14-05-2017, 01:41 PM
matlab code for liver tumor segmentation pdf
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matlab code for liver tumor segmentation pdf
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matlab code for liver tumor segmentation pdf
19-05-2017, 09:15 AM
Segmentation of computed tomography (CT) liver tumors and analysis of tumor burden play an important role in the choice of therapeutic strategies for liver disease and follow-up of treatment. In this work, a new segmentation method is proposed for liver tumors from CT with contrast. Since the manual segmentation of tumors for the planning of liver treatment is both labor-intensive and time-consuming, highly accurate automatic tumor segmentation is desired. The proposed framework is fully automatic and requires no user interaction. The proposed segmentation evaluated in the clinical real-world data of the patients is based on a hybrid method that integrates the cuckoo optimization and the fuzzy c-means algorithm with the random algorithm of the walkers. The accuracy of the proposed method was validated using a clinical data set from the liver containing one of the highest numbers of tumors used for segmentation of the hepatic tumor containing 127 tumors in total with further validation of the results by a consultant radiologist. The proposed method was able to achieve one of the highest reported accuracies in the literature for hepatic tumor segmentation compared to other segmentation methods with an average overlap error of 22.78% and similarity coefficient of data of 0, 75 in 3Dircadb and an overlap error of 15.61% and the coefficient of similarity of the data of 0.81 in the MIDAS data set. The proposed method was able to overcome most of the other tumor segmentation methods reported in the literature, while it represents an improvement of the overlap error of 6% compared to one of the most effective automatic methods in the literature. The proposed framework was able to consistently provide accurate results taking into account the number of tumors and variations in tumor contrast enhancements and tumor appearances, whereas tumor burden was estimated with an average error of 0.84% in 3Dircadb dataset.
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