Computer-assisted detection of infectious lung diseases: A review

Abstract

Respiratory tract infections are a leading cause of death and disability worldwide. Although radiology serves as a primary diagnostic method for assessing respiratory tract infections, visual analysis of chest radiographs and computed tomography (CT) scans is restricted by low specificity for causal infectious organisms and a limited capacity to assess severity and predict patient outcomes. These limitations suggest that computer-assisted detection (CAD) could make a valuable contribution to the management of respiratory tract infections by assisting in the early recognition of pulmonary parenchymal lesions, providing quantitative measures of disease severity and assessing the response to therapy. In this paper, we review the most common radiographic and CT features of respiratory tract infections, discuss the challenges of defining and measuring these disorders with CAD, and propose some strategies to address these challenges.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

“…In terms of efficiency, region-based segmentation methods can be considered efficient because the timings (a few seconds to a few minutes) and the computational cost reported in the literature are within the bounds of clinical utility (10)(11)(12)26,30,46). The repeatability of the regionbased segmentation methods depends on the location of the seed points (if seeding-based segmentation); hence, different region-based methods have different robustness for repeatability.…”

“…However, depending on the magnitude of noise and the precision of the neighborhood criteria, region-based methods can suffer from false negatives within the lung region and thus regions near the lungs, it may be more feasible to crop out the lung regions from the CT image and process the delineation algorithm on the newly defined region of interest in which artifacts do not exist anymore. Last, but not least, for challenging cases such as when nodules or pathologic conditions are near the lung boundary, attenuation remapping to the lung region as well as enhancing the lung boundary with edge detection can be useful for accurate segmentation without having failures (10)(11)(12)26,30,46).…”

“…Those segmentation methods have been shown to be effective in the calculation of lung volume and the initiation of computer-aided detection systems (10), which is considered in a wide range of clinical applications (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). However, those segmentation methods fail to perform efficiently when a pathologic condition or abnormality is present in moderate to marked lung volumes or demonstrates complex patterns of attenuation (11)(12)(13)(16)(17)(18). For example, cavities and consolidation can lead to inaccurate boundary identification lungs with pathologic conditions on chest CT images, with illustrations to provide radiologists with a better understanding of potential choices for decision support in everyday practice.…”

Medical Image Segmentation and Classification Using MKFCM and Hybrid Classifiers

“…In a D-dimensional hyperspace, particle , with velocity = [ 1 , 2 and are initialized randomly, updated generation to generation.…”

“…To overcome such problems and with an aim to achieve promising results in the diagnosis of TB, in the proposed technique we employ a hybrid classifier. The steps that are involved in processing of medical images are pre-processing, segmentation, feature extraction, classification [2]. The methods used for performing these processes in our system is explained below.…”