Study of computed tomography images of the lungs: A survey

Abstract

Computed tomography (CT) technology helps us to acquire high resolution, isotropic images of the lungs in a single breath hold. Analysis of these large volumes of data manually is very time consuming and tedious. Automation of analysis of the CT images is therefore vital in the study of CT images. This paper reviews the literature on computer analysis of the lungs in CT scans addressing segmentation of various lungs anatomical structures and works on detection and quantification of chest abnormalities.

Automated Detection of Cancerous Lung Nodule from the Computed Tomography Images

“…But in our project; in the final step we get the lung nodule segmented part. CT technology was also adopted by K.Devaki et al [13] to acquire high resolution of the lungs in a single breath hold. Analysis of these large volumes of image data was manually performed, which was time consuming task.…”

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

“…Various research papers have discussed challenges associated with diagnosing medical images, but very few are found to be associated with chest x-ray [5] [6]. Over the period of time, certain researchers have dedicated themselves to discuss the existing challenges as well as problems associated with image processing techniques over chest radiographs [7][8] [9]; however, there is no effective disclosure about effectivity in the approaches presented by researchers. The automated diagnosis processes (on the basis of research) are categorized into two problems, i.e., identification process and classifying the disease.…”

“…The automated diagnosis processes (on the basis of research) are categorized into two problems, i.e., identification process and classifying the disease. There are various significant techniques for assisting in detection operation for chest radiographs [10]- [15]. The detection process is mainly dependent on segmentation techniques [16] and feature-based aspects [17] while the classification techniques are more into involving machine learning approaches [18]- [20].…”

Techniques for Detection of Solitary Pulmonary Nodules in Human Lung and Their Classifications -A Survey

“…Since the invention in 1972 by G.N.Hounsfiled, the advancement in CT imaging has contributed in a great deal to the field of Pathology. CT has been influential in increasing the survival rate by diagnosing the life threatening ailments, mainly the cancer [4]. Recent CT scanners exhibit isotropic acquisition of whole chest with very high resolution within a single breath hold.…”