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.

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.

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.

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.