Abstract
Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.
Highlights
Cancer is one of the most dangerous types of disease, spreading day by day across the globe
The LIDC-IDRI of lung Computer Tomography (CT) images was used to evaluate the performance of the proposed algorithm
The Cancer Imaging Archive (TCIA) hosts the LIDC, which is freely accessible on website of the TCIA [35]
Summary
Cancer is one of the most dangerous types of disease, spreading day by day across the globe. One of the main causes of death is lung cancer. The underlying causes of cancer are not wholly known, which results in the frequent occurrence of the disease. Health Organization fact sheet, cancer is ranked as the leading cause of death across the globe. Several SR-based fusion approaches have been studied in recent years [21]. According to Zhu et al [22], image patches were generated using a sampling approach and classified by a clustering algorithm, and a dictionary was constructed using the K-SVD methodology. A medical image fusion scheme based on discriminative low-rank sparse dictionary learning was proposed by Li et al [23]. Convolutional-sparsity-based morphological component analysis was introduced by Liu et al in 2019 [24] as a sparse representation model for pixel-level medical image fusion.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
