Samsung Electronics Co., Ltd.

Seoul National University

Samsung Medison Co., Ltd.

Samsung Life and Welfare Foundation

Yeong-Kyeong Seong

Moon-ho Park

Ye-hoon Kim

Moon-Ho Park

Baek-Hwan Cho

Jong-Ha Lee

Chu-Ho Chang

Jung-Hoe Kim

Ki-Yong Lee

Jae-Cheol Lee

Kyoung-Gu Woo

Sin-Sang Yu

Won-hee Choe

Min Woo Lee

Yoon-Hee Choi

Tae-hee Kim

Ki-Young Lee

Hyun-Ki Baik

Dong-Woo Im

Joo-Hyuk Jeon

Myung-Eun Lee

Jong-Hyo Kim

Won-sik Kim

Hyeong-Min Jin

Boo-kyung Han

Won-Sik Kim

Oh-Young Jang

Disclosed is a user interface which enables mark based interaction for images. The present disclosure relates to a user interface which enables mark based interaction for images, the images comprising a volume which is a three-dimensional image and slices which are two-dimensional images, each of which represents a cross section of the volume. At least two of the images each include the same visual mark for identifying at least one common region of interest. The user interface comprises: an input unit for receiving a user input associated with the same visual mark included in one of the images; and at least one component for enabling the interaction for the images including the same visual mark associated with the user input.

User interface system and method for enabling mark-based interaction for images

Disclosed are an apparatus and a method for determining lesion similarity of a medical image. The apparatus for determining lesion similarity according to one aspect of the present invention may comprise: an image input unit for receiving a reference image comprising a reference lesion area, and a target image comprising a target lesion area; and a similarity determination unit for determining similarity of the reference lesion area and the target lesion area by applying an advantage weight, which increases as getting closer to the center of the reference lesion area, to pixels included in a first area of the reference lesion area, and a penalty weight, which increases as getting farther away from the reference lesion area, to pixels included in a second area of the target lesion area.

Apparatus and method for determining lesion similarity of medical image

Provided is a medical imaging apparatus including: an image processor configured to extract properties that an object has with respect to at least one feature, based on a plurality of medical images of the object; and a controller configured to control a display to display a first medical image from among the plurality of medical images and the extracted properties and display a first property shown in the first medical image from among the properties and a second property not shown in the first medical image in such a manner that the first property and the second property are distinguished from each other.

Medical imaging apparatus and method of operating same

An apparatus and method for medical diagnostics includes receiving three-dimensional (3D) volume data of a part of a patient's body, and generating two-dimensional (2D) slices including cross-sections of the 3D volume data cut from a cross-section cutting direction. The apparatus and the method also determine whether a lesion in each of the 2D slices is benign or malignant and output results indicative thereof, select a number of the 2D slices based on the results, and make a final determination whether the lesion is benign or malignant based on the selected 2D slices.

Apparatus and method for computer-aided diagnosis

A diagnosis aiding apparatus and method to provide diagnosis information is provided. The diagnosis aiding apparatus includes an information display unit configured to display a reference cross-section of an image, display diagnosis information belonging to the reference cross-section, and display diagnosis information corresponding to a different cross-section within the reference cross-section displayed.

Diagnosis aiding apparatus and method to provide diagnosis information and diagnosis system thereof

An apparatus for detecting an error in a contour of a lesion includes an extracting unit configured to extract a contour of a lesion in each of a plurality of two-dimensional image frames that form a three-dimensional image, and an error determining unit configured to determine a presence or an absence of an error in a contour of a lesion in a target image frame of the two-dimensional image frames based on estimation information about the lesion in the target image frame and/or an energy value that corresponds to the contour of the lesion in the target image frame.

Apparatus and method for detecting error in lesion contour, apparatus and method for correcting error in lesion contour, and apparatus for inspecting error in lesion contour

An apparatus and a method for supporting acquisition of a multi-parametric image are provided. An apparatus for supporting acquisition of a multi-parametric image includes: a disease selector configured to select a suspected disease of a patient based on patient information; and an image selector configured to determine a set of imaging conditions of a multi-parametric magnetic resonance image corresponding to the suspected disease based on a multi-parametric magnetic resonance imaging model.

Apparatus and method for supporting acquisition of multi-parametric images

A lesion diagnosis apparatus and a lesion diagnosis method are provided. A lesion-surrounding area determination unit is configured to determine an existence of a lesion-surrounding area from continuous medical images. A feedback provision unit is configured to generate feedback information about a presence of a lesion in the lesion-surrounding area.

Lesion diagnosis apparatus and method

A computer-aided diagnosis (CAD) method includes extracting a lesion feature value of a lesion feature of a lesion from a captured lesion image; receiving additional information; and diagnosing the lesion based on a combination of the extracted lesion feature value and the received additional information.

Computer-aided diagnosis method and apparatus

An apparatus for detecting a lesion is provided. The apparatus includes an extracting unit configured to extract at least one tissue region from an image of tissue regions, a setting unit configured to set at least one of the at least one extracted tissue region as a lesion detection candidate region, and a detecting unit configured to detect a lesion from the lesion detection candidate region.

Apparatus and method for detecting lesion and lesion diagnosis apparatus

An apparatus and method are provided including a first segmenter and a second segmenter. The first segmenter is configured to generate a first segmentation result from a medical image using a first segmentation parameter for a candidate lesion. The second segmenter is configured to determine a target lesion to segment from among the candidate lesion based on the first segmentation result, and generate a second segmentation result using a second segmentation parameter to segment the target lesion.

Apparatus and method for lesion segmentation and detection in medical images

An apparatus and a method for diagnosis are provided. The apparatus for diagnosis lesion include: a model generation unit configured to categorize learning data into one or more categories and to generate one or more categorized diagnostic models based on the categorized learning data, a model selection unit configured to select one or more diagnostic model for diagnosing a lesion from the categorized diagnostic models, and a diagnosis unit configured to diagnose the lesion based on image data of the lesion and the selected one or more diagnostic model.

Apparatus and method of diagnosing a lesion using image data and diagnostic models

An apparatus and a method for combining three-dimensional ultrasound images are provide. The method involves obtaining a plurality of three-dimensional ultrasound image data that corresponds to a Region of Interest (ROI); detecting one or more landmarks, using a parameter for detection; outputting the detection result and receiving a response from a user; registering each of one or more selected landmarks as link information according to a received response from the user; and generating a combined three-dimensional ultrasound image by combining at least two pieces of three-dimensional ultrasound image data using at least one of the one or more selected landmarks registered as the link information, wherein the at least two pieces of three-dimensional ultrasound image data commonly comprise the at least one of the one or more selected landmarks.

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Yongin-si, South Korea

Yeong-Kyeong Seong