Diagnosis of lung nodule using Gini coefficient and skeletonization in computerized tomography images

Abstract

This paper uses the Gini coefficient and a set of skeleton measures, with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images.Based on a sample of 31 nodules, 25 benign and 6 malignant, these methods are first analyzed individually and then jointly, with classification and analysis techniques (linear stepwise discriminant analysis, leave-one-out and ROC curve). We have concluded that the individual measures and their combinations produce good results in the diagnosis of lung nodules.

Lung Cancer Detection System Using Image Processing and Machine Learning Techniques

“…Silva, Carvalho, and Gattass (2004) [15], This paper presents how the lung nodule is being diagnose using Gini Coefficient and skeletonization methods, the purpose of this method is used to characterize the lung cancer nodules as class malignant or as normal class benign in CT image dataset, in this article the researcher are initially analyze the texture by applying Gini coefficient, this method help to analyze the distribution of nodules in lung with the same there is another method is used in this paper that is skeletonization which analyze the shape of the nodules, through this authors has developed their prototype and using the discriminant analysis to categories the lung nodule as malignant or benign. The evaluation of the result was based on classification and ROC curve.…”

Efficient Segmentation of Lung Using FIDRM Filter and Successive Peak Quantization Transform

“…This results in set of regions that entirely covers the original image. Each pixel in the region is same as the computed property such as color, intensity or texture [1]. Several approaches have been introduced in order to detect and evaluate the lung cancer [2 -4].…”