Image analysis – Applications – Biomedical applications
Reexamination Certificate
1997-06-03
2001-10-09
Patel, Jayanti K. (Department: 2623)
Image analysis
Applications
Biomedical applications
C378S037000
Reexamination Certificate
active
06301378
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to the field of computer aided diagnosis of abnormal lesions in medical images. In particular, the invention relates to a fast algorithm for detecting masses in a digital mammogram to assist in the detection of malignant breast cancer tumors at an early stage in their development.
BACKGROUND OF THE INVENTION
Breast cancer in women is a serious health problem, the American Cancer Society currently estimating that over 180,000 U.S. women are diagnosed with breast cancer each year. Breast cancer is the second major cause of cancer death among women, the American Cancer Society also estimating that breast cancer causes the death of over 44,000 U.S. women each year. While at present there is no means for preventing breast cancer, early detection of the disease prolongs life expectancy and decreases the likelihood of the need for a total mastectomy. Mammography using x-rays is currently the most common method of detecting and analyzing breast lesions.
The detection of suspicious portions of mammograms is an important first step in the early diagnosis and treatment of breast cancer.
FIG. 1A
shows a continuum of potentially cancerous shapes found in mammograms, ranging from sharply defined masses on the left, moving rightward to somewhat spiculated (i.e., stellar-shaped) masses, mostly spiculated masses, highly spiculated masses, and then finally to pure spiculations on the right.
Sharply defined masses such as those at the left of
FIG. 1A
are rarely associated with malignant tumors, while the presence of spiculated masses is a strong indicator of malignancy. Pure spiculations, however, are often found among normal fibrous breast tissue and may not indicate a cancerous condition at all. Overall, both the mass qualities and “spiculatedness” qualities of shapes found in mammograms must be analyzed in locating suspicious portions of the mammogram.
While it is important to detect the suspicious portions of an x-ray mammogram as early as possible, i.e. when they are as small as possible, practical considerations can make this difficult. In particular, a typical mammogram may contain myriads of lines corresponding to fibrous breast tissue, and the trained, focused eye of a radiologist is needed to detect suspicious features among these lines. Moreover, a typical radiologist may be required to examine hundreds of mammograms per day, leading to the possibility of a missed diagnosis due to human error.
Accordingly, the need has arisen for a computer-assisted diagnosis (CAD) system for assisting in the detection of abnormal lesions in medical images. The desired CAD system digitizes x-ray mammograms to produce a digital mammogram, and performs numerical image processing algorithms on the digital mammogram. The output of the CAD system is a highlighted display which directs the attention of the radiologist to suspicious portions of the x-ray mammogram. The desired characteristics of a CAD system are high speed (requiring less processing time), high sensitivity (the ability to detect subtle suspicious portions), and high specificity (the ability to avoid false positives).
Many algorithms for processing digital mammograms in CAD systems start by processing the digital mammogram to locate masses (or “densities”). After this step, the “spiculatedness” of these masses is characterized. See Yin et. al., “Computerized Detection of Masses in Digital Mammograms: Analysis of Bilateral Subtraction Images,”
Med. Phys.
18(5), September/October 1991, pp. 955-963, and Sahiner et. al., “Classification of Masses on Mammograms Using a Rubber-Band Straightening Transform and Feature Analysis,”
Medical Imaging
1996, SPIE Symposium on Medical Imaging (San Diego, Calif.), Paper No. 2710-06 at p. 204, the contents of which are hereby incorporated by reference into the present application.
A key shortcoming of the above serial approach, in which masses are first detected and then analyzed in a subsequent step, is that some very suspicious shapes are not recognized. In particular, those masses which are small, but which are highly spiculated, often do not survive the “first cut” of the mass detection routine, which will not recognize masses having density characteristics below a certain threshold. This shortcoming was recognized by Nico Karssemeijer in “Recognition of Stellate Lesions in Digital Mammograms,”
Digital Mammography: Proceedings of the
2
nd International Workshop on Digital Mammography
, York, England, Jul. 10-12 1994 (Elsevier Science 1994), pp. 211-219, the contents of which are hereby incorporated by reference into the present application. There, Karssemeijer proposes an algorithm for the direct detection of spiculations (“stellate patterns”) in a digital mammogram without assuming the presence of a central mass.
Another method for the direct detection of spiculations in digital mammograms is provided in Kegelmeyer et. al., “Computer-aided Mammographic Screening for Spiculated Lesions,”
Radiology
191:331-337 (1994), the contents of which are hereby incorporated by reference into the present application. Yet another method for the direct detection of spiculations, along with linear classification steps which use both mass and spiculation information in identifying suspicious portions of the digital mammogram, is provided by Roehrig et. al. in the above referenced U.S. patent application entitled “Method and Apparatus for Fast Detection of Spiculated Lesions in Digital Mammograms.”
One improvement which may be incorporated into CAD systems is further integration and symmetry between of the steps of mass detection and spiculation detection. Such integration and symmetry would provide for more efficient programming of the CAD system, more efficient processing by the CAD system, and reduced memory requirements. In particular, it would be desirable to execute both mass detection and spiculation detection steps using the same or similar computation engines in the CAD system. Additionally, it would be desirable to harness algorithmic advances made in spiculation detection algorithms by applying them to mass detection algorithms.
Accordingly, it is an object of the present invention to provide a fast computer-assisted diagnosis (CAD) system for assisting in the identification of suspicious masses and spiculations in digital mammograms, the CAD system being capable of producing an output which directs attention to suspicious portions of the x-ray mammogram for increasing the speed and accuracy of x-ray mammogram analysis.
It is a further object of the present invention to provide a method for adapting a spiculation detection algorithm for use in a mass detection algorithm, for increased symmetry and integration of CAD system algorithms, and for adapting algorithmic advances in spiculation detection algorithms to mass detection algorithms.
SUMMARY OF THE INVENTION
These and other objects of the present invention are provided for by an improved CAD system capable of detecting masses in a digital mammogram image, wherein a gradient image is created from the digital mammogram, and wherein information in the gradient image is then processed for identifying masses. In a preferred embodiment, a portion of a spiculation detection algorithm is applied to the gradient image for identifying masses.
A spiculation detection algorithm normally comprises a line detection portion and a post-line detection portion. However, in a preferred embodiment, the post-line detection portion of the spiculation detection algorithm is applied to a gradient image for identifying masses, instead of being applied to a line image for identifying spiculations. Thus, instead of being provided with line and direction parameters, the post-line detection portion of the spiculation detection algorithm is provided with gradient magnitude and gradient direction parameters. The post-line detection portion of the spiculation detection algorithm then operates normally, except that its output corresponds to mass location and mass Idensity information instead of spiculation location and spiculat
Karssemeijer Nico
Te Brake Guido M.
Patel Jayanti K.
Pennie & Edmonds LLP
R2 Technology, Inc.
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