Kenji SuzukiRolling Meadows, IL

6754380, Jun 22, 2004

Feb 14, 2003

10/366482

Kenji Suzuki - Clarendon Hills IL
Kunio Doi - Willowbrook IL

The University of Chicago - Chicago IL

G06K 962

382156, 382157, 382158

A method, system, and computer program product of selecting a set of training images for a massive training artificial neural network (MTANN). The method comprises selecting the set of training images from a set of domain images; training the MTANN with the set of training images; applying a plurality of images from the set of domain images to the trained MTANN to obtain a corresponding plurality of scores; and determining the set of training images based on the plurality of images, the corresponding plurality of scores, and the set of training images. The method is useful for the reduction of false positives in computerized detection of abnormalities in medical images. In particular, the MTAAN can be used for the detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network capable of operating on image data directly.

6819790, Nov 16, 2004

Apr 12, 2002

10/120420

Kenji Suzuki - Clarendon Hills IL
Kunio Doi - Willowbrook IL

The University of Chicago - Chicago IL

G06K 962

382156, 382157, 382130

A method of training an artificial neural network (ANN) involves receiving a likelihood distribution map as a teacher image, receiving a training image, moving a local window across sub-regions of the training image to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to the ANN so that it provides output pixel values that are compared to output pixel values of corresponding teacher image pixel values to determine an error, and training the ANN to reduce the error. A method of detecting a target structure in an image involves scanning a local window across sub-regions of the image by moving the local window for each sub-region so as to obtain respective sub-region pixel sets, inputting the sub-region pixel sets to an ANN so that it provides respective output pixel values that represent likelihoods that respective image pixels are part of a target structure, the output pixel values collectively constituting a likelihood distribution map. Another method for detecting a target structure involves training N parallel ANNs on either (A) a same target structure and N mutually different non-target structures, or (B) a same non-target structure and N mutually different target structures, the ANNs outputting N respective indications of whether the image includes a target structure or a non-target structure, and combining the N indications to form a combined indication of whether the image includes a target structure or a non-target structure. The invention provides related apparatus and computer program products storing executable instructions to perform the methods.

7545965, Jun 9, 2009

Nov 10, 2003

10/703617

Kenji Suzuki - Clarendon Hills IL, US
Kunio Doi - Willowbrook IL, US

The University of Chicago - Chicago IL

G06K 9/00

382128, 382155

A method, system, and computer program product for modifying an appearance of an anatomical structure in a medical image, e. g. , rib suppression in a chest radiograph. The method includes: acquiring, using a first imaging modality, a first medical image that includes the anatomical structure; applying the first medical image to a trained image processing device to obtain a second medical image, corresponding to the first medical image, in which the appearance of the anatomical structure is modified; and outputting the second medical image. Further, the image processing device is trained using plural teacher images obtained from a second imaging modality that is different from the first imaging modality. In one embodiment, the method also includes processing the first medical image to obtain plural processed images, wherein each of the plural processed images has a corresponding image resolution; applying the plural processed images to respective multi-training artificial neural networks (MTANNs) to obtain plural output images, wherein each MTANN is trained to detect the anatomical structure at one of the corresponding image resolutions; and combining the plural output images to obtain a second medical image in which the appearance of the anatomical structure is enhanced.

2006001, Jan 26, 2006

Jul 15, 2005

11/181884

Kenji Suzuki - Clarendon Hills IL, US
Kunio Doi - Willowbrook IL, US

UC Tech - Chicago IL

G06K 9/00

382128000

A system, method, and computer program product for classifying a target structure in an image into abnormality types. The system has a scanning mechanism that scans a local window across sub-regions of the target structure by moving the local window across the image to obtain sub-region pixel sets. A mechanism inputs the sub-region pixel sets into a classifier to provide output pixel values based on the sub-region pixel sets, each output pixel value representing a likelihood that respective image pixels have a predetermined abnormality, the output pixel values collectively determining a likelihood distribution output image map. A mechanism scores the likelihood distribution map to classify the target structure into abnormality types. The classifier can be, e.g., a single-output or multiple-output massive training artificial neural network (MTANN).

2019008, Mar 21, 2019

Mar 3, 2016

16/080773

- Schaumburg IL, US
Kenji Suzuki - West Chicago IL, US

C09D 11/101
C09D 11/107
C09D 11/322

Disclosed is an electron beam curable inkjet ink composition, including in 100 parts by mass thereof the following components a to c: a. 10 to 70 parts by mass of a monofunctional polymerizable compound having a weight average molecular weight of 100 to 400 and having a hydroxyl group; b. 10 to 75 parts by mass of a A functional polymerizable compound; and c. 0.1 to 10 parts by mass of an unreactive resin, wherein the electron beam curable inkjet ink composition does not include any of an organophosphorus compound, a polymerization initiator and a sensitizer, and has a viscosity of 30 mPa-s or less.