A data processing method for a biochip comprises: acquiring a biochip image to be detected; performing binarization processing on the biochip image to obtain a binary image; performing a morphological dilation operation on the binary image in a row direction to obtain a first image, and performing a morphological dilation operation on the binary image in a column direction to obtain a second image; performing connected domain detection on the first image in the row direction, and performing connected domain detection on the second image in the column direction, to determine the number of rows and the number of columns of a sample point array and center position information of each sample point.

Artwork carrying machine readable data is generated by editing artwork according to a data signal or transforming the data signal into artwork. The machine-readable data signal is generated from a digital payload and converted into an image tile. Artwork is edited according to the image tile by moving graphic elements, adapting intersections of lines, or altering line density, among other techniques. Artwork is generated from the data signal by skeletonizing it and applying morphological operators to a skeletal representation, such as a medial axis transform. Artistic effects are introduced by filtering the data signal with directional blurring or shape filters.

Artwork carrying machine readable data is generated by editing artwork according to a data signal or transforming the data signal into artwork. The machine-readable data signal is generated from a digital payload and converted into an image tile. Artwork is edited according to the image tile by moving graphic elements, adapting intersections of lines, or altering line density, among other techniques. Artwork is generated from the data signal by skeletonizing it and applying morphological operators to a skeletal representation, such as a medial axis transform. Artistic effects are introduced by filtering the data signal with directional blurring or shape filters.

Exemplary embodiments are directed to a system for selective enhancement of an object in an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more objects in the original image, generate a neural network mask of the original image for the one or more objects in the original image, apply one or more enhancements to the objects associated with the neural network mask, the one or more modules generating an enhanced image including the one or more enhancements to the objects, and generate a combined image, the combined image including the original image combined with the one or more enhancements to the objects of the enhanced image.

Exemplary embodiments are directed to a system for selective enhancement of an object in an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more objects in the original image, generate a neural network mask of the original image for the one or more objects in the original image, apply one or more enhancements to the objects associated with the neural network mask, the one or more modules generating an enhanced image including the one or more enhancements to the objects, and generate a combined image, the combined image including the original image combined with the one or more enhancements to the objects of the enhanced image.

There is provided a radiographic image processing apparatus including: an acquirer that acquires a dynamic image including a plurality of frame images captured by a radiographic imaging apparatus; a hardware processor that determines whether or not there is an abnormality in the dynamic image by using some of the frame images of the acquired dynamic image; and a notifier that notifies of a result of the determination by the hardware processor.

There is provided a radiographic image processing apparatus including: an acquirer that acquires a dynamic image including a plurality of frame images captured by a radiographic imaging apparatus; a hardware processor that determines whether or not there is an abnormality in the dynamic image by using some of the frame images of the acquired dynamic image; and a notifier that notifies of a result of the determination by the hardware processor.

The present invention discloses a method of separating, identifying and characterizing cracks in 3D space, which processes as follows to a volumetric image, so as to perform the separation, identification and the characterization of the cracks in the 3D space: 1) preprocessing digital image; 2) statistically analyzing basic information of the digital image: the basic information of the image includes porosity, connectivity of each pore, statistics of pore size, and position, size, orientation and anisotropy of each pore-structure; 3) filtration: removing non-crack structure in the image; 4) smoothening: smoothening and mending the image; 5) thinning: thinning the void structure into a thickness d (d can be any value, but more appropriate to be 2 to 3 voxels generally) in a direction with shortest extension in the 3D space; 6) separation: separating intersected cracks in a crack network by breaking the connections; 7) combination: combining those elongated cracks that are disconnected in the last step, merging tiny structures that are formed during the separation to a nearby large cluster, and restoring cracks to the thickness before thinning, and eventually giving out the characterization of the cracks. In the following expression, the wording “void” is used more, emphasizing the “empty” gap in the image rather than the rock solid. In this patent application, it is mainly for the case where the void appears in a state of crack, not excluding the case where the void appears in a state of small pore.

An analysis method for breast image and an electronic apparatus using the same are provided. The method includes the following steps. A breast image scanned by an ultrasound wave is obtained. Based on rectangular features of the breast image, a region of interest including an aberrant region in the breast image is obtained by applying a detection model. The aberrant region is further acquired from the region of interest, and a plurality of feature parameters of the aberrant region are extracted for a property analysis of the aberrant region.

In various examples, a background of an object may be modified to generate a training image. A segmentation mask may be generated and used to generate an object image that includes image data representing the object. The object image may be integrated into a different background and used for data augmentation in training a neural network. Data augmentation may also be performed using hue adjustment (e.g., of the object image) and/or rendering three-dimensional capture data that corresponds to the object from selected views. Inference scores may be analyzed to select a background for an image to be included in a training dataset. Backgrounds may be selected and training images may be added to a training dataset iteratively during training (e.g., between epochs). Additionally, early or late fusion nay be employed that uses object mask data to improve inferencing performed by a neural network trained using object mask data.