Taihao Medical Inc.

Hong-Hao Chen

Hsin-Hung Lai

Rong-Tai Chen

Ruey-Feng Chang

Jen-Feng Hsu

You-Wei Wang

Wei-Han Teng

Yuan-Yen Chang

Tsung-Chen Chiang

An analysis method and an electronic apparatus for breast image are provided. The method includes the following steps. One or more breast ultrasound images are obtained. The breast ultrasound images are used for forming a three-dimensional (3D) breast model. A volume of interest (VOI) in the breast ultrasound image is obtained by applying a detection model on the 3D breast model. The VOI is compared with a tissue segmentation result. The VOI is determined as a false positive according to a compared result between the VOI and the tissue segmentation result. The compared result includes that the VOI is located at a glandular tissue based on the tissue segmentation result. In response to the VOI being located in the glandular tissue of the tissue segmentation result, the VOI is compared with the lactiferous duct in the 3D breast model.

Analysis method for breast image and electronic apparatus using the same

A system and a method for cloud medical image analysis are provided. The system for cloud medical image analysis has a cloud medical analysis platform and an electronic device. The electronic device obtains a medical image. The electronic device quantifies the medical image to obtain a first feature value. The electronic device sends the first feature value to the cloud medical analysis platform. The cloud medical analysis platform inputs the first feature value into an analysis module to obtain an analysis result, wherein the analysis module uses a self-learning module and the analysis module is trained via a plurality of training images. The cloud medical analysis platform sends the analysis result to the electronic device.

System and method for cloud medical image analysis using self-learning model

A focus detection apparatus and a method thereof are provided. In the method, a medical image is obtained. Size of a target focus and a sliding window are determined, and side length of the sliding windows is at least twice the side length of the target focus. The medical image is scanned through the sliding window, and a stride which the sliding windows moves each time is not greater than the side length of the target focus. At least one area of interest is obtained based on the scan result. Then, the area of interest is identified based on machine learning techniques, and perform candidate aggregation and multiple size aggregation to determine at least one focus position. Accordingly, the computational time and the detection accuracy can be improved.

Focus detection apparatus and method thereof

A medical image processing apparatus and a breast image processing method thereof are provided. The processing method at least contains but not limited to the following steps. At least one slice of breast image is obtained. Mammary glandular tissue in each breast image is detected through a mammary glandular tissue detector. The mammary glandular tissue detector is based on texture characteristic analysis. Therefore, the embodiments of the present disclosure would assist density analysis of the mammary glandular tissue and efficiently reduce false positive of computer-aided detection. In addition, based on a result of the density analysis of the mammary glandular tissue, the embodiment would further determine lactation yield and present density diagrams of mammary glandular tissue of left and right breasts. A breast region may also be separated from the breast image based on rib information according to the embodiments of the present disclosure.

Medical image processing apparatus and breast image processing method thereof

A lesion detecting method and a lesion detecting apparatus for breast image in a rotating manner are provided. In the method, a set of breast image in the rotating manner is obtained. The set of breast image in the rotating manner contains sub breast images. The sub breast image is reconstructed, to generate a reconstructed breast image. The reconstructed breast image is compared with the set of breast image in the rotating manner without being reconstructed. Accordingly, at least one lesion position would be confirmed according to the comparing result. Therefore, viewing a three-dimensional breast image would be convenient for medical staff, and false positive of detecting lesion would be reduced.

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Hsinchu, Taiwan

Hong-Hao Chen