摘要:
A method and device for interpreting and processing medical ultrasound and other video images. The mix of reflection coefficients in pixel windows is measured by determining the range and distribution of pixel gray scale values to establish 'echoic texture' characteristics. These characteristics are compared with corresponding characteristics of known tissues. The degree of correlation allows a prediction of tissue characteristics of the examined window.
摘要:
A medical diagnostic apparatus includes: a feature calculating unit configured to calculate a plurality of kinds of features based on a received signal from a specimen; a classification unit configured to classify attributes of a tissue of the specimen by using a feature determined among the plurality of kinds of features calculated by the feature calculating unit according to a classification item selected in advance, and configured to assign visual information according to a classification result to pixels of an image based on the received signal; and a feature image data generating unit configured to generate feature image data on which the visual information assigned to the pixels of the image based on the received signal is superimposed.
摘要:
The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.
摘要:
A method for analysing an image of a lesion on the skin of a subject including (a) identifying the lesion in the image by differentiating the lesion from the skin; (b) segmenting the image; and (c) selecting a feature of the image and comparing the selected feature to a library of predetermined parameters of the feature. The feature of the lesion belongs to any one selected from the group: colour, border, asymmetry and texture of the image.
摘要:
A feature value calculating section 43a calculates a feature value from a medical image picked up of a living mucous membrane, an extraction section 43b extracts a living mucous membrane microstructure based on a calculated feature value, the feature value calculating section 43a further calculates a feature value from an extracted living mucous membrane microstructure, and a region division section 43c divides the structure into partial regions according to a predetermined condition based on a calculated feature value.
摘要:
Systems and methods for classifying tissue using quantitative ultrasound techniques. Parameters are calculated directly from raw echo signal data acquired from a region-of-interest during an ultrasound scan and parametric maps are produced using these parameters. These parameters can be calculated after normalizing the echo signal data using reference data so as to mitigate the effects of variations in instruments settings, ultrasound beam diffraction, and attenuation effects. First-order and second- order statistical measures are computed from these parametric maps, and are used to classify the tissue or tissues in the region-of-interest. Using these systems and methods, tissue can be classified with different levels of classification. For example, a tissue characterized as malignant cancer can additionally be graded (e.g., Grade I, II, or III).
摘要:
A feature value calculating section 43a calculates a feature value from a medical image picked up of a living mucous membrane, an extraction section 43b extracts a living mucous membrane microstructure based on a calculated feature value, the feature value calculating section 43a further calculates a feature value from an extracted living mucous membrane microstructure, and a region division section 43c divides the structure into partial regions according to a predetermined condition based on a calculated feature value.
摘要:
An image processing apparatus is provided with: a first feature value calculating section calculating a first feature value for each of pixels constituting an image obtained by picking up an image of a subject; a region dividing section dividing the image into multiple regions on the basis of the first feature values; a second feature value calculating section calculating a second feature value for each of the divided regions; a classification section performing classification with regard to which of multiple kinds of attributes each region of the multiple regions has, on the basis of the second feature values; and a diagnostic support information calculating section calculating diagnostic support information for supporting a diagnosis, on the basis of rates of attributes which two or more predetermined number of regions among the multiple regions classified by the classification section have.
摘要:
An image processing device classifies an attention region included in an image into multiple classification items. The image processing device has: an initial region detector (110) that detects at least part of the attention region and sets the part as an initial region; an expansion region detector (120, 210) that detects an expansion region by expanding the initial region; and a region determining unit (130, 310) that calculates feature data of the initial region and the expansion region, and determines, based on the feature data, to which of the multiple classification items the attention region belongs.