Image analysis – Applications – Biomedical applications
Reexamination Certificate
2000-02-15
2004-07-06
Chang, Jon (Department: 2623)
Image analysis
Applications
Biomedical applications
C382S156000, C382S173000, C382S203000, C128S922000, C128S925000
Reexamination Certificate
active
06760468
ABSTRACT:
TECHNICAL FIELD
The present invention relates to an automated method and system for digital image processing of radiologic images and, more specifically, to an automated method and system for the detection of abnormalities, like lung nodules in radiological chest images, using digital image processing and artificial neural networks.
BACKGROUND OF THE INVENTION
Lung cancer, next to heart disease, is the second highest leading cause of death in the United States. Successful detection of early-stage cancer tumors is able to increase the cure rate. Detection and diagnosis of cancerous lung nodules in chest radiographs are among the most important and difficult tasks performed by radiologists. To date, diagnosis in x-ray chest radiograph is the most important diagnostic procedure for detecting early-stage, clinically occult lung cancer. However, the radiographic miss rate for the detection of lung nodules is quite high. Observer error, which causes these lesions to be missed, may be due to the camouflaging effect of the surrounding anatomic background on the nodule of interest or to the subjective and varying decision criteria used by radiologists. Under-reading of a radiograph may be due to many other reasons, including lack of clinical data, focusing of attention on another abnormality by virtue of a specific clinical question, etc. However, most peripheral lung cancers are visible, in retrospect, on previous films. Thus, a need remains for an automated method and system for digital image processing of radiographic images to alert radiologists to the location of highly suspect lung nodules. Early radiological detection of lung nodules can significantly improve the chances of survival of lung cancer patients. Through its capability to locate the presence of nodules commonly obscured by overlying ribs, bronchi, blood vessels, and other normal anatomic structures on radiographs, the automated system and method should reduce false negative diagnosis, hence leading to earlier detection of pulmonary lung cancers and of metastatic nodules with high accuracy.
PRIOR ART
Several computer-aided diagnosis techniques using digital image processing and artificial neural networks have been described in the open literature and in patents. Of particular relevance to the present invention are the following:
U.S. Pat. No. 4,907,156 to Doi et al. describes a method for detecting and displaying abnormal anatomic regions existing in a digital X-ray image. A single projection digital X-ray image is processed to obtain signal-enhanced image data with a maximum signal-to-noise ratio (SNR) and is also processed to obtain signal-suppressed image data with a suppressed SNR. Then, difference image data are formed by subtraction of the signal-suppressed image data from the signal-enhanced image data to remove low-frequency structured anatomic background, which is basically the same in both the signal-suppressed and signal-enhanced image data. Once the structured background is removed, feature extraction is performed. For the detection of lung nodules, pixel thresholding is performed, followed by circularity and/or size testing of contiguous pixels surviving. thresholding. Threshold levels are varied, and the effect of varying the threshold on circularity and size is used to detect nodules. For the detection of mammographic microcalcifications, pixel thresholding and contiguous pixel area thresholding are performed. Clusters of suspected abnormalities are then detected. The major differences between the method and system of the present invention and the approach of Doi et. al include the use of a neural network architecture, use of a sphericity test, use of multiple anatomic categories, body part segmentation, and use of phantom nodules in the present invention to further improve the detection accuracy.
U.S. Pat. No. 5,463,548 to Asada et al. describes a system for computer-aided differential diagnosis of diseases, and in particular, computer-aided differential diagnosis using neural networks. A first design of the neural network distinguishes between a plurality of interstitial lung diseases on the basis of inputted clinical parameters and radiographic information. A second design distinguishes between malignant and benign mammographic cases based upon similar inputted clinical and radiographic information. The neural networks were first trained using a database made up of hypothetical cases for each of the interstitial lung diseases and for malignant and benign cases. The performance of the neural network was evaluated using receiver operating characteristic (ROC) analysis. The decision performance of the neural network was compared to experienced radiologists and achieved a high performance comparable to that of the experienced radiologists. The neural network according to the invention can be made up of a single network or a plurality of successive or parallel networks. The neural network according to the invention can also be interfaced to a computer that provides computerized automated lung texture analysis to supply radiographic input data in an automated manner. However, Asada's method is limited to the detection of lung diseases-not including lung cancer, which presents different symptoms.
Y. S. P. Chiou, Y. M. F. Lure, and P. A. Ligomenides, “Neural Network Image Analysis and Classification in Hybrid Lung Nodule Detection (HLND) System”,
Neural Networks for Processing III Proceedings of the
1993
IEEE
-
SP Workshop
, pp. 517-526. The Chiou et al. article describes a Hybrid Lung Nodule Detection (HLND) system based on artificial neural network architectures, which is developed for improving diagnostic accuracy and speed of lung cancerous pulmonary radiology. The configuration of the HLND system includes the following processing phases: (1) pre-processing to enhance the figure-background contrast; (2) quick selection of nodule suspects based upon the most pertinent feature of nodules; and (3) complete feature space determination and neural network classification of nodules. Major differences between the present invention and the Chiou et al. article are that the present invention introduces nodule phantoms, a neural network for data fusion, certain ratios between multiple anatomic categories, sphericity testing, and body part segmentation.
S. C. Lo, J. S. Lin, M. T. Freedman, and S. K. Mun, “Computer-Assisted Diagnosis of Lung Nodule Detection Using Artificial Convolution Neural Network”,
Proceedings of SPIE Medical Imaging VI
, Vol. 1898, 1993. This article describes nodule detection methods using a convolutional neural network consisting of a two-dimensional connection trained with a back propagation learning algorithm, in addition to thresholding and circularity calculation, morphological operation, and a two-dimensional sphere profile matching technique. Major differences between the present invention and Lo at al. article are that the present invention introduces nodule phantoms, a neural network for data fusion, certain ratios between multiple anatomic categories, sphericity testing, and body part segmentation. The architectures used in the Lo et al. article and the present invention are significantly different.
J-S Lin, P. Ligomenides, S-C B. Lo, M. T. Freedman, S. K. Mun, “A Hybrid Neural-Digital Computer Aided Diagnosis System for Lung Nodule Detection on Digitized Chest Radiographs”,
Proc.
1994
IEEE Seventh Symp. on Computer Based Medical Systems
, pp. 207-212, describes a system for the detection and classification of cancerous lung nodules utilizing image processing and neural network techniques. However, the system described in this article presents differences from the present invention similar to those between the system described in the Lo et al. 1993 article and the present invention. S. C. B. LO, S. L. A. Lou, J. S. Lin, M. T. Freedman, M. V. Chien, and S. K. Mun, “Artificial Convolution Neural Network Techniques and Applications for Lung Nodule Detection”,
IEEE Transactions on Medical Imaging,
1995, Vol. 14, No. 4, pp 5 711-718, describes a system for detection and clas
Lin Jyh-Shyan
Lure Yuan-Ming Fleming
Yeh Hwa-Young Michael
Chang Jon
Deus Technologies LLC
Gluck Jeffrey W.
Kinberg Robert
Venable LLP
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