aided diagnosis (CAD) models for determining pulmonary nodule malignancy characterize nodule shape, density, and border in computed tomography (CT) data. Analyzing the lung parenchyma surrounding the nodule has been minimally explored. We hypothesize that improved nodule classification is achievable by including features quantified from the surrounding lung tissue. To explore t...

Lung nodule classification utilizing support vector machines

Lung cancer is one of the deadly and most common diseases in the world. Radiologists fail to diagnose small pulmonary nodules in as many as 30% of positive cases. Many methods have been proposed in the literature such as neural network algorithms. Recently, support vector machines (SVM)'s had received an increasing attention for pattern recognition. The advantage of SVM lies in better modeling ...

Lung Nodule Detection and Classification

Detection of malignant lung nodules in chest radiographs is currently performed by pulmonary radiologists, potentially with the aid of CAD systems. Recent advancements in convolutional neural network (CNN) models have improved image classification and detection for many tasks, but there has been little exploration of their use for nodule detection in chest radiographs. In this paper we explore ...

Lung Parenchyma Sparing Resection for Pulmonary Malignancies

Toward Precise Pulmonary Nodule Descriptors for Nodule Type Classification

A framework for nodule feature-based extraction is presented to classify lung nodules in low-dose CT slices (LDCT) into four categories: juxta, well-circumscribed, vascularized and pleural-tail, based on the extracted information. The Scale Invariant Feature Transform (SIFT) and an adaptation to Daugman's Iris Recognition algorithm are used for analysis. The SIFT descriptor results are projecte...