Abstract: A medical image processing apparatus of the present invention has a three-dimensional model estimating section for estimating, based on an inputted two-dimensional image of an image of a living tissue within a body cavity, a three-dimensional model of the living tissue, a shape feature value calculating section for calculating shape feature values of respective voxels included in the three-dimensional model of the living tissue, a three-dimensional shape extracting section for extracting a first voxel group whose three-dimensional model has been estimated as a predetermined shape, in the respective voxels included in the three-dimensional model of the living tissue, based on the shape feature values, and a protruding shape detecting section for detecting the first voxel group as a voxel group configuring a protruding shape in the three-dimensional model of the living tissue.

Abstract: An image processing apparatus according to the present invention includes a pixel selection unit adapted to select a pixel of interest from an image; a contrast feature value calculation unit adapted to calculate, as a contrast feature value, a value which represents an amount of change in contrast in a local region including the pixel of interest; a geometric feature value calculation unit adapted to calculate, as a geometric feature value, a value of an index which indicates whether or not a linear or massive structure is contained in the local region; an evaluation value calculation unit adapted to calculate an evaluation value by weighting each of the contrast feature value and the geometric feature value; and a region extraction unit adapted to extract a candidate region estimated to contain the linear or massive structure from the image based on a calculation result of the evaluation value.

Abstract: A medical image processing apparatus is a medical image processing apparatus performing image segmentation processing for a medical image including: a feature value calculating unit that calculates at least one feature value from the medical image; a category setting unit that sets, on the basis of the feature value calculated by the feature value calculating unit, plural categories in the medical image; a parameter setting unit that sets, on the basis of the feature value calculated by the feature value calculating unit, a parameter for each of areas of the medical image respectively classified into the categories set by the category setting unit; and an image segmenting unit that performs the image segmentation processing for the medical image according to an arithmetic operation of employing a dynamic contour model specified according to the number of categories set by the category setting unit and parameters set by the parameter setting unit.

Abstract: A medical image processing method for image processing of a medical image picking up an image of a living mucous comprises a boundary information detecting step for detecting boundary information corresponding to a boundary portion of a living mucous from the medical image and a mucous feature detecting step for detecting presence of a living mucous with a different feature on the basis of the boundary information detected at the boundary information detecting step.

Abstract: A medical image processing apparatus of the present invention includes an edge extracting section that extracts edges of an inputted two-dimensional image, a three-dimensional-model estimating section that estimates a three-dimensional model on the basis of the two-dimensional image, a voxel extracting section that extracts, on the basis of positions of respective voxels, where the edges are present, a predetermined voxel group to be set as a calculation object of a shape feature value, a shape-feature-value calculating section that calculates the shape feature value for at least a part of voxels among the predetermined voxel group, a three-dimensional-shape extracting section that extracts a voxel group, a three-dimensional model of which is estimated as a predetermined shape, on the basis of the shape feature value, and a tuberal-shape detecting section that detects the voxel group as a voxel group forming a tuberal shape in the three-dimensional model of the living tissue.

Abstract: The present invention provides a process for producing a precursor fiber which can provide a carbon fiber having high strength and high elastic modulus. The process of the present invention comprises a step where an aqueous solution of amphoteric molecule is prepared; a step where carbon nanotube is added to the aqueous solution of the amphoteric molecule so that the carbon nanotube is dispersed therein to prepare a dispersion of carbon nanotube; a step where the carbon nanotube dispersion is mixed with a polyacrylonitrile polymer and rhodanate or zinc chloride to prepare a spinning dope; a step where a coagulated yarn is prepared from the spinning dope by a wet or dry-wet spinning method; and a step where the coagulated yarn is drawn to give a precursor fiber for carbon fiber.

Abstract: There is provided a medical image processing apparatus including an image-extracting section extracting a frame image from in vivo motion picture data picked up by an in vivo image pickup device or a plurality of consecutively picked-up still image data, and an image analysis section analyzing the frame image extracted by the image-extracting section to output an image analysis result. The image analysis section includes a first biological-feature detection section detecting a first biological feature, a second biological-feature detection section detecting, based on a detection result obtained by the first biological feature detection section, a second biological feature in a frame image picked up temporally before or after the image used for detection by the first biological feature detection section; and a condition determination section making a determination for a biological condition based on a detection result obtained by the second biological feature detection section to output the determination.

Abstract: A signal processing circuit of an external device includes a CPU and a memory which are not shown. A program for estimating at least one of the position and orientation of a capsule endoscope on the basis of strength signals received through respective antennas is installed in the signal processing circuit. A single-core coil to generate a magnetic field is arranged in the capsule endoscope. The generated magnetic field is detected by a plurality of coils arranged outside a body, whereby a distance that the capsule endoscope has traveled can be obtained with accuracy. This arrangement controls image-capture timing to reliably capture images necessary for a diagnosis and prevent unnecessary image capture.

Abstract: A signal processing circuit of an external device includes a CPU and a memory which are not shown. A program for estimating at least one of the position and orientation of a capsule endoscope on the basis of strength signals received through respective antennas is installed in the signal processing circuit. A single-core coil to generate a magnetic field is arranged in the capsule endoscope. The generated magnetic field is detected by a plurality of coils arranged outside a body, whereby a distance that the capsule endoscope has traveled can be obtained with accuracy. This arrangement controls image-capture timing to reliably capture images necessary for a diagnosis and prevent unnecessary image capture.

Abstract: A signal processing circuit of an external device includes a CPU and a memory which are not shown. A program for estimating at least one of the position and orientation of a capsule endoscope on the basis of strength signals received through respective antennas is installed in the signal processing circuit. A single-core coil to generate a magnetic field is arranged in the capsule endoscope. The generated magnetic field is detected by a plurality of coils arranged outside a body, whereby a distance that the capsule endoscope has traveled can be obtained with accuracy. This arrangement controls image-capture timing to reliably capture images necessary for a diagnosis and prevent unnecessary image capture.

Abstract: There is provided a medical image processing apparatus including an image-extracting section extracting a frame image from in vivo motion picture data picked up by an in vivo image pickup device or a plurality of consecutively picked-up still image data, and an image analysis section analyzing the frame image extracted by the image-extracting section to output an image analysis result. The image analysis section includes a first biological-feature detection section detecting a first biological feature, a second biological-feature detection section detecting, based on a detection result obtained by the first biological feature detection section, a second biological feature in a frame image picked up temporally before or after the image used for detection by the first biological feature detection section; and a condition determination section making a determination for a biological condition based on a detection result obtained by the second biological feature detection section to output the determination.

Abstract: A plurality of images inputted in an image signal input portion are divided into a plurality of regions by an image dividing portion, and a feature value in each of the plurality of regions is calculated by a feature value calculation portion and divided into a plurality of subsets by a subset generation portion. On the other hand, a cluster classifying portion classifies a plurality of clusters generated in a feature space into any one of a plurality of classes on the basis of the feature value and occurrence frequency of the feature value. And a classification criterion calculation portion calculates a criterion of classification for classifying images included in one subset on the basis of a distribution state of the feature value in the feature space of each of the images included in the one subset.

Abstract: A plurality of images inputted in an image signal input portion are divided into a plurality of regions by an image dividing portion, and a feature value in each of the plurality of regions is calculated by a feature value calculation portion and divided into a plurality of subsets by a subset generation portion. On the other hand, a cluster classifying portion classifies a plurality of clusters generated in a feature space into any one of a plurality of classes on the basis of the feature value and occurrence frequency of the feature value. And a classification criterion calculation portion calculates a criterion of classification for classifying images included in one subset on the basis of a distribution state of the feature value in the feature space of each of the images included in the one subset.

Abstract: A plurality of images inputted in an image signal input portion are divided into a plurality of regions by an image dividing portion, and a feature value in each of the plurality of regions is calculated by a feature value calculation portion and divided into a plurality of subsets by a subset generation portion. On the other hand, a cluster classifying portion classifies a plurality of clusters generated in a feature space into any one of a plurality of classes on the basis of the feature value and occurrence frequency of the feature value. And a classification criterion calculation portion calculates a criterion of classification for classifying images included in one subset on the basis of a distribution state of the feature value in the feature space of each of the images included in the one subset.

Abstract: A signal processing circuit of an external device includes a CPU and a memory which are not shown. A program for estimating at least one of the position and orientation of a capsule endoscope on the basis of strength signals received through respective antennas is installed in the signal processing circuit. A single-core coil to generate a magnetic field is arranged in the capsule endoscope. The generated magnetic field is detected by a plurality of coils arranged outside a body, whereby a distance that the capsule endoscope has traveled can be obtained with accuracy. This arrangement controls image-capture timing to reliably capture images necessary for a diagnosis and prevent unnecessary image capture.

Abstract: An image processing apparatus and an image processing method which can improve efficiency of observation by a user are provided. The image processing apparatus of the present invention includes an image inputting unit configured to input a medical image including a plurality of color signals; a determining unit configured to determine whether the biological mucosa is sufficiently captured in the inputted medical image or not; and a controlling unit configured to control at least either of display or storage of the medical image based on the determination result in the determining unit.

Abstract: An endoscope inserting direction detecting system receives an endoscopic image, detects an endoscope inserting direction, in which an endoscope should be inserted, on the basis of the endoscopic image and produces information concerning the detected inserting direction. The endoscope inserting direction detecting method includes setting sampling pixels, detecting the direction of a change in brightness in the endoscopic image and evaluating the angles among a plurality of inserting-direction candidates and the direction of the change in brightness.

Abstract: An endoscope inserting direction method includes a first step of receiving endoscopic images time-sequentially, a second step of sampling pixels representing low densities from pixels of each of the endoscopic images which are time-sequentially received in the first step and a third step of determining whether or not number of pixels representing low densities which are sampled in the second step is equal to or larger than predetermined number of pixels. In a fourth step, if the number of pixels representing low densities is determined to be equal to or larger than the predetermined number of pixels in the third step, a position of the barycenter of the pixels representing low densities is obtained, and based on a change in the position of the barycenter of the pixels representing low densities of the time-sequentially received endoscopic images, a direction of a shift in the time-sequentially received plurality of endoscopic images is detected.

Abstract: A process for producing an optical compensation film comprises a liquid crystal layer application step (10) of applying a liquid crystal layer coating solution containing a liquid crystal compound to an alignment layer on a surface of a transparent substrate (4c) that is continuously transported; a liquid crystal layer drying step (11) of drying the liquid crystal layer; a cooling and curing step of curing the liquid crystal layer while cooling the liquid crystal layer to a temperature lower than a drying temperature used in the drying step; and a heating and curing step of curing the alignment layer while heating the alignment layer to a temperature higher than a cooling temperature used in the cooling and curing step. The process enables the durability and damage resistance as well as the optical characteristics of the optical compensation film to be independently controlled.

Abstract: A medical image processing apparatus having an image conversion section including a coordinate-system converting section for converting an image in an orthogonal coordinate system to an image in a polar coordinate system, for geometrically converting a medical image of a tubular part in vivo picked up and obtained by the coordinate-system converting section; a squamocolumnar junction detecting section for detecting a squamocolumnar junction that is a junction between a squamous epithelium and a columnar epithelium on the image of the polar coordinate system; and an analyzing section for comparing an evaluation value for a feature value determined for the detected squamocolumnar junction and a predetermined reference value and calculating an analysis result.