Abstract: A commodity identification device is provided with: one or more processing devices; and one or more storage devices storing instructions for causing the one or more processing devices to: obtain a taken image; determine whether a commodity is provided with a corresponding mark or not by using a mark detector on the obtained taken image; identify which of a plurality of commodity groups a commodity provided with the mark belongs to by using a first neural network having learned the commodity, on the taken image where the mark is detected; and identify the commodity by using a second neural network having performed learning for each of the commodity groups.

Abstract: A commodity identification device is provided with: one or more processing devices; and one or more storage devices storing instructions for causing the one or more processing devices to: obtain a taken image; determine whether a commodity is provided with a corresponding mark or not by using a mark detector on the obtained taken image; identify which of a plurality of commodity groups a commodity provided with the mark belongs to by using a first neural network having learned the commodity on the taken image where the mark is detected; and identify the commodity by using a second neural network having performed learning for each of the commodity groups.

Abstract: When brain images are inputted through MRI (Magnetic Resonance Imaging) and subjected to image processing to assist the diagnosis of brain diseases, functional and morphological images in an ASL coordinate system are inputted from the head of a subject by an ASL (Arterial Spin Labeling) imaging method using an MRI device. The inputted functional images are subjected to mask processing to extract only the region of cerebral parenchyma, and functional images of only the extracted region of the cerebral parenchyma are thereby produced and displayed to be overlaid on the morphological images. In this manner, a functional image such as a perfusion weighted image of only the region of the cerebral parenchyma can be extracted and displayed overlaid on a morphological image.

Abstract: When brain images are inputted through MRI (Magnetic Resonance Imaging) and subjected to image processing to assist the diagnosis of brain diseases, functional and morphological images in an ASL coordinate system are inputted from the head of a subject by an ASL (Arterial Spin Labeling) imaging method using an MRI device. The inputted functional images are subjected to mask processing to extract only the region of cerebral parenchyma, and functional images of only the extracted region of the cerebral parenchyma are thereby produced and displayed to be overlaid on the morphological images. In this manner, a functional image such as a perfusion weighted image of only the region of the cerebral parenchyma can be extracted and displayed overlaid on a morphological image.

Abstract: Input MRI brain images are positioned so as to correct a spatial deviation, gray matter tissues are extracted from these images to effect a first image smoothing, the thus-obtained images are subjected to anatomical standardization, a second image smoothing is effected, the gray level is corrected, brain images after correction are statistically compared with MRI brain images of normal cases, thereby providing the diagnosis result. In this instance, the brain images are automatically checked for input images regarding the resolution dot density and the like, the result of gray matter tissue extraction and the result of anatomical standardization, by which specifications of input images and the like can be confirmed objectively and automatically to make a diagnosis automatically by image processing. Further, an ROI-based analysis is made to provide the analysis result as the diagnosis result.

Abstract: Input MRI brain images are positioned so as to correct a spatial deviation, gray matter tissues are extracted from these images to effect a first image smoothing, the thus-obtained images are subjected to anatomical standardization, a second image smoothing is effected, the gray level is corrected, brain images after correction are statistically compared with MRI brain images of normal cases, thereby providing the diagnosis result. In this instance, the brain images are automatically checked for input images regarding the resolution dot density and the like, the result of gray matter tissue extraction and the result of anatomical standardization, by which specifications of input images and the like can be confirmed objectively and automatically to make a diagnosis automatically by image processing. Further, an ROI-based analysis is made to provide the analysis result as the diagnosis result.