Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases. (March 2017)

• Record Type: Journal Article
• Authors: Abbas, Qaisar
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 325
• Page End: 334
• Publication Date: 2017-03
• Subjects: Lung cancer -- Computed tomography (CT) -- Computer-aided detection -- Segmentation -- Variational level-set -- Fuzzy c-means clustering -- Fuzzy entropy -- Discrete wavelet transform; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.12.019
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: The systematic flow diagram of the proposed system for segmentation of differential structures from CT scan images. Highlights: The pre-processing step is integrated to enhance by reconstruction of an input image into the 4 frequency subbands through discrete wavelet transform (DWT) and un-sharp energy mask (UEM). A new segmentation algorithm is developed for detecting of four differential structures from CT scan images of lungs. Local Energy constraints (LEC) are integrated to Fuzzy c-means clustering algorithm to effectively find out the candidate regions for effectively initialization of variational level set (VLS) method. Abstract: Computer-aided diagnostics (CAD) systems for automatic detection of lung cancer or lung-related diseases have highly depended on the segmentation accuracy of differential structures from computed tomography (CT) scan images. By detection of differential structures such as right/left Lungs, lung nodules, human airways and pulmonary trees, the new segmentation algorithm (PropSeg) is proposed. The PropSeg method is developed based on four major phases such as pre-processing, detection of candidate regions, segmentation, and post-processing. The pre-processing step is performed to enhance by reconstruction of an input image into the 4 frequency subbands through discrete wavelet transform (DWT) and un-sharp energy mask (UEM). The 3 levels of fuzzy c-means (FCM) clustering is used to detect candidate regions by an integration of local energy constraints (LEC) and variational level set (VLS) method is then utilized to segment differential regions. Moreover, the post-processing step is performed by morphological edge detection to enhance the results of segmentation. The system is tested with manually draw radiologist contours on the 220 images by using statistical measures. The performance of PropSeg is also compared with other four state-of-the-art segmentation methods. The achieve results show that the PropSeg system is outperformed compared to other techniques and it is favorable for automatic diagnosis of lung cancer or to detect lung-related diseases.