Abstract: An image recognition method and an image recognition system can be applied to fetal ultrasound images. The image recognition method includes: (a) adjusting the image with a filter operator to decrease noise and to homogenize an image expression level of the pixel units within an individual object structure; (b) analyzing the image by a statistic information function, determining a foreground object pixel unit and a background pixel unit according to a max information entropy state of the statistic information function; and (c) searching by a profile setting value and recognizing a target object image among the foreground object pixel unit. The image recognition method can not only increase the efficiency of identifying the object of interests within the image and measuring the object of interests, but also improve the precision of measurements of the object of interests.

Abstract: An image segmentation system for performing image segmentation on an image data includes an image preprocessing module, a motion analyzing module, a detection module, a classification module, and a multi-dimensional detection module. The image data has a plurality of image stacks ordered chronologically that respectively have a plurality of images sequentially ordered according to spatial levels, wherein one spatial level is designated as a first stack. The image preprocessing module transforms the images into binary images while the motion analyzing module finds a repeating pattern in the binary images in the first stack and accordingly generates a repeating motion result. The classification module generates a classification result based on a spatial and an anatomical assumption to classify objects. The multi-dimensional detection module generates segmentation results for stacks above and below the first stack using spatial and temporal consistency of geometric layouts of object structures.

Abstract: A real-time cloud imaging system includes at least a frontend device and a backend system. The frontend device generates an instruction message and a ROI (Region of Interest) message, and the backend system is coupled to the frontend device. The backend system has at least a raw image, wherein the raw image has a plurality of images of different resolutions. Each of the images and the raw image are composed of a plurality of tiles. The ROI message corresponds to a region of interest respectively within each of the images and the raw image. The backend system, according to the instruction message and the ROI message, selectively provides a grouping of the tiles within the region of interest of the raw image or one of the images to the frontend device.

Abstract: An artificial intelligence analysis, pattern recognition and prediction method is implemented with software installed in computer hardware to create a system. The method has a classified data inputting act, a first learning act, a building act, an unclassified data inputting act, an analyzing act, a comparing act, an ending act, a transferring act and a second learning act. The comparing act is the comparing of an actual classifier of a testee with a predicted classifier by the system, and results in conformity or nonconformity between the actual class label and the predicted class label. The second learning act is the learning of the new data by the machine learning algorithm when nonconformity is the result of the comparing act. The refining act is the refining of the rules and patterns. The method concludes a predicted result and refines itself when the predicted result is different from an actual result.