Patent application number | Description | Published |
20110237929 | BLADDER WALL THICKNESS MAPPING FOR TUMOR DETECTION - Disclosed is a method and apparatus for detection of a bladder wall tumor. Layers of a bladder wall are created by magnetic resonance imaging. A group of voxels having a lowest intensity is identified in a layer and an energy function modification enlarges the layer of the bladder wall. A partial volume image segmentation obtains tissue type mixture percentages in each voxel near inner and outer borders of the bladder wall in the layer of the bladder wall to obtain a bladder wall thickness. A range of uncertainty at the inner and outer borders of the bladder wall is obtained, and integration is performed of the bladder wall thickness along a path starting at a point on the outer border and ending at a corresponding point on the inner border. | 09-29-2011 |
20120033861 | SYSTEMS AND METHODS FOR DIGITAL IMAGE ANALYSIS - Systems and methods for implementing a hierarchical image recognition framework for classifying digital images are provided. The provided hierarchical image recognition framework utilizes a multi-layer approach to model training and image classification tasks. A first layer of the hierarchical image recognition framework generates first layer confidence scores, which are utilized by the second layer to produce a final recognition score. The provided hierarchical image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-layer image recognition frameworks. In some embodiments real-time operator guidance is provided for an image classification task. | 02-09-2012 |
20120033862 | SYSTEMS AND METHODS FOR SEGMENTING DIGITAL IMAGES - Methods and systems disclosed herein provide the capability to automatically process digital pathology images quickly and accurately. According to one embodiment, an digital pathology image segmentation task may be divided into at least two parts. An image segmentation task may be carried out utilizing both bottom-up analysis to capture local definition of features and top-down analysis to use global information to eliminate false positives. In some embodiments, an image segmentation task is carried out using a “pseudo-bootstrapping” iterative technique to produce superior segmentation results. In some embodiments, the superior segmentation results produced by the pseudo-bootstrapping method are used as input in a second segmentation task that uses a combination of bottom-up and top-down analysis. | 02-09-2012 |
20120087556 | DIGITAL IMAGE ANALYSIS USING MULTI-STEP ANALYSIS - Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks. | 04-12-2012 |
20120093396 | DIGITAL IMAGE ANALYSIS UTILIZING MULTIPLE HUMAN LABELS - Systems and methods for implementing a multi-label image recognition framework for classifying digital images are provided. The provided multi-label image recognition framework utilizes an iterative, multiple analysis path approach to model training and image classification tasks. A first iteration of the multi-label image recognition framework generates confidence maps for each label, which are shared by the multiple analysis paths to update the confidence maps in subsequent iterations. The provided multi-label image recognition framework permits model training and image classification tasks to be performed more accurately than conventional single-label image recognition frameworks. | 04-19-2012 |
20120128237 | SUPERPIXEL-BOOSTED TOP-DOWN IMAGE RECOGNITION METHODS AND SYSTEMS - Systems and methods for implementing a superpixel boosted top-down image recognition framework are provided. The framework utilizes superpixels comprising contiguous pixel regions sharing similar characteristics. Feature extraction methods described herein provide non-redundant image feature vectors for classification model building. The provided framework differentiates a digitized image into a plurality of superpixels. The digitized image is characterized through image feature extraction methods based on the plurality of superpixels. Image classification models are generated from the extracted image features and ground truth labels and may then be used to classify other digitized images. | 05-24-2012 |
20120275703 | SUPERPIXEL SEGMENTATION METHODS AND SYSTEMS - Systems and methods for implementing a superpixel image segmentation technique using a boundary preserving distance metric are disclosed. The disclosed technique segments a digital image into superpixels comprising contiguous pixel regions sharing similar characteristics. Superpixel image segmentation techniques presented herein utilize a boundary preserving distance metric. A boundary preserving distance metric presented herein measures the similarity between two pixels of a digital image at least partially based on a boundary probability values of the two pixels and surrounding pixels. | 11-01-2012 |
20130243277 | FLOURESCENT DOT COUNTING IN DIGITAL PATHOLOGY IMAGES - Fluorescence in situ hybridization (FISH) enables the detection of specific DNA sequences in cell chromosomes by the use of selective staining. Due to the high sensitivity, FISH allows the use of multiple colors to detect multiple targets simultaneously. The target signals are represented as colored dots, and enumeration of these signals is called dot counting. Using a two-stage segmentation framework guarantees locating all potential dots including overlapped dots. | 09-19-2013 |
20130243289 | GRAPH CUTS-BASED INTERACTIVE SEGMENTATION OF TEETH IN 3-D CT VOLUMETRIC DATA - An interactive segmentation framework for 3-D teeth CT volumetric data enables a user to segment an entire dental region or individual teeth depending upon the types of user input. Graph cuts-based interactive segmentation utilizes a user's scribbles which are collected on several 2-D representative CT slices and are expanded on those slices. Then, a 3-D distance transform is applied to the entire CT volume based on the expanded scribbles. Bony tissue enhancement is added before feeding 3-D CT raw image data into the graph cuts pipeline. The segmented teeth area is able to be directly utilized to reconstruct a 3-D virtual teeth model. | 09-19-2013 |
20130243308 | INTEGRATED INTERACTIVE SEGMENTATION WITH SPATIAL CONSTRAINT FOR DIGITAL IMAGE ANALYSIS - An integrated interactive segmentation with spatial constraint method utilizes a combination of several of the most popular online learning algorithms into one and implements a spatial constraint which defines a valid mask local to the user's given marks. Additionally, both supervised learning and statistical analysis are integrated, which are able to compensate each other. Once prediction and activation are obtained, pixel-wised multiplication is conducted to fully indicate how likely each pixel belongs to the foreground or background. | 09-19-2013 |
20140270431 | CHARACTERIZING PATHOLOGY IMAGES WITH STATISTICAL ANALYSIS OF LOCAL NEURAL NETWORK RESPONSES - For digital pathology imaging, intelligent processing, such as automatic recognition or content-based retrieval, is one significant benefit that drives the wide application of this technology. Before any intelligent processing on pathology images, every image is converted into a feature vector which quantitatively capture its visual characteristics. An algorithm characterizing pathology images with statistical analysis of local responses of neural networks is described herein. The algorithm framework enables extracting sophisticated textural features that are well adapted to the image data of interest. | 09-18-2014 |
20140270432 | COMBINING INFORMATION OF DIFFERENT LEVELS FOR CONTENT-BASED RETRIEVAL OF DIGITAL PATHOLOGY IMAGES - Content-based retrieval of digital pathology images (DPI) is a fundamental component in an intelligent DPI processing and management system. One key issue of content-based DPI retrieval is how to represent an image as a feature vector, capturing its key information that is most relevant to the goal of retrieval. A unified framework of extracting information of different levels for DPI, namely low level color information, middle level texture information and high level diagnostic information is described herein. Such information from all the levels are integrated to the end of content-based DPI retrieval. | 09-18-2014 |
20140270496 | DISCRIMINATIVE DISTANCE WEIGHTING FOR CONTENT-BASED RETRIEVAL OF DIGITAL PATHOLOGY IMAGES - Content-based retrieval of digital pathology images (DPI) is a fundamental component in an intelligent DPI processing and management system. The fundamental procedure of the retrieval is evaluating the similarity between the query image and every image in the database with some distance function, and sorting of the latter based on their distances to the query. A novel approach to optimally combine a set of existing distance functions into a stronger distance that is suitable for retrieving DPI in a way respecting human perception of image similarity is described herein. | 09-18-2014 |
20140279755 | MANIFOLD-AWARE RANKING KERNEL FOR INFORMATION RETRIEVAL - A manifold-aware ranking kernel (MARK) for information retrieval is described herein. The MARK is implemented by using supervised and unsupervised learning. MARK is ranking-oriented such that the relative comparison formulation directly targets on the ranking problem, making the approach optimal for information retrieval. MARK is also manifold-aware such that the algorithm is able to exploit information from ample unlabeled data, which helps to improve generalization performance, particularly when there are limited number of labeled constraints. MARK is nonlinear: as a kernel-based approach, the algorithm is able to lead to a highly non-linear metric which is able to model complicated data distribution. | 09-18-2014 |