Abstract: The present invention disclose a medical image-based system for predicting lesion classification and a method thereof. The system comprises a feature data extracting module for providing a raw feature data based on a medical image, and a predicting module for outputting a predicted class and a risk index according to the raw feature data. The predicting module comprises a classification unit for generating the predicted class and a prediction score corresponding thereto according to the raw feature data, and a risk evaluation unit for generating the risk index according to the prediction score. The system provides medical personnels a reference score and a risk index to determine progression of a certain disease.

Abstract: An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.

Abstract: The present invention relates to a method for measuring muscle mass, including: a first selection step, wherein a frame selection information is obtained by using a frame to select a fascia region from a provided computed tomography image under the condition that the window width ranges from 300 HU to 500 HU and the window level ranges from 40 HU to 50 HU, wherein the selected range of the fascia region includes a muscle; and a second selection step, wherein a muscle information of the muscle is obtained by calculating a pixel value in the frame-selected fascia region under the condition that the HU value of the CT image ranges from ?29 HU to 150 HU.

Abstract: A sensor device for high speed rotating machine includes a rotating body and a sensor assembly. The rotating body includes a magnetic permeable ring and at least one positioning structure. The magnetic permeable ring includes a radial displacement sensing area and a rotational speed sensing area, and the positioning feature is arranged in the rotational speed sensing area. The sensor assembly includes four radial displacement sensors and two rotational speed sensors. The radial displacement sensor is a double-probe type sensor, and the speed sensor is a double-probe or four-probe type sensor. Through different types of the rotational speed sensors and the radial displacement sensors, the rotation speed and turning direction of the rotating body can be calculated.

Abstract: An interactive image marking method is introduced. The interactive image marking method includes the following steps, displaying a target image and at least one marked region in the target image; receiving an interactive signal, where the interactive signal corresponds to a first pixel of the target image; calculating a correlation between the first pixel and pixels of the target image, and determining a correlation range in the target image according to the correlation; editing the marked region according to the correlate range; and displaying the edited marked region. In addition, an electronic device and a recording medium using the method are also introduced.

Abstract: The present invention relates to a method for measuring muscle mass, including: a first selection step, wherein a frame selection information is obtained by using a frame to select a fascia region from a provided computed tomography image under the condition that the window width ranges from 300 HU to 500 HU and the window level ranges from 40 HU to 50 HU, wherein the selected range of the fascia region includes a muscle; and a second selection step, wherein a muscle information of the muscle is obtained by calculating a pixel value in the frame-selected fascia region under the condition that the HU value of the CT image ranges from -29 HU to 150 HU.

Abstract: A sensor device for high speed rotating machine includes a rotating body and a sensor assembly. The rotating body includes a magnetic permeable ring and at least one positioning structure. The magnetic permeable ring includes a radial displacement sensing area and a rotational speed sensing area, and the positioning feature is arranged in the rotational speed sensing area. The sensor assembly includes four radial displacement sensors and two rotational speed sensors. The radial displacement sensor is a double-probe type sensor, and the speed sensor is a double-probe or four-probe type sensor. Through different types of the rotational speed sensors and the radial displacement sensors, the rotation speed and turning direction of the rotating body can be calculated.

Abstract: An interactive image marking method is introduced. The interactive image marking method includes the following steps, displaying a target image and at least one marked region in the target image; receiving an interactive signal, where the interactive signal corresponds to a first pixel of the target image; calculating a correlation between the first pixel and pixels of the target image, and determining a correlation range in the target image according to the correlation; editing the marked region according to the correlate range; and displaying the edited marked region. In addition, an electronic device and a recording medium using the method are also introduced.

Abstract: An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.

Abstract: A rotor driving system has a rotor, a revolution sensor, a sensing circuit, a controller, and a rotor driver. The revolution sensor is configured to sense a revolution frequency of the rotor so as to generate a measurement signal. The sensing circuit is electrically connected to the revolution sensor and configured to convert the measurement signal into a current revolution measured value. The controller is electrically connected to the sensing circuit and configured to generate an estimated revolution frequency based on a historical revolution measured value, the current revolution measured value, and a reference value, and generate a rotor driving signal based on the estimated revolution frequency and a revolution control signal. The rotor driver is electrically connected to the controller and configured to drive the rotor to rotate based on the rotor driving signal.

Abstract: A measurement device is provided. The measurement device comprises a ring-shaped base and multiple sensing units and a plurality of coils. The sensing units are disposed on and protruded from the inner circumferential surface of the ring-shaped base. Each sensing unit comprises a circumferential groove and an axial groove. The axial groove is connected to the circumferential groove. Each coil is surrounded within the circumferential groove and extended along the axial groove.

Abstract: A measurement device is provided. The measurement device comprises a ring-shaped base and multiple sensing units and a plurality of coils. The sensing units are disposed on and protruded from the inner circumferential surface of the ring-shaped base. Each sensing unit comprises a circumferential groove and an axial groove. The axial groove is connected to the circumferential groove. Each coil is surrounded within the circumferential groove and extended along the axial groove.