Abstract: The disclosure relates to a method for automatically recognizing liver tumor types in ultrasound images. The method specifically comprises: using a plurality of Regions of Interest (ROIs) to represent a CEUS image; different lesions are distinguished by the performance and changes of the ROI in time and space; representing a space-time relationship between ROIs by establishing a model in time and space at the same time; and determining, by the model, a relatively appropriate ROI and relevant parameters of the model according to existing CEUS lesion samples by means of an iterative learning method. After giving a sample, an appropriate ROI can be determined and a reference diagnosis for the lesion can be given by removing part of inappropriate ROIs in advance and by means of a rapid search method.

Abstract: The disclosure relates to a method for automatically recognizing liver tumor types in ultrasound images. The method specifically comprises: using a plurality of Regions of Interest (ROIs) to represent a CEUS image; different lesions are distinguished by the performance and changes of the ROI in time and space; representing a space-time relationship between ROIs by establishing a model in time and space at the same time; and determining, by the model, a relatively appropriate ROI and relevant parameters of the model according to existing CEUS lesion samples by means of an iterative learning method. After giving a sample, an appropriate ROI can be determined and a reference diagnosis for the lesion can be given by removing part of inappropriate ROIs in advance and by means of a rapid search method.