CT Images Of Lung Cancer Research Articles

Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm

This paper focuses on early stage lung cancer detection. Genetic K-Nearest Neighbour (GKNN) Algorithm is proposed for the detection which is a non parametric method. This optimization algorithm allows physicians to identify the nodules present in the CT lung images in the early stage hence the lung cancer. Since the manual interpretation of the lung cancer CT images are time consuming and very critical, to overcome this difficulty the Genetic Algorithm method is combined with K-Nearest Neighbour (K-NN) algorithm which would classify the cancer images quickly and effectively. The MATLAB image processing toolbox based implementation is done on the CT lung images and the classifications of these images are carried out. The performance measures like the classification rate and the false positive rates are analyzed. In traditional K-NN algorithm, initially the distance between all the test and training samples are calculated and K-neighbours with greater distances are taken for classification. In this proposed method, by using Genetic Algorithm, K (50-100) numbers of samples are chosen for each iteration and the classification accuracy of 90% is achieved as fitness. The highest accuracy is recorded each time.

Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

Fostering Open Science in Lung Cancer Lesion Sizing with ITK module LSTK

This document describes the latest efforts in integrating the Lesion Sizing Toolkit (LSTK) into ITK v4 as an external/remote module providing an Open Science dashboard website with a large open image archive of lung cancer CT images for LSTK development and testing.