Abstract: An image processing program for cell observation in which living cells can be distinguished from foreign matter other than living cells during cell observation under wide observation conditions. The image processing program includes a step for capturing first and second images of an object positioned in the observation field of view at a predetermined time interval, a step for extracting a representation of the object shown in the obtained images, steps for aligning the rotational angular orientation of the object in the image plane and computing a correlation value, a step for determining whether the object is a living cell on the basis of the computed correlation value, and steps for outputting the result of the decision made with regard to the object.

Abstract: An image-processing method for processing a time lapse image includes acquiring a first image obtained by capturing an image of an object located within a field of view using an imaging device and a second image obtained by capturing an image of the object located within the field of view using the imaging device after a predetermined time period has elapsed, calculating a positional correction value for a positional shift between the first image and the second image, by using a correlation function weighted according to an image feature of each of the objects included in the fist image and the second image, and performing, based on the calculated positional correction value, a positional correction between the first image and the second image. The time lapse image is generated using the first image and the second image on which the positional correction has been performed.

Abstract: A spectral image processing system and method of performing robust unmixing on measurement noise. Based on an observed spectral image acquired from a specimen and emission spectral data of each of plural materials contained in the specimen, a contribution of each of the plural materials to the observed spectral image is unmixed by a process, including an evaluating step of evaluating reliability of each component of the observed spectral image based on a predicted spectral image of the observed spectral image, and a reflecting step of reflecting a result of the evaluation in a content of the unmixing.

Abstract: A spectral image processing method is capable of reducing noise while maintaining necessary information. The spectral image processing method performs processing on a spectral image of a specimen, including a step of normalizing spectra (=spectral brightness curves) of respective pixels constituting the spectral image such that their brightness levels become equal, a step of smoothing the normalized spectra in spatial directions of the respective pixels, and a step of denormalization of multiplying spectra of the respective pixels obtained by the smoothing by either one of brightness levels of the pixels corresponding the spectra and values corresponding to the brightness levels. Consequently, the noise can be reduced while information on brightness distribution on the image is maintained.

Abstract: An image of a plurality of areas of which two adjacent areas have different image characteristics from each other are acquired (steps 111 through 114); the image is analyzed in light of the difference between image characteristics, for example textures, of the two adjacent areas (step 115), and information about the boundary between the two adjacent areas is obtained (step 116). And by detecting shape information and/or position information of a given image area based on the obtained boundary information, shape information, position information, optical characteristic information, etc., of the object are detected (step 117). Thus, shape information, position information, optical characteristic information, etc., of the object are accurately detected.

Abstract: A degree-of-randomness calculation unit calculates the degrees of randomness of data values in the respective data sets as division results, on feature amount data at feature points of the signal waveforms obtained when an image pick-up unit picks up images of marks, while changing the data division form, in the respective data division forms, and calculates the sum of the degrees of randomness. A classification calculation unit classifies the feature points in the data division form in which the sum of degrees of randomness is minimized, thereby classifying the feature amount data into signal data and noise data. A position calculation unit calculates mark position information on the basis of the position of the feature point determined as signal data by S/N discrimination with reference to such degrees of randomness. As a consequence, the position information of each mark formed on the object is accurately detected.