Abstract: The embodiments of the application provide a backlight module and a displaying device, relating to the technical field of display. The backlight module comprises a first support structure, an optical film material and a buffer structure; the first support structure is arranged on a side away from a light-outgoing side of the optical film material, and the first support structure and the optical film material have a through-hole; the buffer structure comprises a first buffer portion arranged in the through-hole, and a rigidity of the first buffer portion is less than a rigidity of the first support structure. The backlight module is internally provided with the buffer structure, and the first buffer portion in the buffer structure is arranged in the through-hole that penetrates through the first support structure and the optical film material.

Abstract: A system, method, software arrangement and computer-accessible medium for performing electronic cleansing of CT colonography images are provided. In this system, method, software arrangement and computer-accessible medium, the digital bowel cleansing can be performed to remove tagged bowel contents from the images. The digital bowel cleansing can apply a local shape analysis throughout the images and use a shape-based speed function to detect the folds and polyps structures while removing the tagged bowel contents region. The system, method, software arrangement and computer-accessible medium can enable a medical examiner to perform an accurate virtual colonoscopy on a patient, without the need for thorough patient preparation.

Abstract: Imaging systems and methods for viewing medical images of human anatomy and, in particular, to a 3-dimensional imaging system that allows a user to efficiently and accurately detect and view coronary artery calcification as displayed graphically on a computer screen. In one aspect, a method for displaying medical images comprises obtaining an image dataset comprising anatomical image data (step 50), automatically grouping connected components in the image data to form groups of connected components (steps 50-57), and displaying the groups of connected components are distinguishable in the displayed image (58-59). The image dataset may comprise a volume data set and the groups of connected components comprise regions of neighboring voxels that share a similar property. The image dataset may comprise a 2-dimensional data set and the groups of connected components comprise regions of neighboring pixels that share a similar property.

Abstract: An adaptive density correction (ADC) method and system automatically compensate for pseudo-enhancement (PEH) of voxels in computed tomography (CT) data, such as in fecal-tagged CT colonography (ftCTC), so air (or another low-contrast background) and soft tissues are represented by their usual CT attenuations. ADC estimates an amount of pseudo-enhancement energy that was received by voxels that are near tagged voxels (i.e., voxels that are tagged with a high-contrast agent), based on a first distribution scheme, such as a Gaussian distribution. ADC then iteratively distributes PEH energy received by voxels to neighboring voxels, according to another distribution scheme, which may be another Gaussian function. ADC then subtracts the total amount of PEH energy at each voxel from the CT data of the voxel.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: An adaptive density mapping (ADM) method and system automatically identify interface regions between air and material tagged with contrast agents in computed tomographic (CT) image data, then map CT attenuations of voxels outside the identified interface regions, such that voxels that represent tagged material are made to represent air or another gas.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: A system, method, software arrangement and computer-accessible medium for performing electronic cleansing of CT colonography images are provided. In this system, method, software arrangement and computer-accessible medium, the digital bowel cleansing can be performed to remove tagged bowel contents from the images. The digital bowel cleansing can apply a local shape analysis throughout the images and use a shape-based speed function to detect the folds and polyps structures while removing the tagged bowel contents region. The system, method, software arrangement and computer-accessible medium can enable a medical examiner to perform an accurate virtual colonoscopy on a patient, without the need for thorough patient preparation.

Abstract: A dynamic thresholding level set method combines two optimization processes, i.e., a level set segmentation and an optimal threshold calculation in a local histogram, into one process that involves a structure called a “propagating shell.” The propagating shell is a mobile 3-dimensional shell structure with a thickness that encompasses the boundary of an object, the boundary between two objects or the boundary between an object and a background. Because the local optimal threshold tends to shift to a value of a small region in a histogram, the shift can drive the propagating shell to an object boundary by pushing or pulling the propagating shell. The segmentation process is an optimizing process to find a balanced histogram with minimal threshold shift. When the histogram in the propagating shell is balanced, the optimal threshold becomes stable, and the propagating shell reaches a convergence location, i.e., an object boundary. This method can be applied to computer-aided organ and tumor volumetrics.

Abstract: Methods are provided for optimizing a vessel centerline in a digital image. For instance, a method includes providing a digital image of a vessel wherein said image comprises a plurality of intensities corresponding to a domain of points in a D-dimensional space, initializing a centerline comprising a plurality of points in the vessel (step 20), determining a cross section of the vessel at each point in the centerline (step 21), evaluating a center point for each cross section of the vessel (step 22), and determining a refined centerline from the center points of each cross section (step 23).

Abstract: An imaging system for automated segmentation and visualization of medical images (100) includes an image processing module (107) for automatically processing image data using a set of directives (109) to identify a target object in the image data and process the image data according to a specified protocol, a rendering module (105) for automatically generating one or more images of the target object based on one or more of the directives (109) and a digital archive (110) for storing the one or more generated images. The image data may be DICOM-formatted image data (103), wherein the imaging processing module (107) extracts and processes meta-data in DICOM fields of the image data to identify the target object. The image processing module (107) directs a segmentation module (108) to segment the target object using processing parameters specified by one or more of the directives (109).

Abstract: A method for visualizing a vascular structure includes obtaining an image dataset (step 20), selecting a vascular central axis (VCA) and a vector of interest (VOI) (step 21), forming a plurality of cross sections perpendicular to the vascular central axis, forming a convex hull to enclose each cross section (step 22), wherein the convex hull is oriented by the vector of interest and determined by the shape of the cross section, and connecting each convex hull to form a biconvex slab (step 23). The biconvex slab comprises two curved surfaces that enclose a 3D volume including the vascular structure 21 of interest. The volume within the biconvex slab can rendered to obtain a 3D view of the entire vascular structure (step 24). Since the biconvex slab is a 3D volume, volume rendering techniques can be used to render the 3D information and generate a resulting image of the vascular structure in a flattened plane having precise 3D spatial information.

Abstract: A lumen tracking method and system automatically extracts a colon from CT image data by locating landmarks in the image data, based on known anatomic features or other predictable features. If the colon is segmented, the method and system may use the landmarks to evaluate candidate segments for inclusion in the extracted colon.

Abstract: An adaptive density correction (ADC) method and system automatically compensate for pseudo-enhancement (PEH) of voxels in computed tomography (CT) data, such as in fecal-tagged CT colonography (ftCTC), so air (or another low-contrast background) and soft tissues are represented by their usual CT attenuations. ADC estimates an amount of pseudo-enhancement energy that was received by voxels that are near tagged voxels (i.e., voxels that are tagged with a high-contrast agent), based on a first distribution scheme, such as a Gaussian distribution. ADC then iteratively distributes PEH energy received by voxels to neighboring voxels, according to another distribution scheme, which may be another Gaussian function. ADC then subtracts the total amount of PEH energy at each voxel from the CT data of the voxel.

Abstract: An adaptive density mapping (ADM) method and system automatically identify interface regions between air and material tagged with contrast agents in computed tomographic (CT) image data, then map CT attenuations of voxels outside the identified interface regions, such that voxels that represent tagged material are made to represent air or another gas.

Abstract: A system (100) and corresponding method for vessel segmentation are provided, the system having an adapter (112, 128, 130) for receiving image data, a processor (102) in signal communication with the input adapter, a pre-processing unit (170) in signal communication with the processor for pre-processing the received image data, and a vessel segmentation unit (180) in signal communication with the processor for segmenting vessels using pre-processed data; and the corresponding method including receiving image data, pre-processing the received data, and segmenting vessels responsive to the pre-processed data.

Abstract: A system (100) and corresponding method for vessel visualization are provided, the system having an input adapter (112, 128, 130) for receiving segmented vessel data, a processor (102) in signal communication with the input adapter, a vessel visualization unit (170) in signal communication with the processor for visualizing the vessel, and a calcium cleansing unit (180) in signal communication with the processor for removing the influences of calcium deposits from the visualized vessel; and the corresponding method including receiving segmented vessel data, visualizing a vessel in correspondence with the segmented vessel data, and cleansing calcium by removing the influences of calcium deposits from the visualized vessel.