Development of computed tomography lung segmentation using image processing

Zamri, Syahirah (2021) Development of computed tomography lung segmentation using image processing. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Aim of this project is to analyze computed tomography lung segmentation using image processing. Image processing is a technique for converting an image into a digital format and executing operations to produce a better image or derive valuable information. Changes in images are automatically implemented and based on algorithms. Segmentation is a significant step in the radiological assessment and computer-assisted diagnostic analysis of medical imagery and classification and the computational approach to determining thoracic-tissue boundary lung borders on computed tomography. Besides, lung segmentation is important technique to allow correct selection of region of interest and improve classification of the lung. In addition, for analyze segmentation of lung is by using noise filters followed by the thresholds are to create a binary mask for the lung area. Formally, the binary mask will only select the lung region and clear the all regions surrounding the lung area. The binary mask to show both lungs simultaneously. For the result, there is a comparison between five patients with ten slices randomly and successfully to segment every lung pair

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