Dai Nippon Printing Co., Ltd.

Tetsutaro Ono

Tomoaki Goto

Hiroshi Matsuda

Seiji Matsuba

Kunihiko Kasahara

A diagnosis support device, which is suitable for comparing different diseases, is provided, along with others. In the diagnosis support device, the input of an MRI brain image of a subject is received, and a shrinkage score, which represents the degree of shrinkage of the brain, is calculated based on the MRI brain image. Subsequently, sites to be compared in the brain are identified. Then, a degree of shrinkage, which represents the degree of shrinkage of each of the identified sites, and a shrinkage ratio, which is the ratio between the degrees of shrinkage of the sites, are calculated, and then compared and displayed.

Medical image display processing method, medical image display processing device, and program

Provided is a medical image processing device capable of notifying the diagnosis personnel that a segmentation error has occurred or may have occurred during tissue segmentation processing. This medical image processing device specifies a gray matter image of a subject, smoothes the gray matter image, and, in accordance with an elevation function for calculating an absolute Z score, calculates an elevation value. Next, the medical image processing device compares the evaluation value with a pre-defined threshold value and determines the segmentation result, and, if the separation result is determined to be abnormal, warns that segmentation result is abnormal and displays a segmentation result display screen showing the segmentation result.

Medical image processing device, medical image processing method, program

Provided is a medical image processing device capable of determining a state of an obtained brain image and adjusting the obtained image to suit for performing tissue separation processing. The medical image processing device is configured to select a slice image to be processed as a target slice image from a brain image configured by a plurality of slice images, performs processing for measuring an effective maximum value in the cerebral parenchyma, an effective maximum value in a whole image, and a peak average value around the skull with respect to the selected target slice to determine necessity of high-signal-value-control processing based on the measured effective maximum value in the cerebral parenchyma, the effective maximum value in a whole image, and the peak average value around the skull so that when it is determined that the high-signal-value-control processing is necessary, the high-signal-value-control processing is performed to the brain image.

Medical image processing device

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.

Medical image display processing method, device, and program

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.

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Tokyo, Japan

Tetsutaro Ono