Abstract:
There is disclosed an embodiment for providing a change trend image. An ultrasound data acquisition unit transmits ultrasound signal to a target object and receives echo signal reflected from the target object to sequentially acquire a plurality of ultrasound data. A processor is connected to the ultrasound data acquisition unit. The processor sequentially extracts feature values from the plurality of ultrasound data, allocates colors corresponding to each feature value and forms a change trend image with the colors indicative of a change trend of the extracted feature values over time.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority from Korean Patent Application No. 10-2009-0116360 filed on Nov. 30, 2009, the entire subject matter of which is incorporated herein by reference. 
     TECHNICAL FIELD 
     The present invention generally relates to ultrasound systems, and more particularly to an ultrasound system and method for providing a change trend image with colors. 
     BACKGROUND 
     An ultrasound system has become an important and popular diagnostic tool due to its non-invasive and non-destructive nature. The ultrasound system can provide high dimensional real-time ultrasound images of inner parts of target objects without a surgical operation. 
     The ultrasound system transmits ultrasound signals to the target objects, receives echo signals reflected from the target objects and provides ultrasound images of the target objects based on the echo signals. The ultrasound system may perform transmitting and receiving of ultrasound signals sequentially and iteratively to thereby form a plurality of ultrasound images. 
     The plurality of ultrasound images may be sequentially displayed on a display unit. Further, the ultrasound system may synthesize the plurality of ultrasound images to form a synthesis image for image enhancement. The synthesis image may also be displayed on the display unit. In such a case, since the ultrasound images and the synthesis image are sequentially displayed one by one on the display unit, it may be difficult to intuitively recognize the change of a specific feature such as a brightness change over time at a specific region in the ultrasound images. 
     SUMMARY 
     An embodiment for providing a change trend image is disclosed herein. In one embodiment, by way of non-limiting example, an ultrasound system includes: an ultrasound data acquisition unit configured to transmit ultrasound signal to a target object and receive echo signal reflected from the target object to sequentially acquire a plurality of ultrasound data; and a processor connected to the ultrasound data acquisition unit, the processor being configured to sequentially extract feature values from the plurality of ultrasound data, allocate colors corresponding to each feature value and form a change trend image with the colors indicative of a change trend of the extracted feature values over time. 
     In another embodiment, a method of providing a change trend image comprises: transmitting ultrasound signal to a target object and receiving echo signal reflected from the target object to sequentially acquire a plurality of ultrasound data; sequentially extracting feature values from the plurality of ultrasound data; allocating colors corresponding to each feature value; and forming a change trend image with the colors indicative of a change trend of the extracted feature values over time. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing an illustrative embodiment of an ultrasound system. 
         FIG. 2  is a block diagram showing an illustrative embodiment of an ultrasound data acquisition unit in  FIG. 1 . 
         FIG. 3  is a flowchart showing a process of providing a change trend image showing a change trend on specific feature values extracted from ultrasound images over time. 
         FIG. 4  is a schematic diagram showing information on feature values from a plurality of ultrasound images including brightness values, maximum brightness values, minimum indexes, minimum index normalized values and average brightness values. 
         FIG. 5  is another flowchart showing a process of providing a change trend image showing a change trend on specific feature values extracted from ultrasound images over time. 
     
    
    
     DETAILED DESCRIPTION 
     This detailed description is provided with reference to the accompanying drawings. One of ordinary skill in the art may realize that the following description is illustrative only and is not in any way limiting. Other embodiments of the present invention may readily suggest themselves to such skilled persons having the benefit of this disclosure. 
     First Embodiment 
       FIG. 1  is a block diagram showing an illustrative embodiment of an ultrasound system. As depicted therein, the ultrasound system  100  may include an ultrasound data acquisition unit  110 , a processor  120 , a memory  130  and a display unit  140 . 
     The ultrasound data acquisition unit  110  may be configured to transmit and receive ultrasound signals to and from a target object to thereby output ultrasound data of the target object. The ultrasound data acquisition unit  110  may be explained more particularly by referring to  FIG. 2 . 
       FIG. 2  is a block diagram showing an illustrative embodiment of the ultrasound data acquisition unit  110 . The ultrasound data acquisition unit  110  may include a transmit (Tx) signal generating section  111 , an ultrasound probe  112  having a plurality of transducer elements (not shown), a beam former  113  and an ultrasound data forming section  114 . 
     The Tx signal generating section  111  may be configured to generate Tx signals. The Tx signal generating section  111  may generate a plurality of Tx signals and apply delays to the Tx signals in consideration of distances between the respective transducer elements and focal points for acquiring ultrasound images indicative of the target object. The ultrasound images may include a brightness mode (B-mode) image, a Doppler mode (D-mode) image, a color mode (C-mode) image and a three-dimensional mode (3D mode) image. The B-mode image may represent a two-dimensional ultrasound image with brightness, which is determined according to reflection coefficients of the ultrasound signals reflected from the target object. The D-mode image may represent a Doppler spectrum image indicative of velocities of a moving target object, which may be measured over time by using the Doppler Effect. The C-mode image may represent an image showing velocities of moving object, which may be measured by using the Doppler Effect, with predetermined colors. The 3D mode image may represent a three-dimensional ultrasound image indicative of the target object, which may be formed by using reflection coefficients of the ultrasound signals reflected from the target object. The Tx signal generating section  111  may generate the Tx signals sequentially and iteratively. 
     The ultrasound probe  112  may include the plurality of transducer elements for reciprocally converting between ultrasound signals and electrical signals. The ultrasound probe  112  may be configured to transmit ultrasound signals to the target object in response to the Tx signals provided from the Tx signal generating section  111 . The ultrasound probe  112  may further receive ultrasound echo signals reflected from the target object to thereby output the received signals. The received signals may be analog signals. The ultrasound probe  112  may form a plurality of received signals by transmitting and receiving ultrasound signals sequentially and iteratively to and from the target object based on the Tx signals. The ultrasound probe  112  may include a three-dimensional (3D) mechanical probe, a two-dimensional (2D) array probe and the like. However, it should be noted herein that the ultrasound probe  112  may not be limited thereto. 
     The beam former  113  may be configured to convert the received signals provided from the ultrasound probe  112  into digital signals. The beam former  113  may further apply delays to the digital signals in consideration of distances between the transducer elements and focal points to thereby output digital receive-focused signals. The receive-focusing and the delay application upon the plurality of received signals may be carried out sequentially and iteratively. 
     The ultrasound data forming section  114  may be configured to form ultrasound data corresponding to each of the plurality of ultrasound images based on the digital receive-focused signals provided from the beam former  113 . The ultrasound data forming section  114  may form a plurality of ultrasound data sequentially and iteratively based on the plurality of digital receive-focused signals. The plurality of ultrasound data may be radio frequency (RF) data. However, it should be noted herein that the ultrasound data may not be limited thereto. The ultrasound data forming section  114  may further perform various signal processing (e.g., gain adjustment) upon the digital receive-focused signals. 
     Referring back to  FIG. 1 , the processor  120  is connected to the ultrasound data acquisition unit  110 . The processor  120  may form a plurality of ultrasound images based on the plurality of ultrasound data sequentially provided from the ultrasound data acquisition unit  110 . The processor  120  may extract specific feature values from the plurality of ultrasound images to obtain a change trend of the specific feature values over time. The specific feature values may include at least one of a maximum brightness value, a normalized value of minimum index, an average brightness value, a normalization function obtained by normalizing an accumulated function of brightness values, first index information on brightness values over a predetermined critical value, a dispersion of brightness values, a standard deviation of brightness values, a skewness of brightness values, a kurtosis of brightness values, a minimum brightness value, a brightness value of an N th  ultrasound image, wherein N is a natural number, a orientation change function of a brightness value gradient and a phase change function of the brightness value gradient. For simplicity of the below description, it is considered that the feature values may include the maximum brightness value, the normalized value of minimum index and the average brightness value. However, the feature values are not limited thereto. Furthermore, the processor  120  may form a change trend image showing the extracted feature values with colors over time. 
       FIG. 3  is a flowchart showing a process of forming a change trend image over time based on the extracted feature values. The processor  120  may form the ultrasound images including index information based on the ultrasound data provided from the ultrasound data acquisition section  110 , at step S 102 . The index information may include information on a formation time of each of the ultrasound images or a serial number indicating a formation order of each of the ultrasound images. The processor  120  may sequentially and iteratively form a plurality of ultrasound images based on the ultrasound data sequentially provided from the ultrasound data acquisition section  110 . The ultrasound images formed at the processor  120  are sequentially stored in the memory  130 , as shown in  FIG. 4 . 
       FIG. 4  is a schematic diagram showing information on feature values from a plurality of ultrasound images including brightness values, maximum brightness values, minimum indexes, minimum index normalized values and average brightness values. 
     The processor  120  may extract the plurality of ultrasound images from the memory  130 , at step S 104 . In one embodiment, the processor may extract the ultrasound images from the first stored ultrasound image U 1  to the lately stored ultrasound image U 8 , as shown in  FIG. 4 . 
     The processor  120  may detect the brightness value (I(p)) of any position (p) of the ultrasound images, at step S 106 . In one embodiment, the processor  120  may detect the I(p) “20”, “10”, “10”, “10”, “30”, “200”, “200” and “190” at p in the ultrasound images U 1 -U 8 , as shown in  FIG. 4 . The p may denote a position of an arbitrary pixel in the two-dimensional ultrasound image such as the B-mode ultrasound image, the D-mode ultrasound image and the C-mode ultrasound image, and denote a position of an arbitrary voxel in the three-dimensional ultrasound image such as the 3D mode ultrasound image. 
     The processor  120  may detect a maximum brightness value at the position “p” based on the detected brightness values, at step S 108 . Furthermore, the processor  120  may set the maximum brightness value at the position “p” by using the detected maximum brightness values, at step S 110 . In one embodiment, the processor  120  may set the maximum brightness value (I max (p)) by using the following equation (1)
 
 I   max ( p )=max( I ( p,i ))  (1)
 
wherein “i” denotes the index information. Thus, the processor  120  may initially set “20,” which is detected from the ultrasound image U 1  as the I max (p), as depicted in  FIG. 5 . The processor  120  may compare the maximum brightness value with the brightness value of the position “p” of the ultrasound image U 2  to check which one is bigger. Since the brightness value “20” of the position “p” of the ultrasound image U 2  is smaller than the I max (p), the processor  120  may set the brightness values “20” as the I max (p) for the ultrasound image U 2 . In the same manner, the processor  120  may set the maximum brightness values “20”, “20”, “30”, “200”, “200”, “200” as I max (p) of the position “p” of the respective ultrasound image U 3-8 , as shown in  FIG. 4 .
 
     The processor  120  may set the minimum index at the position “p” of the ultrasound images by using the detected maximum brightness value, at step S 112 . The minimum index may be the index corresponding to the ultrasound image with the maximum brightness value detected. In one embodiment, the processor  120  may set the minimum index (M min (p)) by using the following equation (2)
 
 M   min ( p )=min(arg max( I   max ( p )))  (2)
 
     In one embodiment, the processor  120  may initially set “1” as the minimum index since the brightness value at the position “p” of the ultrasound image U 1  is the maximum brightness value. Since the brightness value at the position “p” of the ultrasound image U 1  is set as the I max (p) of at the position “p” for the ultrasound images U 2 -U 4 , the processor  120  may set “1” as the minimum index at the position “p” of the ultrasound images U 2 -U 4 , as shown in  FIG. 4 . In the same manner, the processor  120  may set the minimum index at the position “p” of the ultrasound images U 5 -U 8  to “5”, “6”, “6” and “6,” as shown in  FIG. 4 . 
     The processor  120  may calculate a normalized value by normalizing the minimum index of the plurality of ultrasound images, at step S 114 . In one embodiment, the processor  120  may calculate the normalized value (NM min (p)) by using the following equation (3). 
                         NM   min     ⁡     (   p   )       =         M   min     ⁡     (   p   )       a       ,     0   ≤       NM   min     ⁡     (   p   )       ≤   1             (   3   )               
wherein “a” denotes an accumulated number of the same minimum index.
 
     In one embodiment, the processor  120  may initially normalize the minimum index “1” at the position “p” of the ultrasound image U 1  by using the equation (3) to thereby obtain a normalizing value “1,” as shown in  FIG. 4 . The processor  120  may calculate the normalizing value by normalizing the minimum index at the position “p” of the ultrasound image U 2  by using the equation (3) to thereby obtain a normalizing value “½.” In the same manner, the processor  120  may calculate the normalizing values by normalizing the minimum indices at the position “p” in each of the ultrasound image U 3 -U 8  to thereby obtain normalizing values “⅓,” “¼,” “1,” “½” and “⅓,” as shown in  FIG. 4 . 
     The processor  120  may calculate an average of brightness values at the position “p” from the sequentially formed ultrasound images, at step S 116 . In one embodiment, the processor  120  may calculate the average brightness value (Ī(p)) by using the following equation (4) 
                       I   _     ⁡     (   p   )       =       1   i     ⁢       ∑   i     ⁢     I   ⁡     (   p   )                   (   4   )               
wherein “i” denotes the number of the ultrasound images.
 
     In other words, the processor  120  may calculate the average brightness value at the position “p” from the ultrasound image U 1  to thereby obtain an average brightness value “20” at the position “p” of the ultrasound image U 1  by using the equation (4), as shown in  FIG. 4 . The processor  120  may calculate the average brightness value at the position “p” from the ultrasound image U 1 -U 2  to thereby obtain an average brightness value “15.” The processor  120  may calculate the average brightness value at the position “p” from the ultrasound images U 1 -U 3  to thereby obtain an average brightness value “13.33.” In the same manner, the processor  120  may calculate average brightness values at the position “p” from the ultrasound images U 4 -U 8  to thereby obtain “12.5,” “16,” “46.67,” “68.57” and “83.75,” as shown in  FIG. 4 . 
     The processor  120  may form a change trend image by using the feature values such as the normalized value (NM min (p)), the average brightness value (Ī(p)) and the maximum brightness value (I max (p)), at step S 118 . The processor  120  may allocate colors corresponding to each feature value. The change trend image may be formed by using various color models such as the HSV color model, the HSB color model, RGB color model, CMYK color model, Lab color model and the like. 
     In case of the HSV color model, the processor  120  may apply calculate channel values for a H channel, a S channel and a V channel by applying the normalized value, the maximum brightness value and the average brightness value to the following equation (5) to thereby form the change trend image.
 
 H ( p )=360× NM   min ( p )
 
 S ( p )= Ī ( p )
 
 V ( p )= I   max ( p )  (5)
 
wherein “H(p)” denotes the channel value of the H channel, “S(p)” denotes the channel value of the S channel and “V(p)” denotes the channel value of the V channel.
 
     In this embodiment, the channel values of the H channel, the S channel and the V channel may be calculated by using the normalized value, the maximum brightness value and the average brightness value. However, the method of calculating the channel values of the H channel, the S channel and the V channel is not limited thereto. In another embodiment, at least one of channel values of the H channel, the S channel and the V channel may be calculated by using the normalized value, the maximum brightness value and the average brightness value, while another channel values of the H channel, the S channel and the V channel may be set as predetermined values. 
     The processor  120  may extract the colors relevant to a change trend of specific feature values from a color map to thereby form the change trend image. The color map may be formed relevant to the change trend. 
     The storage unit  130  may sequentially store the ultrasound images formed at the processor  120 . The ultrasound images may include the index information. The display unit  140  may display the change trend image formed by the processor  120 . Furthermore, the display unit  140  may display the ultrasound images formed by the processor  120 . The display unit  140  may include a cathode ray tube (CRT) display, a liquid crystal display (LCD), organic light emitting diodes (OLED) display and the like. 
     Second Embodiment 
       FIG. 5  is another flowchart showing a change trend image forming process based on the extracted feature values over time. 
     The processor  120  may extract specific feature values from the plurality of ultrasound images to obtain a change trend of the specific feature values over time. The specific feature values may include at least one of a maximum intensity value, a normalized value of minimum index, an average intensity value, a normalization function obtained by normalizing an accumulated function of intensity values, first index information on intensity values over a predetermined critical value, a dispersion of intensity values, a standard deviation of intensity values, a skewness of intensity values, a kurtosis of intensity values, a minimum intensity value, a intensity value of an N th  ultrasound image, wherein N is a natural number, a orientation change function of a intensity value gradient, and a phase change function of the intensity value gradient. For simplicity of the below description, it is considered that the feature values may include the maximum intensity value, the normalized value of minimum index and the average intensity value. However, the feature values are not limited thereto. Furthermore, the processor  120  may form a change trend image showing the extracted feature values with colors over time. 
     Referring to  FIG. 5 , the processor  120  may extract the plurality of ultrasound data from the memory  130 , at step S 202 . In one embodiment, the processor  120  may extract the ultrasound data from the first stored ultrasound image to the lately stored ultrasound image. 
     The processor  120  may detect the intensity value based on the extracted ultrasound data, at step S 204 . In one embodiment, the processor  120  may detect a relevant ultrasound data at any position “p” of the ultrasound image by using a scan conversion relation between the ultrasound data and the ultrasound image to thereby detect the intensity value of the detected ultrasound data. 
     The processor  120  may detect the maximum intensity value at the position “p” by using the detected intensity value, at step S 206 . Furthermore, the processor  120  may set the maximum intensity value at the position “p” by using the detected maximum intensity value, at step S 208 . In the second embodiment, the method of detecting and setting the maximum intensity value is similar to the method of detecting and setting the maximum brightness value of the first embodiment. Thus, the detailed description of the method of detecting and setting the maximum intensity value is omitted. 
     The processor  120  may set the minimum index at the position “p” of the ultrasound image by using the detected maximum intensity value, at step S 210 . The minimum index may represent the index of the maximum intensity value detected ultrasound data. In the second embodiment, the method of setting the minimum index is similar to the method of the first embodiment. Thus, the detailed description of the method of setting the minimum index is omitted. 
     The processor  120  may calculate the normalized value by performing normalizing of the minimum index, at step S 212 . In the second embodiment, the method of calculating the normalized value of the minimum index is similar to the method of the first embodiment. Thus, the detailed description of the method of calculating the normalized value of the minimum index is omitted. 
     The processor  120  may calculate an average of intensity values at the position “p” from the sequentially formed ultrasound images relevant to the ultrasound data by using the intensity value, at step S 214 . In the second embodiment, the method of calculating the average intensity value is similar to the method of the first embodiment. Thus, the detailed description of the method of calculating the average intensity value is omitted. 
     The processor  120  may form the change trend image by using the detected change trend such as the normalized value, the average intensity value and maximum intensity value, at step S 216 . 
     The change trend image may be formed by using various color models such as the HSV color model, the HSB color model, RGB color model, CMYK color model, Lab color model and the like. The processor  120  may allocate colors corresponding to each feature value. The processor  120  may extract the colors relevant to the feature values from a color map to thereby form the change trend image. The color map may be formed relevant to the change trend. 
     Referring back to  FIG. 1 , the display unit  140  may display the change trend image formed by the processor  120 . 
     Any reference in this specification to “one embodiment,” “an embodiment,” “example embodiment,” “illustrative embodiment,” etc. means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment. Further, when a particular feature, structure or characteristic is described in connection with any embodiment, it is submitted that it is within the purview of one skilled in the art to affect such feature, structure or characteristic in connection with other embodiments. 
     Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, numerous variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.