Body part recognition in radiographic images

The body part classification of a radiographic image is a key component of systems that produce an optimal tone scale curve for either hard copy or soft copy presentation. A method automatically determines the body part class based on a subset of relevant features and a probabilistic reasoning unit (PRU). The reasoning unit estimates the most probable body part class based on probabilistic information that associates a given class with the joint probability of the detection of a subset of features and their spatial relationships.

FIELD OF THE INVENTION 
This invention relates in general to digital image processing, and more 
specifically, to a method for recognizing the body part contained in a 
digital radiographic image. 
BACKGROUND OF THE INVENTION 
In medical imaging, in order to render the optimal hard copy or soft copy 
image, common practice is to develop a tone scale curve that is customized 
to the particular body part. Furthermore, common practice is to have the 
user input the body part type. An example of this approach is Capozzi and 
Schaetzing, U.S. Pat. No. 5,164,993, issued Nov. 17, 1992, entitled Method 
and Apparatus for Automatic Tonescale Generation in Digital Radiographic 
Images. It is desirable to eliminate the need to manually specify the body 
part by automatically recognizing the body part class that is to be 
rendered. 
A common approach to object recognition is based on collecting a set of 
features and comparing these features with feature vectors of target 
classes. Knecht and Chenoweth, U.S. Pat. No. 4,881,270, issued Nov. 14, 
1989, apply this approach to naval ship detection utilizing Fourier 
transform coefficients. Hutcheson, Or, et al, U.S. Pat. No. 5,465,308, 
issued Nov. 7, 1995, utilizes a neural network to recognize a two 
dimensional image which is similar to an image stored in a database. The 
images are processed to obtain the images power spectrum and the 
coefficients are used as the features. Kaoru and Watanabe, U.S. Pat. No. 
4,944,023, issued Jul. 24, 1990, describe a method of object recognition 
based on describing the image as a set of regions in a n-dimensional space 
defined by a tree structure. 
Horimaki, U.S. Pat. No. 5,263,098, issued Nov. 16, 1993, bases his approach 
on the comparison of a gray level histogram formed from the unknown image 
and compares this histogram with histograms of known objects in an attempt 
to match unknown objects with known objects. 
Katsuma, U.S. Pat. No. 5,353,132, issued Oct. 4, 1994, relies on the 
distribution of color information in an image represented by a color 
histogram for matching. 
Barber, Beital, et al, U.S. Pat. No. 5,579,471, issued Nov. 26, 1996, 
describe a method to search a database in response to a user's queries. 
The system uses general features, including color, of an image in an 
attempt to find similar looking images. 
The methods described by Knecht and Chenoweth, Hutcheson and Or and Daoru 
and Watanabe do not directly use any information regarding the spatial 
relationship between features, or the probability of how likely it is that 
these features will be detected in the unknown image. The method disclosed 
by Horikami is sensitive to scale and occlusion of the objects. Katsuma's 
method relies on color histograms and would not be applicable to gray 
level radiographic images. The purpose of Barber, Beital, et al., is to 
retrieve images from an image database. This approach is related to 
content sensitive image database searching (Chang, S. F. and Smith, J. R., 
"Extracting Multi-Dimensional Signal Features for Content-Based Visual 
Query," SPIE Symposium on Visual Communications and Signal Processing, 
May, 1995). The purpose of these systems is to locate in a large image 
database images that are similar to an example image, sketch or keyword 
text input provided by the user. These systems typically depend on global 
color representation using color histograms and texture (Rubner, Y., 
Guibas, L., and Tomasi, C., "Navigating Through a Space of Color Images," 
World Wide Web, Stanford University). Color representation is a key 
component of these systems. Since the color histograms lack spatial cues, 
coarse segmentation based on color is sometimes used. 
All of these methods either generate a feature vector or histogram and 
attempt to find the best match in a known database. None of the methods 
use a probabilistic model founded on anatomical information, nor is the 
final decision based on the probabilistic evidence resulting from the 
subset of features that were detected. 
SUMMARY OF THE INVENTION 
According to the present invention, there is provided a solution to the 
problems of the prior art. 
According to a feature of the present invention, there is provided a method 
for automatically recognizing the body part class (lateral c-spine, AP 
chest, lateral chest, etc.) contained in a radiographic image. The system 
uses a set of features (or a subset of those features) combined with a 
probabilistic reasoning unit implemented using a Bayesian network to 
identify, in a maximum probabilistic sense, the most probable body part. 
ADVANTAGEOUS EFFECT OF THE INVENTION 
The invention disclosed here matches an unknown image to a body part class 
(for example, lateral c-spine, AP chest, lateral chest, etc.) in a 
database. The purpose is to recognize the class the image belongs to, not 
find images that look similar under some metric. This invention segments 
the unknown image into primitive objects, such as edges, curves, complex 
analytic functions, texture measure, etc. and groups these objects 
together to describe the overall spatial layout of image. The invention is 
a robust automatic body part recognition method that is applicable to both 
storage phosphor based radiographic system as well as direct digital 
radiographic systems. The method of the invention eliminates the need for 
a user to manually insert body part information into the system.

DETAILED DESCRIPTION OF THE INVENTION 
The method of the present invention is described as a series of operations 
performed on a digital radiographic image of a body part. The digital 
image can be formed by the digital image acquisition system of FIG. 4. As 
shown, x-ray source 200 projects x-rays through object 202 (such as a body 
part extremity, e.g., hand or foot) to image acquisition system 204. The 
image acquisition system can be, for example, (1) a standard x-ray 
screen/film combination which produces an x-ray film image which is 
processed chemically or thermally and the processed film digitized by a 
scanner/digitizer 206; (2) a computed radiography system where a latent 
x-ray image is formed in a storage phosphor 204 and a corresponding 
digital image is produced by reading out the storage phosphor by a CR 
reader 206; (3) a diagnostic scanner (such as MRI, CT, US, PET) produces 
an electronic x-ray image which is digitized; and (4) a direct digital 
acquisition system typically consisting of a phosphor based scintillating 
screen coupled to an imager (CCD, MOS) through a lens or fiber optic 
system. 
The digital image is processed in image processor 208, according to the 
method of the present invention. Image processor 208 can take the form of 
a digital computer, such as illustrated in FIG. 5. In such case, one or 
more of the steps of said method can be carried out using software 
routines. Image processor 208 can also include hardware or firmware for 
carrying out one or more of said method steps. Thus, the steps of the 
method of the invention can be carried out using software, firmware, and 
hardware, either alone or in any preferable combination. 
As shown in FIG. 5, a digital computer 300 includes a memory 310 for 
storing digital images, application programs, operating system, etc. 
Memory 310 can include mass memory (such as a hard magnetic disc or CD 
ROM), and fast memory (such as RAM). Computer 30 also includes input 
device 312 (such as a keyboard, mouse, touch screen), display 314 (CRT 
monitor, LCD), central processing unit 316 (microprocessor), output device 
318 (thermal printer, dot matrix printer, laser printer, ink jet printer). 
Components 310,312,314,316,318 are connected together by control/data bus 
320. Computer 300 can include a transportable storage medium drive 322 for 
reading from and/or writing to transportable storage media 324, such as a 
floppy magnetic disk or writeable optical compact disk (CD). 
As used in this application, computer readable storage medium can include, 
specifically, memory 310 and transportable storage medium 324. More 
generally, computer storage medium may comprise, for example, magnetic 
storage media, such as magnetic disk (hard drive, floppy disk) or magnetic 
tape; optical storage media, such as optical disk, optical tape, or 
machine readable bar code; solid state electronic storage devices, such as 
random access memory (RAM), read only memory (ROM); or any other physical 
device or medium which can be employed to store a computer program. 
FIG. 1 is a block diagram of the body part recognition method of the 
present invention. Box 101 is the gray level digital radiographic image 
which is to be classified as to body part class by the method. The unknown 
digital radiographic image is segmented to separate the body part from the 
foreground and background in Box 102. Edge enhancement and noise removal 
algorithms are also applied as a preprocessing step before the extraction 
of features. Boxes 103 and 107 extract the same set of features, the 
difference being that box 103 extracts the features from the unknown 
image, whereas Box 107 extracts the features from the set of images that 
compose the Image Database (Box 105) (e.g., FIG. 5, memory 310). Image 
Database (Box 105) contains representative samples of the image types that 
the system is designed to identify. This would include a wide range of 
body part classes and projections. The features extracted may include both 
low level features, such as gray level values, edges, simple curves, 
complex curves, textures, etc., and higher level features, such as bone 
regions, tissue regions, bone geometry, and hypothesized bone and tissue 
objects (femur bone, lung region, etc.). The method described does not 
limit the features to the ones listed. Any feature can be included that is 
relevant, that is, will help discriminate one body part class from 
another. 
Box 109 is the Probabilistic Reasoning Unit (PRU). A single PRU may contain 
the information for all possible body part classes in the image database, 
or individual PRU can be implemented for each body part class as 
illustrated in FIG. 1. In this case, a subclass of features is chosen that 
best represents that body part class. For example, the chest image class 
may include a texture measure for lung tissue and a detector for vertebra, 
whereas the PRU for the PA hand body class would not. A conditional 
probability for each class of body part and features is estimated based on 
the data in the Image Database (Box 105). The probabilities are 
conditioned not only on if a feature is present, but also on it's spatial 
location, i.e., P(class A.vertline.feature #k present in specified 
location). 
The result is each body part class is described by a set of features and a 
description of the spatial relationship between the features. The spatial 
relationships could be implemented using spatial descriptions such as, to 
the left of, above, below, to the right of and combinations of such 
descriptors, i.e., to the right of and below. Another method would be 
comprised of normalizing the images to a standard size and then projecting 
a grid. In this approach, the features would be located using a grid 
coordinate system, and the spatial relationships between the features 
would be inherent in the grid position description. As shown in FIG. 2, 
Features A, B, C, and D can be located using the descriptors A is to the 
left of C, A and C are both above B, and B is above D. Another method to 
capture the spatial relationships between the features is to use the grid 
locations, i.e., Feature A is located at (2,C), B is located at (5,d), 
etc. 
In a Bayesian Network implementation of a probabilistic reasoning unit, 
each feature is represented by a node in a directed graph. The directed 
links between the nodes indicates the causal relationships between the 
nodes. The conditional probabilities assigned to these links are 
determined from the training data. For example, P(chest=k.vertline.feature 
#1 is present in specified location) is the probability that the image is 
a chest given that feature #1 is true. P(chest=k.vertline.feature #1 is 
not present in specified location) which is the probability that the image 
is a chest given that feature #1 is false. In FIG. 3, the probability that 
the image is a chest is conditioned on the joint probability of C and F3. 
When an unknown image is processed by the feature extraction algorithms 
(FIG. 1, Box 103) it is highly unlikely that all the features will be 
detected. It is now necessary to calculate the probability of the image 
being a member of class A given that only a subset of features were 
detected. For example: 
P(class A .vertline. Feature #1 present in specified location, Feature #2 
not present, Feature #3 not present, . . . , Feature #k present in 
specified location). 
This conditional joint probability is determined by a Bayesian Network 
which has the ability to process uncertainties of this type and produce 
the hypothesis as to which is the most likely body part (J. Pearl, 
"Probabilistic Reasoning in Intelligent System," Morgan-Kaufmann, 1988, 
and F. Jensen, "An Introduction to Bayesian Networks," Springer-Verlag, 
1996). 
The likelihood for each body part is updated when a feature is or is not 
detected. A given set of features will result in a set of probable 
hypothesis which is the maximum of the probabilities of each hypothesis 
given the evidence found (FIG. 1, Box 111) is chosen (FIG. 1, Box 113, 
Body Part Class). For example, if lung tissue is detected in a lateral 
c-spine image, this feature would provide some evidence for a chest image. 
But the remaining feature evidence would not support this possibility and 
the correct c-spine class would be identified. 
The invention has been described in detail with particular reference to 
certain preferred embodiments thereof, but it will be understood that 
variations and modifications can be effected within the spirit and scope 
of the invention. 
TS LIST 
101 gray level digital radiographic image 
102 preprocessing 
103 extract features 
105 image database 
107 extract features 
109 probabilistic reasoning unit (PRU) 
111 max{P(hypothesis.vertline.evidence)} 
113 body part class 
200 x-ray source 
202 object 
204 image acquisition system 
206 scanner/digitizer 
208 image processor 
310 memory 
312 input device 
314 display 
316 central processing unit 
318 output device 
320 control/data bus 
322 transportable storage medium drive