Abstract:
A method, system and computer readable medium configured for computerized detection of lung abnormalities, including obtaining a standard digital chest image and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; applying extracted features from remaining candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities to respective artificial neural networks to eliminate further false positive candidate abnormalities; performing a logical OR operation of the candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities; and outputting a signal indicative of a result of performing the logical OR operation. The logical OR combination, of locations of the candidate abnormalities detected in the first difference image and the second difference image, yields an improved detection sensitivity (over 90%) and only slightly increased false positives rate (3.2 false positives per chest image).

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS AND PUBLICATIONS 
     The present invention is related to automated techniques for automated detection of abnormalities in digital images, for example as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; and 5,740,268; as well as U.S. application Ser. Nos. 08/158,388; 08,173,935; 08/220,917; 08/398,307; 08/428,867; 08/523,210; 08/536,149; 08/515,798; 08/562,188; 08/562,087; 08/757,611; 08/758,438; 08/900,188; 08/900,189; 08/900,191; 08/900,192; 08/900,361; 08/900,362; 08/979,623; 081979,639; 08/982,282; 09/028,518; 09/027,685, and 09/053,798, each of which are incorporated herein by reference in their entirety. Of these patents and applications, U.S. Pat. Nos. 4,907,156; 5,289,374; 5,319,549; 5,463,548; 5,622,171; U.S. Ser. Nos. 08/562,087; 08/562,188; 08/757,611; 08/758,438; 08/900,361 and 09/027,685 are of particular interest. 
     The present invention also relates to various technologies referenced and described in the references identified in the appended APPENDIX and cross-referenced throughout the specification by reference to the number, in brackets, of the respective reference listed in the APPENDIX, the entire contents of which are also incorporated herein by reference. Various of these publications may correspond to various of the cross-referenced patents and patent applications. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention is related to computer-aided detection of lung nodules in medical images and, in particular, to computer-aided diagnosis of soft-tissue and standard chest radiograph images for improving performance in detecting lung nodules. 
     2. Discussion of the Background 
     Lung cancer is the leading cause of cancer deaths among the population in the United States. It is estimated that there were 177,000 new lung cancer cases and 158,700 patient deaths from this disease in 1996. Patients with early detection of lung cancer followed by proper treatment with surgery and/or combined with radiation and chemotherapy can improve their five-year survival rate from 13% to about 41%. [1] Currently, chest radiography is still the most commonly used diagnostic modality for detecting the solitary lung nodule in chest images, which is an important sign of primary lung cancer. However, the detection and diagnosis of pulmonary nodules in standard chest radiographic images are very difficult even for experienced radiologists, mainly because of the interference of the normal anatomic background structures in the images. Standard chest radiographic images are chest images containing normal anatomic background structures in the images, such as ribs, clavicle, cardiac shadow, and pulmonary vessels, and typically obtained by single-exposure using screen film systems. Many studies have indicated that radiologists could overlook up to 30% of actual lung cancer cases. [2-4] Previously, investigators at the Department of Radiology of the University of Chicago have developed an improved computer-aided diagnosis (CAD) scheme for automated detection of lung nodules in standard chest radiographic images. [5-6] Radiologists may use the computer output from the CAD scheme as a “second opinion” to improve their diagnostic accuracy in the detection of early lung cancer. 
     Nevertheless, the normal anatomic background structures in the standard chest radiographic image, namely, ribs, clavicle, cardiac shadow, and pulmonary vessels tend to degrade the performance (in terms of the sensitivity and number of false positives per image) of the CAD scheme. Nodules may not be detected by the CAD scheme if they overlap fully or partially with ribs or clavicles. Crossings of rib-rib or rib-vessel are the major source of a false-positive detection output from the CAD scheme. Therefore, it is expected that the performance of lung nodule detection from the CAD scheme for the chest radiographic images would be improved if the bony structures can be removed therefrom. 
     The energy subtraction technique implemented in some recent chest computed radiography (CR) systems have provided soft-tissue chest images in which bony structures are successfully removed by subtraction of a properly weighted low energy x-ray exposed image from a properly weighted high energy x-ray exposed image. [7-11] However, soft-tissue images usually are very noisy and lower in image contrast compared with standard chest radiographic images. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     The present invention was made in part with U.S. Government support under grant numbers CA 62625 (National Institutes of Health). The U.S. Government has certain rights in the invention. 
    
    
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to provide improved automated lung nodule detection using soft-tissue and standard chest images. 
     It is another object of the present invention to provide an improved CAD scheme for lung nodule detection using both soft-tissue and standard chest images. 
     It is a further object of the present invention to provide an improved CAD scheme for lung nodule detection using soft-tissue and standard chest images, as well as the logical OR combination of the two. 
     These and other objects are achieved according to the present invention by providing a novel method, system and computer readable medium for computerized detection of lung abnormalities, including obtaining a standard digital chest image and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; performing a logical OR operation of the candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities; and outputting a signal indicative of a result of performing the logical OR operation. 
     The present invention similarly includes a computer readable medium storing program instructions by which the method of the invention can be performed when the stored program instructions are appropriately loaded into a computer, and a system for implementing the method of the invention. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein: 
     FIG. 1 is a top-level block diagram of the system for implementing the computer-aided diagnosis (CAD) scheme according to the present invention; 
     FIG. 2 is a flowchart illustrating the CAD scheme according to the present invention; 
     FIG. 3 is a flowchart illustrating details of the CAD scheme according to the present invention; 
     FIGS. 4A and 4B show a standard chest image (FIG. 4A) and its corresponding soft-tissue chest image (FIG.  4 B), wherein nodules at right middle lung and left lower lung are overlapped with ribs; 
     FIGS. 5A and 5B show difference images of the standard (FIG. 5A) and soft-tissue (FIG. 5B) chest images, wherein the difference image of the soft-tissue image has a more uniform background than that of the standard chest image; 
     FIGS. 6A and 6B show computer outputs from the CAD scheme according to the present invention for the standard (FIG. 6A) and the soft-tissue (FIG. 6B) chest images, wherein two nodules are detected by the CAD scheme in the soft-tissue chest image without any false positives and the left lower nodule is missed by the CAD scheme in the standard chest image; 
     FIGS. 7A and 7B show the standard (FIG. 7A) and its corresponding soft-tissue (FIG. 7B) chest image, wherein a nodule is located at an apex of left lung; 
     FIGS. 8A and 8B show the computer outputs from the CAD scheme according to the present invention for the standard (FIG. 8A) and soft-tissue (FIG. 8B) chest images, wherein the nodule at the apex of left lung is not detected by the CAD scheme for the soft-tissue image due low image contrast and high noise level in that region and the nodule is detected in the standard chest image with two false positives; 
     FIG. 9 is a graph comparing FROC curves resulting from the application of the CAD scheme according to the present invention on soft-tissue chest images, standard chest images, and the logical OR combination thereof, respectively; 
     FIG. 10 is a schematic illustration of a general purpose computer  300  programmed according to the teachings of the present invention. 
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Referring now to the drawings, wherein like reference numerals designate identical or corresponding parts throughout the several views, and more particularly to FIG. 1 thereof, there is illustrated a top-level block diagram of the system for implementing the computer-aided diagnosis (CAD) scheme according to the present invention According to the present invention, a total of 31 pairs or cases of, e.g., 10″×12″, standard and soft-tissue chest films were used. These films were printed from April to September, 1997, in The Department of Radiology, The University of Chicago Hospitals. All of these cases contained lung nodules. A total of 65 nodules were confirmed in these 31 cases by two chest radiologists, based on their consensus. 
     In FIG. 1, the system includes digital image obtaining device(s)  100  coupled to a computer  300 . Digital images are obtained via digital image obtaining device(s)  100 , such a as an X-ray printing device and an image acquisition device. For example, films are printed using the X-ray printing device, such the CR system, or the like. Digital images of the 31 pairs of standard and soft-tissue chest films are obtained by digitization of these films using the image acquisition device, such as the Konica laser digitizer (LD4500), or the like. The resolution and the gray scale of the digitization is, for example, 0.175 mm and 10 bits, respectively. The digital images are then, for example, sub-sampled to a matrix size of 500×500 with an effective pixel size of 0.7 mm (not shown). 
     In addition, it should be noted that digital images can also be obtained with the digital image obtaining device(s)  100 , such as a picture archive communication system (PACS). In other words, often the digital images being processed will be in existence in digital form and need not be converted to digital form in practicing the invention. 
     The CAD scheme according to the present invention, based on the obtained digital images, is implemented using a general purpose computer  300 , such as a Intel-based personal computer, Macintosh personal computer, or the like, as is later described, coupled to the digital image obtaining device(s)  100  via a network connection, modem connection, or the like. 
     FIG. 2 is a top-level flowchart illustrating the (CAD) scheme according to the present invention. In FIG. 2, after obtaining a digital image at step  10 , the CAD scheme according to the present invention includes four major processing steps  20 - 50  for standard images and  20 ′- 50 ′ for soft-tissue images. [5-6] As previously discussed, the digital images may be obtained, for example, via digital image obtaining device(s)  100 , such as (i) the X-ray printing device and the image acquisition device, or (ii) the PACS. 
     In steps  20  and  20 ′, a difference image for each of the standard and soft-tissue chest images is produced (e.g., as taught in U.S. Pat. No. 4,907,156 and patent application Ser. Nos. 08/562,087 and 09/027,685) based on the respective images acquired at step  10 . Next, initial nodule candidates are selected from the respective difference images at steps  30  and  30 ′ (e.g., as taught in U.S. patent application Ser. No. 08/900,361), as is later described. 
     In steps  40  and  40 ′, adaptive rule-based analysis is performed on the standard digital chest image and its difference image (step  40 ) and separately on the soft-tissue digital chest image and its difference image (step  40 ′). In step  40 , features are extracted from the standard digital chest image and from its respective difference image and the extracted features are analyzed to identify false positive nodule candidates and to eliminate the identified false positives nodule candidates from further consideration. Correspondingly, in step  40 ′, features are extracted from the soft-tissue digital chest image and its respective difference image and the extracted features are analyzed to identify false positive nodules candidate and to eliminate the identified false positives nodule candidates from further consideration (e.g., as taught in U.S. Pat. Nos. 5,289,374 and 5,319,549 and patent application Ser. Nos. 08/562,087 and 08/900,361). [5-6] The extracted features are related to gray level, morphology, or edge gradient, such as effective diameter, degrees of circularity and irregularity, slopes of the effective diameter and degrees of circularity and irregularity, average gradient, standard deviation of gradient orientation, contrast and net contrast (e.g., as taught in patent application Ser. No. 08/562,087). 
     In steps  50  and  50 ′, trained artificial neural network (ANN) are employed for further removal of false positive outputs remaining after the adaptive rule-based analysis of steps  40  and  40 ′ (e.g., as taught in U.S. Pat. Nos. 5,463,548 and 5,622,171 and patent application Ser. Nos. 08/562,087; 08/562,188; 08/758,438; 08/900,361; and 09/027,685), respectively. A logical OR operation is performed on the results from steps  50  and  50 ′ at step  60  and a signal indicative of a result of performing the logical OR operation is output. 
     At step  70 , the results of the CAD scheme are displayed with arrows, or the like (e.g., as taught in patent application Ser. Nos. 08/757,611, and 08/900,361), indicating the location of the final nodule candidates determined from steps  50 ,  50 ′ and/or step  60 , on the soft-tissue or standard images. 
     FIG. 3 is a flowchart illustrating initial nodule candidate selection of steps  30  and  30 ′ in FIG.  2 . In FIG. 3, multiple gray-level thresholding of the respective difference images obtained at steps  20  and  20 ′ is performed at steps  32  and  32 ′ followed by classification of each of the respective candidates into six groups at steps  34  and  34 ′ (e.g., as taught in patent application Ser. Nos. 08/562,087 and 08/900,361). Briefly, after the initial nodule candidates are selected from the difference image by multiple gray-level thresholding, these nodule candidates are then classified in six groups according to their “starting % threshold levels”, i.e., the percentage threshold levels at which the nodule candidates can be identified (see, e.g., patent application Ser. No. 08/562,087). [5-6] 
     It is noted that the CAD scheme was initially developed for standard chest images. According to the present invention, it was found that this scheme can be applied to soft-tissue chest images directly without any modification of the basic procedures of the CAD scheme. However, the rules for applying the adaptive rule-based tests to eliminate false positives in each candidate group typically were determined separately for standard and soft-tissue chest images (e.g., steps  40  and  40 ′ of FIG.  2 ). 
     Individual selection of adaptive rule-based test rules for standard and soft-tissue chest images is typically necessary, because the derived image features are typically different for nodule candidates in standard and in soft-tissue chest images. For example, the effective diameter (in terms of mm) and degree of circularity obtained by a region growing technique on a nodule in soft-tissue images tends to be larger than that of the same nodule in standard chest images. This is because, in the soft-tissue images, the effects of ribs or bones on the region growing process are diminished, and thus the size and shape derived from the region growing process for a nodule are very close to its actual size and shape. However, for the same nodule in the standard chest images, the size and shape obtained by the region growing technique typically tend to be smaller and more irregular than the original size and shape due to the presence of rib or bone structures around the nodule. 
     Because the soft-tissue chest images typically appear low in image contrast and noisy, the image feature of nodule contrast, which is defined as the pixel value difference before and after the region growing process, derived from the soft-tissue images typically is smaller than that from the corresponding standard chest images. However, although the rules for applying the adaptive rule-based tests to eliminate false positives in each candidate group typically were determined separately for standard and soft-tissue chest images, the same adaptive rule-based tests could be applied to both types of images. In addition, although the flowchart of FIG. 2 shows respective parallel paths for processing the standard and soft-tissue images (e.g., FIG. 2, steps  20 - 50  and  20 ′- 50 ′), it is possible to perform serial processing of both types of images (e.g., FIG. 2, steps  20 - 30  and  50 ), especially where the same adaptive rule-based analysis is performed for each type of image (e.g., if in FIG. 2, steps  40  and  40 ′ are the same). 
     For both the standard and soft-tissue images, the present invention employs an artificial neural network (ANN) for further analysis and further elimination of false positives, where possible (FIG. 2, steps  50  and  50 ′). Thereafter, in a preferred embodiment of the invention, the remaining candidate nodules are OR&#39;d (FIG. 2, step  60 )and signals related thereto are output, for example, for display. (FIG. 2, step  70 ). In step  50 , for each candidate nodule derived from the standard chest image and remaining after step  40 , extracted features for the respective remaining candidate nodule are applied as ANN inputs to an ANN. In steps  50 , for each remaining candidate nodule, respective extracted features at steps  40  from both the standard chest image and its difference image are applied as ANN inputs. Similar processing occurs in step  50 ′ on the remaining candidate nodules derived from the soft-tissue image and its difference image. In the constructing the ANN, the present invention employs, for example, the leave-one-out method instead of the Jack-Knife method because of the relatively small database. The final performance of the CAD scheme for the standard and soft-tissue chest images is represented by FROC curves, as is later discussed. 
     FIGS. 4A and 4B respectively show standard and soft-tissue chest images showing two nodules in the middle right and lower left lung. The difference images corresponding to the standard and soft-tissue chest images are shown in FIGS. 5A and 5B, respectively. It is noted that the difference image resulting from the soft-tissue image contains a more uniform background than does that from the corresponding standard image. Thus, it is expected that the difference image resulting from the soft-tissue image would yield fewer false positives. It also noted that some nodules in the standard chest images are overlapped with ribs, for example, the lower left lung nodule in FIG.  4 A. These nodules are often less enhanced, even by the difference image technique, and thus are difficult to detect in the standard chest images. However, these nodules may be detectable in the soft-tissue images because of the removal of the rib or bone structures as shown in FIG.  4 B. 
     FIGS. 6A and 6B show the respective computer display outputs from the CAD scheme according to the present invention for the standard and the soft-tissue images. It is noted that the lower left lung nodule was not detected in the standard chest image (FIG.  6 A). Nevertheless, in the corresponding soft-tissue chest image (FIG.  6 B), the CAD scheme detected both the middle right and lower left lung nodules with no false positive output. For a pair of standard and soft-tissue chest images, the logical OR combination output is also displayed on the computer (i.e., with arrows as taught in U.S. patent application Ser. Nos. 08/757,611, and 08/900,361) marked on the standard chest images to indicate the potential nodule locations. However, these arrows are derived from the logical OR operation of detected nodule locations of the standard and its corresponding soft-tissue chest image. In this case, the logical OR combination output (not shown) is the standard chest image or the soft-tissue chest image with a total of 3 arrows pointing to the middle right nodule, lower left nodule, and a false positive at the left diaphragm area, respectively. 
     In FIGS. 7A and 7B, a nodule is present at the apex of the left lung. The soft-tissue chest image (FIG. 7B) has a low image contrast and high noise level around the nodule area. Accordingly, the CAD scheme according to the present invention does not detect this nodule in the soft-tissue chest image (FIG. 7B) due to these factors as shown in FIG.  8 B. However, this nodule is detected in the standard chest image (FIG.  7 A), but with two false positives as shown in FIG.  8 A. In this case, the logical OR combination output (not shown) is the same as the output on the standard chest image (FIG.  8 A). 
     FIG. 9 shows FROC curves for cases where the CAD scheme is applied to standard chest images, the corresponding soft-tissue images, and a logical OR combination of the detection results from both the standard and soft-tissue images. It is noted that the CAD scheme typically achieves better performance as applied to soft-tissue images, in terms of high sensitivity and low false positive rate, as compared to being applied to standard chest images. For this very limited database, at the sensitivity of 70%, the false positive rate is less than 1 per chest image for soft-tissue images. However, for standard chest images, the false positive rate is about 2.2 per chest image at the same sensitivity level. The logical OR combination can have a much higher sensitivity in the detection of lung nodules in chest images, as shown in FIG.  9 . For a sensitivity above 90%, the number of false positives per chest image is about 3.2 for the logical OR combination. By comparing the FROC curves for the logical OR combination and the standard and soft-tissue chest images, it is apparent that an increase in the sensitivity from 70% to 90% is more significant than a modest increase in the number of false positives per image (from about 2.2 to 3.2). Since radiologists may miss up to 30% of actual lung cancer cases in reading chest images, the CAD scheme according to the present invention with a detection sensitivity of 90% and a modest false positive rate may greatly improve the radiologists&#39; diagnostic accuracy in detecting lung nodules in chest images. 
     This invention may be conveniently implemented using a conventional general purpose digital computer or micro-processor programmed according to the teachings of the present specification, as will be apparent to those skilled in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those skilled in the software art. 
     The present invention includes a computer program product which is a storage medium including instructions which can be used to program a computer to perform processes of the invention. The storage medium can include, but is not limited to, any type of disk including floppy disks, optical discs, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions. 
     FIG. 10 is detailed schematic diagram of the general purpose computer  300  of FIG.  1 . In FIG. 10, the computer  300 , for example, includes a display device  302 , such as a touch screen monitor with a touch-screen interface, a keyboard  304 , a pointing device  306 , a mouse pad or digitizing pad  308 , a hard disk  310 , or other fixed, high density media drives, connected using an appropriate device bus, such as a SCSI bus, an Enhanced IDE bus, a PCI bus, etc., a floppy drive  312 , a tape or CD ROM drive  314  with tape or CD media  316 , or other removable media devices, such as magneto-optical media, etc., and a mother board  318 . The motherboard  318  includes, for example, a processor  320 , a RAM  322 , and a ROM  324 , I/O ports  326  which are used to couple to the image acquisition device  200  of FIG. 1, and optional specialized hardware  328  for performing specialized hardware/software functions, such as sound processing, image processing, signal processing, neural network processing, etc., a microphone  330 , and a speaker or speakers  340 . 
     Stored on any one of the above described storage media (computer readable media), the present invention includes programming for controlling both the hardware of the computer  300  and for enabling the computer  300  to interact with a human user. Such programming may include, but is not limited to, software for implementation of device drivers, operating systems, and user applications. Such computer readable media further includes programming or software instructions to direct the general purpose computer  300  to perform tasks in accordance with the present invention. 
     The programming of general purpose computer  300  may include a software module for digitizing and storing images obtained from the image acquisition device  200  of FIG.  1 . Alternatively, it should be understood that the present invention can also be implemented to process digital data derived from images obtained by other means. 
     The invention may also be implemented by the preparation of application specific integrated circuits or by interconnecting an appropriate network of conventional component circuits, as will be readily apparent to those skilled in the art. 
     Although the present invention is described in terms of adaptive rule-based analysis (FIG. 2, steps  40  and  40 ′) occurring prior to ANN analysis (FIG. 2, steps  50  and  50 ′), it should be understood that the ANN analysis can precede the adaptive rule-based analysis. Also, while the preferred embodiment includes ANN analysis, improvement in lung nodule detection, relative to the prior schemes, can be achieved according to the present invention by OR&#39; ing remaining candidate nodules after adaptive rule-based analysis. Thus, if processing simplicity is paramount, one or both of ANN steps  50 , 50 ′ can be eliminated, albeit with a reduction in performance. 
     Obviously, numerous modifications and variations of the present invention are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described herein. 
     APPENDIX 
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