Comprehensive Review of Abdominal Image Segmentation using Soft and Hard Computing Approaches

Medical image segmentation is the method of partitioning a medical image obtained from different modalities such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) into its constituent’s regions. Now a day, an automated system for segmentation plays a vital role in quick diagnosis and treatment planning by the radiologists. As the abdominal image dataset is having multiple organs such as kidney, liver, aorta, spleen and many more, so the accurate segmentation of different organs is a challenging issue. Though, there are several number of techniques based on soft and hard computing approaches have been developed for multiple organ segmentation from the abdominal image dataset. Still, accurate and efficient segmentation of different organs draws the attention of the researchers around the world. This paper provides a detailed review of hard and soft computing approaches by the analysis of techniques, preprocessing procedure, observation, advantages and disadvantages of the different current literatures of abdominal image segmentation. By referring to this paper, researchers can easily quick review the recent most cited papers based on abdominal image segmentation.