Integration of Improved Region Growing (iRG) and Level Set Method for Automated Medical Image Segmentation


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Abstract


Precise segmentation of organs from medical images assists radiologists and surgeons in patient diagnosis and surgical planning. Nevertheless, the issues like intensity similarity, partial volume effect (PVE) between the neighboring organs; left the task of organ segmentation critical. The accurate segmentation of organs helps the surgeons to analyze the organ anatomy, physiology and pathology, that helps them in the treatment decision phase. In this paper, we propose an effective segmentation algorithm that integrates an improved Region Growing (iRG) algorithm with level set method for segmentation of organ structures from medical images. The level set evolution is initiated from the results of iRG. Moreover, the level set evolution is regularized with localized morphological gradient function and iRG controlled parameter setting. The performance of the proposed technique was tested on medical images from different modalities acquired across patients. The proposed method affirms its efficiency in medical image segmentation
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Keywords


Medical Imaging; Image Segmentation; Region Growing; Level Set Method; Image Classification

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