Component-Based Approach For Fast Left Ventricle Detection
A method for estimating a configuration of an internal structure within a medical image includes detecting a location of the internal structure. Component-based identification is performed within the detected location of the internal structure to identify a plurality of components. The configuration of the internal structure is estimated based on the relative position of the identified components.
1 . A method for estimating a configuration of an internal structure within a medical image, comprising:
detecting a location of the internal structure;
performing component-based identification within the detected location of the internal structure to identify a plurality of components; and
estimating the configuration of the internal structure based on the relative position of the identified components.
2 . The method of claim 1 , wherein the medical image is an echocardiograph.
3 . The method of claim 1 , wherein the internal structure is a left ventricle of a heart.
4 . The method of claim 1 , wherein the plurality of components include an apex or a valve annulus.
5 . The method of claim 1 , wherein the configuration of the internal structure includes the orientation of the Internal structure.
6 . The method of claim 1 , wherein performing component-based identification within the detected location of the internal structure to identify a plurality of components includes the use of rotation-invariant detectors.
7 . The method of claim 1 , wherein performing component-based identification within the detected location of the internal structure to identify a plurality of components includes the use of detectors trained by Adaboost cascade technique.
8 . The method of claim 1 , wherein performing component-based identification within the detected location of the internal structure to identify a plurality of components includes performing scale-invariant feature transforms (STFT) or calculating histograms of oriented gradients.
9 . The method of claim 1 , additionally including a training step for learning to discriminate the plurality of components from the medical image based on a set of training data.
10 . The method of claim 10 , wherein the training data includes rotation variations.
11 . The method of claim 1 , additionally comprising:
estimating covariance matrices based on the identified plurality of components; and
modeling detection uncertainty based on the estimated covariance matrices.
12 . The method of claim 1 , wherein performing component-based identification within the detected location of the internal structure to identify a plurality of components includes:
estimating a plurality of modes on a detection map;
finding the center of each of the plurality of modes;
determining a detection area corresponding to each of the plurality of modes, wherein the detection area is a partial fan area substantially originating from the center of the respective mode; and
locating a component of the plurality of components within each of the detection areas.
13 . A method for estimating a configuration of a left ventricle within an echocardiograph, comprising:
estimating a location of the left ventricle within the echocardiograph;
identifying a plurality of components within the detected location of the left ventricle using rotation-invariant detectors; and
estimating the configuration of the left ventricle based on the identified components.
14 . The method of claim 13 , additionally comprising a training step for learning to discriminate the plurality of components from within the location of the left ventricle based on a set of training data.
15 . The method of claim 14 , wherein the training data includes rotation variations.
16 . The method of claim 13 , additionally comprising:
estimating covariance matrices based on the identified plurality of components; and
modeling detection uncertainty based on the estimated covariance matrices.
17 . The method of claim 13 , wherein performing component-based identification within the detected location of the internal structure to identify a plurality of components includes:
estimating a plurality of modes on a detection map;
finding the center of each of the plurality of modes;
determining a detection area corresponding to each of the plurality of modes, wherein the detection area is a partial fan area substantially originating from the center of the respective mode; and
locating a component of the plurality of components within each of the detection areas.
18 . A computer system comprising:
a processor; and
a program storage device readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for estimating a configuration of a left ventricle within an echocardiograph, the method comprising:
estimating a location of the left ventricle within the echocardiograph;
identifying a plurality of components within the detected location of the left ventricle; and
estimating the configuration of the left ventricle based on the identified components.
19 . The computer system of claim 17 , wherein the method additionally comprises a training step for learning to discriminate the plurality of components from within the location of the left ventricle based on a set of training data.
20 . The computer system of claim 17 , wherein performing component-based identification within the detected location of the internal structure to identify a plurality of components includes:
estimating a plurality of modes on a detection map;
finding the center of each of the plurality of modes;
determining a detection area corresponding to each of the plurality of modes, wherein the detection area is a partial fan area substantially originating from the center of the respective mode; and
locating a component of the plurality of components within each of the detection areas.