Abstract: Structural information can be accurately drawn even for a secondary image generated by using resampled image data and mask data. There is provided an image processing device including a mask data acquisition unit that acquires mask data obtained by defining a degree of drawing for each pixel of image data, a resampling processing unit that generates resampled image data by performing resampling processing on the image data, a resampled mask data generation unit that calculates an estimated value obtained by estimating pixel values of the resampled image data based on the mask data and pixels included in a predetermined local region of the image data to generate resampled mask data in which a mask value for each pixel of the resampled image data is determined based on the pixel values of the resampled image data and the estimated value, and an image generation unit that generates an image of a drawing target from the resampled image data and the resampled mask data.

Abstract: There is provided an image processing device capable of generating a more accurate analysis image by extracting a region, which exists at a boundary between different substances and has a pixel value that is non-uniform and continuously changes, from volume data. The image processing device acquires the volume data of a region which includes a large intestine, sets a plurality of starting points of region expansion in a boundary region between air and a residue in the large intestine, sets a condition of an expandable range of a width according to a gradient of a pixel value of each of the starting points, performs a region expansion process from each of the starting points according to the set condition, and generates the analysis image based on the result of the region expansion process.

Abstract: There is provided an image processing device capable of generating a more accurate analysis image by extracting a region, which exists at a boundary between different substances and has a pixel value that is non-uniform and continuously changes, from volume data. The image processing device acquires the volume data of a region which includes a large intestine, sets a plurality of starting points of region expansion in a boundary region between air and a residue in the large intestine, sets a condition of an expandable range of a width according to a gradient of a pixel value of each of the starting points, performs a region expansion process from each of the starting points according to the set condition, and generates the analysis image based on the result of the region expansion process.

Abstract: An image processing apparatus of the present invention includes a processing unit that calculates a first focal point position of a first parallax image group in the first attention region on the basis of the conditions including an attention region used to generate a stereoscopic image, generates the first parallax image group from the first focal point position by using volume data which is obtained from an image scanning apparatus, calculates a second focal point position which is located on a stereoscopic central line set when the first parallax image group is generated and which is the same depth direction position as a position of a point in the second attention region which is different from the first attention region, generates a second parallax image group from the second focal point position, and generates stereoscopic images by using the first parallax image group and the second parallax image group.

Abstract: There is provided a medical image processing device and a medical image processing method that can extract and display lesion candidate regions having different sizes and similarity forms at a time through a series of processing. Therefore, with respect to a first medical image, a first evaluation for the curved surface form of the first medical image is made to extract first lesion candidate regions. With respect to each of the first lesion candidate regions extracted by a first extracting unit, a second evaluation for the curved surface form thereof is made to extract a second lesion candidate region. The second lesion candidate region extracted by a second extracting unit is superimposed and displayed on a second medical image.

Abstract: A medical image processing device is configured to set a parameter to be used for detecting a lesion candidate region. For example, the medical image processing device is configured to input a first parameter P1 estimating a dimension of the lesion, calculate a second parameter P2 based on the first parameter P1, calculate a feature amount indicating a shape of an organ surface by using the second parameter in the medical image, and extract the lesion candidate region based on the calculated feature amount.

Abstract: There is provided a medical image processing device and a medical image processing method that can extract and display lesion candidate regions having different sizes and similarity forms at a time through a series of processing. Therefore, with respect to a first medical image, a first evaluation for the curved surface form of the first medical image is made to extract first lesion candidate regions. With respect to each of the first lesion candidate regions extracted by a first extracting unit, a second evaluation for the curved surface form thereof is made to extract a second lesion candidate region. The second lesion candidate region extracted by a second extracting unit is superimposed and displayed on a second medical image.

Abstract: A medical image processing device 100 is configured to be able to set a parameter to be used for detecting a lesion candidate region in executing a processing of detecting the lesion candidate region from an image of an organ surface in order to provide an image processing device for detecting a lesion region under the condition that an operator is allowed to easily change a detection object in accordance with diagnostic purposes. For example, a parameter P1 to be set is a polyp diameter. Values in accordance with modes or operator's desired values are set as values of the parameter P1. CPU 101 further calculates a parameter P2 such as the inter-distance between differentiation reference points based on the set parameter P1, calculates the shape (curvature value) of each point on the organ surface using the parameter P2, and extracts a point having a predetermined shape as a lesion candidate region.