Abstract: Provided is a non-invasive system and method of determining pennation angle and/or fascicle length based on image processing. An ultrasound scan image is processed to facilitate distinguishing of muscle fiber and tendon. The processed ultrasound scan image is then analyzed. The pennation angle and/or fascicle length is determined based on the analysis. An example method includes receiving an ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the image provided by a plurality of pixels. The method continues by introducing noise into the pixels of the image and thresholding the pixels of the image to provide a binary image having a plurality of structural elements of different sizes. The method continues with morphing the structural elements of the binary image to remove small structural elements and connect large structural elements.

Abstract: Provided is a non-invasive system and method of determining muscle tissue size based on image processing. The method includes receiving at least one ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the image provided by a plurality of pixels. The method continues by introducing noise into the pixels of the image and thresholding the pixels of the image to provide a binary image having a plurality of structural elements of different sizes. The method continues with morphing the structural elements of the binary image to remove small structural elements and connect large structural elements. With this resulting image, the method distinguishes muscle tissue from remaining elements and determines the muscle tissue size. Associated apparatuses and computer program products are also disclosed.

Abstract: Provided is a non-invasive system and method for determining a fuel value for a target muscle and potentially at least one indicator muscle. The method includes receiving an ultrasound scan of a target muscle; evaluating at least a portion of the ultrasound scan to determine fuel value within the target muscle; recording the determined fuel value for the muscle as an element of a data set for the muscle; evaluating the fuel data set to determine a value range; and in response to the range being at least above a pre-determined threshold, establishing a target score for the muscle as based on an upper portion of the value range. The method may be repeated to identify ranges for a plurality of muscles, the muscle with the greatest range being identified as an indicator muscle. Based on these findings the muscles estimated fuel level, fuel rating and energy status may be determined. An associated system is also disclosed.

Abstract: Provided is a non-invasive system and method for determining a fuel value for a target muscle and potentially at least one indicator muscle. The method includes receiving an ultrasound scan of a target muscle; evaluating at least a portion of the ultrasound scan to determine fuel value within the target muscle; recording the determined fuel value for the muscle as an element of a data set for the muscle; evaluating the fuel data set to determine a value range; and in response to the range being at least above a pre-determined threshold, establishing a target score for the muscle as based on an upper portion of the value range. The method may be repeated to identify ranges for a plurality of muscles, the muscle with the greatest range being identified as an indicator muscle. Based on these findings the muscles estimated fuel level, fuel rating and energy status may be determined. An associated system is also disclosed.

Abstract: Provided is a non-invasive system and method of determining pennation angle and/or fascicle length based on image processing. An ultrasound scan image is processed to facilitate distinguishing of muscle fiber and tendon. The processed ultrasound scan image is then analyzed. The pennation angle and/or fascicle length is determined based on the analysis. An example method includes receiving an ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the image provided by a plurality of pixels. The method continues by introducing noise into the pixels of the image and thresholding the pixels of the image to provide a binary image having a plurality of structural elements of different sizes. The method continues with morphing the structural elements of the binary image to remove small structural elements and connect large structural elements.

Abstract: Provided is a non-invasive system and method of determining muscle tissue quality based on image processing. The non-invasive system and method includes determining muscle intramuscular fat content. The methods includes receiving at least one ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the skin layer defining a horizontal axis and the image provided by a plurality of pixels. The method continues by blurring the pixels of the image and thresholding the pixels of the image to provide an image having a plurality of structural elements of different sizes and gray scale. The method continues with morphing the structural elements of the image to remove small structural elements and connect large structural elements. With this resulting image, the method distinguishes muscle tissue from remaining elements. A ratio of black to white elements is evaluated to determine the muscle tissue quality or intramuscular fat content.

Abstract: Provided is a non-invasive system and method of determining muscle tissue size based on image processing. The method includes receiving at least one ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the image provided by a plurality of pixels. The method continues by introducing noise into the pixels of the image and thresholding the pixels of the image to provide a binary image having a plurality of structural elements of different sizes. The method continues with morphing the structural elements of the binary image to remove small structural elements and connect large structural elements. With this resulting image, the method distinguishes muscle tissue from remaining elements and determines the muscle tissue size. Associated apparatuses and computer program products are also disclosed.

Abstract: Provided is a non-invasive system and method for determining a target glycogen score value for a target muscle and potentially at least one indicator muscle. The method includes receiving an ultrasound scan of a target muscle; evaluating at least a portion of the ultrasound scan to determine glycogen store value within the target muscle; recording the determined glycogen store value for the muscle as an element of a glycogen value data set for the muscle; evaluating the glycogen value data set to determine a value range; and in response to the range being at least above a pre-determined threshold, establishing a target score for the muscle as based on an upper portion of the value range. The method may be repeated to identify ranges for a plurality of muscles, the muscle with the greatest range being identified as an indicator muscle. An associated system is also disclosed.

Abstract: Provided is a non-invasive system and method of determining human body fat based on image processing. The method includes receiving at least one ultrasound scan image of at least a portion of a skin layer as disposed above one or more additional tissue layers, the skin layer defining a horizontal axis and the image provided by a plurality of pixels. The method continues by horizontally blurring the pixels of the image and thresholding the pixels of the image to provide a binary image having a plurality of elements of different sizes. The method continues with morphing the structural elements of the binary image to remove small elements and connect large elements. With this resulting image, the method distinguishes a body fat layer from the remaining elements and determines the body fat layer thickness. An associated system is also disclosed.