An Enhanced Approach for Segmentation of Liver from Computed Tomography Images

287-293An accurate segmentation of liver from Computed Tomography (CT) scans is essential for liver tumor research as it offers valuable information for clinical diagnosis and treatment. However, it is challenging to achieve an accurate segmentation of the liver because of the blurred edges, low contrast and similar intensity of the organs in the CT scan. In this paper, an automated model which will segment the liver from CT images using a hybrid algorithm has been used. The segmentation of liver from CT scan is done with the help of Particle Swarm Optimization (PSO) followed by level set algorithm. The ultimate aim of using this hybrid algorithm is to improve the accuracy of liver segmentation. Computer aided classification of liver CT into healthy and tumorous images aids in diagnosis of liver diseases. It can help a great deal in diagnosis of liver disorders. In order to achieve better classification results, it is of high importance to segment the liver accurately without an error of over or under segmentation. The results obtained indicate that the approach used in this work is faster and has 98.62% accuracy, 99.2% specificity, 97.1% sensitivity, 97.8% F-measure, 96.6% Matthews Coefficient Constant (MCC), 99.08% precision, 97.8% dice coefficient and 95.7% jaccard coefficient in segmenting the liver

Reinforcing optimization enabled interactive approach for liver tumor extraction in computed tomography images

Detecting liver abnormalities is a difficult task in radiation planning and treatment. The modern development integrates medical imaging into computer techniques. This advancement has monumental effect on how medical images are interpreted and analyzed. In many circumstances, manual segmentation of liver from computerized tomography (CT) imaging is imperative, and cannot provide satisfactory results. However, there are some difficulties in segmenting the liver due to its uneven shape, fuzzy boundary and complicated structure. This leads to necessity of enabling optimization in interactive segmentation approach. The main objective of reinforcing optimization is to search the optimal threshold and reduce the chance of falling into local optimum with survival of the fittest (SOF) technique. The proposed methodology makes use of pre-processing stage and reinforcing meta heuristics optimization based fuzzy c-means (FCM) for obtaining detailed information about the image. This information gives the optimal threshold value that is used for segmenting the region of interest with minimum user input. Suspicious areas are recognized from the segmented output. Both public and simulated dataset have been taken for experimental purposes. To validate the effectiveness of the proposed strategy, performance criteria such as dice coefficient, mode and user interaction level are taken and compared with state-of-the-art algorithms