Breast skin line detection in radiographic images

An image segmentation embodiment comprises applying a second derivative operator to the pixels of an image, growing a set of contours using seeding grid points as potential contour starting points, determining a contour strength vector for each of the contour pixels, generating a partial ellipse representing an estimated location of an object in the image, dividing the partial ellipse into a plurality of support sectors with control points, determining a contour strength and position for each contour, adjusting a position of each sector control point based on the contour positions weighted by the contour strengths of the contours centered in the respective sector, fitting the partial ellipse to the adjusted positions of the control points, and generating a segmentation mask of the object based on the partial fitted ellipse.

TECHNICAL FIELD

The present disclosure relates generally to computer-aided detection of tissue areas in radiographic images, and more particularly to a breast segmentation system and method.

BACKGROUND

Radiologists use radiographic images such as mammograms to detect and pinpoint suspicious lesions in a patient as early as possible, e.g., before a disease is readily detectable by other, intrusive methods. As such, there is real benefit to the radiologist being able to locate, based on imagery, extremely small cancerous lesions and precursors. Microcalcifications, particularly those occurring in certain types of clusters, exemplify one signature of concern. Although the individual calcifications tend to readily absorb radiation and can thus appear quite bright in a radiographic image, various factors including extremely small size, occlusion by other natural structure, appearance in a structurally “busy” portion of the image, all sometimes coupled with radiologist fatigue, may make some calcifications hard to detect upon visual inspection.

Computer-Aided Detection (CAD) algorithms have been developed to assist radiologists in locating potential lesions in a radiographic image. CAD algorithms operate within a computer on a digital representation of the mammogram set for a patient. The digital representation can be the original or processed sensor data, when the mammograms are captured by a digital sensor, or a scanned version of a traditional film-based mammogram set. An “image,” as used herein, is assumed to be at least two-dimensional data in a suitable digital representation for presentation to CAD algorithms, without distinction to the capture mechanism originally used to capture patient information. The CAD algorithms search the image for objects matching a signature of interest, and alert the radiologist when a signature of interest is found.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

For example, embodiments discussed herein are generally described in terms of assisting medical personnel in the examination of breast x-ray images, such as those that may be obtained in the course of performing a mammogram. Other embodiments, however, may be used for other situations, including, for example, detecting anomalies in other tissues such as lung tissue, any type of image analysis for statistical anomalies, and the like.

Referring now to the drawings, wherein like reference numbers are used herein to designate like or similar elements throughout the various views, illustrative embodiments of the present invention are shown and described. The figures are not necessarily drawn to scale, and in some instances the drawings have been exaggerated and/or simplified in places for illustrative purposes only. One of ordinary skill in the art will appreciate the many possible applications and variations of the present invention based on the following illustrative embodiments of the present invention.

Referring first toFIG. 1, a system100for assisting in detecting anomalies during, for example, mammograms, is illustrated in accordance with an embodiment. The system100includes an imaging unit102, a digitizer104, and a computer aided detection (CAD) unit106. The imaging unit102captures one or more images, such as x-ray images, of the area of interest, such as the breast tissue. In the embodiment in which the system100is used to assist in analyzing a mammogram, a series of four x-ray images may be taken while the breast is compressed to spread the breast tissue, thereby aiding in the detection of anomalies. The series of four x-ray images include a top-down image, referred to as a cranio caudal (CC) image, for each of the right and left breasts, and an oblique angled image taken from the top of the sternum angled downwards toward the outside of the body, referred to as the medio lateral oblique (MLO) image, for each of the right and left breasts.

The one or more images may be embodied on film or digitized. Historically the one or more images are embodied as x-ray images on film, but current technology allows for x-ray images to be captured directly as digital images in much the same way as modern digital cameras. As illustrated inFIG. 1, a digitizer104allows for digitization of film images into a digital format. The digital images may be formatted in any suitable format, such as industry standard Digital Imaging and Communications in Medicine (DICOM) format.

The digitized images, e.g., the digitized film images or images captured directly as digital images, are provided to a Computer-Aided Detection (CAD) unit106. As discussed in greater detail below, the CAD unit106processes the one or more images to detect possible locations of various types of anomalies, such as calcifications, relatively dense regions, distortions, and/or the like. Once processed, locations of the possible anomalies, and optionally the digitized images, are provided to an evaluation unit108for viewing by a radiologist, the attending doctor, or other personnel, with or without markings indicating positions of any detected possible anomalies. The evaluation unit108may comprise a display, a workstation, portable device, and/or the like.

FIG. 2illustrates components that may be utilized by the CAD unit106(seeFIG. 1) in accordance with an embodiment. Generally, the CAD unit106includes a segmentation unit202, one or more detection units204a-204n, and one or more display pre-processors206a-206n. As will be appreciated, an x-ray image, or other image, may include regions other than those regions of interest. For example, an x-ray image of a breast may include background regions as well as other structural regions such as the pectoral muscle. In these situations, it may be desirable to segment the x-ray image to define a search area, e.g., a bounded region defining the breast tissue, on which the one or more detection units204a-204nis to analyze for anomalies.

The one or more detection units204a-204canalyze the one or more images, or specific regions as defined by the segmentation unit202, to detect specific types of features that may indicate one or more specific types of anomalies in the patient. For example, in an embodiment for use in examining human breast tissue, the detection units204a-204nmay comprise a calcification unit, a density (mass) unit, and a distortion unit. As is known in the medical field, the human body often reacts to cancerous cells by surrounding the cancerous cells with calcium, creating micro-calcifications. These micro-calcifications may appear as small, bright regions in the x-ray image. The calcification unit detects and identifies these regions of the breast as possible micro-calcifications.

It is further known that cancerous regions tend to be denser than surrounding tissue, so a region appearing as a generally brighter region indicating denser tissue than the surrounding tissue may indicate a cancerous region. Accordingly, the density unit analyzes the one or more breast x-ray images to detect relatively dense regions in the one or more images. Because the random overlap of normal breast tissue may sometimes appear suspicious, in some embodiments the density unit may correlate different views of an object, e.g., a breast, to determine if the dense region is present in other corresponding views. If the dense region appears in multiple views, then there is a higher likelihood that the region is truly malignant.

The distortion unit detects structural defects resulting from cancerous cells effect on the surrounding tissue. Cancerous cells frequently have the effect of “pulling in” surrounding tissue, resulting in spiculations that appear as a stretch mark, star pattern, or other linear line patterns.

It should be noted that the above examples of the detection units204a-204n, e.g., the calcification unit, the density unit, and the distortion unit, are provided for illustrative purposes only and that other embodiments may include more or fewer detection units. It should also be noted that some detection units may interact with other detection units, as indicated by the dotted line208. The detection units204a-204nare discussed in greater detail below with reference toFIG. 3.

The display pre-processors206a-206ncreate image data to indicate the location and/or the type of anomaly. For example, micro-calcifications may be indicated by a line encircling the area of concern by one type of line (e.g., solid lines), while spiculations (or other type of anomaly) may be indicated by a line encircling the area of concern by another type of line (e.g., dashed lines).

FIG. 3illustrates components of that may be utilized for each of the detection units204a-204nin accordance with an embodiment. Generally, each of the detection units204a-204nmay include a detector302, a feature extractor304, and a classifier306. The detector302analyzes the image to identify attributes indicative of the type of anomaly that the detection unit is designed to detect, such as calcifications, and the feature extractor304extracts predetermined features of each detected region. For example, the predetermined features may include the size, the signal-to-noise ratio, location, and the like.

The classifier306examines each extracted feature from the feature extractor304and determines a probability that the extracted feature is an abnormality. Once the probability is determined, the probability is compared to a threshold to determine whether or not a detected region is to be reported as a possible area of concern.

A suitable segmentation unit202is specified in U.S. Provisional Application Ser. Nos. 61/400,573 and 61/398,571 and co-filed U.S. patent application Ser. No. 13/168,588, suitable detection units for use in detecting and classifying microcalcifications are specified in U.S. Provisional Application Ser. Nos. 61/343,557 and 61/343,609 and International Application No. PCT/US2011/034696, a suitable detection unit for detecting and classifying malignant masses is specified in U.S. Provisional Application Ser. No. 61/343,552 and International Application No. PCT/US2011/034698, a suitable detection unit for detecting and classifying spiculated malignant masses is specified in U.S. Provisional Application Ser. No. 61/395,029 and International Application No. PCT/US2011/034699, a suitable probability density function estimator is specified in U.S. Provisional Application Ser. No. 61/343,608 and International Application No. PCT/US2011/034700, and suitable display pre-processors are specified in U.S. Provisional Application Ser. No. 61/399,094, all of which are incorporated herein by reference.

The following paragraphs provide greater details regarding a segmentation unit, such as may be utilized as a segmentation unit202(seeFIG. 2) in accordance with an embodiment. In particular, the embodiments described below seek to detect a skin line a radiographic image.

A mammogram can contain background areas, image artifacts, breast tissue, and non-breast tissue regions in some views. A proper segmentation of the image, with the boundaries between each of these areas defined correctly, can provide benefits to CAD performance. First, it instills a radiologist with confidence in the CAD algorithms when a well-segmented image is displayed to the radiologist (and can have the opposite effect when the algorithms identify non-tissue regions as breast and place a suspicious mark outside of the breast area). It is important for thorough examination, however, that the CAD algorithms examine the entire breast portion of the image. This requires that the segmentation not be under inclusive. Also, some CAD algorithms, such as an algorithm that adjusts the base intensity of the image skin line region and algorithms that rely on calcium statistics, can be particularly sensitive to segmentation accuracy.

FIGS. 4A and 4Billustrate “ideal segmentations” for a hypothetical cranio-caudal (CC) view,410,FIG. 4A, and a corresponding hypothetical mediolateral oblique (MLO) view,420,FIG. 4B. In the following embodiments, a few a priori assumptions generally are made about the location of breast tissue in an image, due to variation in breast size and shape, operator positioning of the image device, etc. Generally, as shown inFIGS. 4A and 4B, the breast meets one edge of the image, e.g., the left edge (or can be flipped right-to-left to place the breast edge on the left, based on simple intensity measures). For a CC view, it is expected that a partial ellipse can roughly describe the breast tissue portion of the image. For an MLO view, it is expected that a partial ellipse will roughly describe the central imaged portion of the breast, but a skin line will typically vary from the ellipse and become more vertical near the top of the image, and end along the top of the image. Also, a pectoral line430, with substantially more image density behind it than in the neighboring breast tissue, is generally visible cutting across the upper left portion of the breast region. It is the goal of segmentation to describe, as accurately as possible, the locations of skin lines410and420, and the pectoral line130.

FIG. 5contains a block diagram500for breast segmentation including an embodiment. Segmentation operates independently on each view presented to the system. Block502performs simple removal of edge artifacts, as will be described first. Block504then creates a start model, or initial informed “guess” at the rough boundaries of the breast. The start model is quickly iterated to produce a much closer estimate of the breast boundaries. The start model is fed to a two-stage refinement process, coarse boundary refinement506and fine boundary refinement508. Each stage uses a common algorithm to more precisely align the boundary description with the actual imaged skin line, with stage508operating on a finer resolution image that stage506. On MLO views, a stage510proceeds to locate and describe the pectoral line in the image. Stage512compiles the segmentation information into a segmentation mask that is passed to the CAD algorithms, along with the coordinates of a pectoral-nipple coordinate system.

The remove artifacts stage502seeks to mask out image areas from consideration that might confuse segmentation, and are obviously not tissue regions. For instance, a bright edge may appear as a straight line parallel or nearly parallel to an image edge, as a scanner or film alignment artifact. Such artifacts are easily detected and masked from consideration during segmentation.

FIG. 6Ais a copy ofFIG. 4A, with cross-section indicators forFIG. 6Bsuperimposed. Due to the manner in which a mammography machine compresses a breast for imaging, the majority of the imaged breast has a uniform thickness (and therefore background density in the image), tapering only in a peripheral region to a thin skin line at the breast boundary.FIG. 6Bshows a smoothed intensity curve600for one typical cross-section through the image.FIG. 6Cshows the first derivative610of intensity curve600, andFIG. 6Dshows the second derivative620of intensity curve600. In particular, the second derivative properties weigh significantly in the detection of the breast boundary.

In one embodiment, an input image (with artifacts removed) is integer subsampled to roughly a 1000-micron pixel spacing. The subsampled image is then smoothed, e.g., by convolution with a two-dimensional Gaussian function with significant (multipixel) smoothing to remove fine detail. A two-dimensional second derivative operator is convolved with the smoothed, subsampled image to create a “curvature” image.

One embodiment uses three separate two-dimensional operators to measure curvature in x, curvature in y, and curvature along the diagonals. These are then combined in a Hessian matrix that described the curvature at each point:

The Hessian matrix at each point is then decomposed to extract two eigenvalues and an angle of orientation for the major/minor axes of the Hessian:

Examination of eigenvalues λ1and λ2determines the type of curvature present at each point. When both eigenvalues are positive, the intensity distribution is concave up at the point Likewise, when both eigenvalues are negative, the intensity distribution is concave down at the point. And when one eigenvalue is positive and the other is negative, the point is a saddle point.

As the presence of tissue in the path of x-rays causes absorption as compared to the free-air path in areas adjoining a tissue region, the present embodiment relies on the fact that moving along an image path that crosses a skin line will result in an increase in image intensity, and therefore a concave up curvature signal. Accordingly, the present embodiment masks out points where both eigenvalues are negative. When at least one eigenvalue is positive, the present embodiment selects that eigenvalue for consideration, and adjusts θ if necessary to align with (rather than orthogonal to) the direction of curvature of the largest positive eigenvalue. As an option, points can be masked from consideration when the largest positive eigenvalue fails to meet a threshold set greater than zero, and/or when one eigenvalue is positive and the other negative, the magnitude of the negative eigenvalue exceeds the positive eigenvalue.

FIG. 7illustrates a seeding grid700for an initial contour calculation. The seeding grid could include each pixel, but experimentation has shown roughly equal performance from a coarser seeding, e.g., placing a seed point every four or five pixels both in row and column.

Each pixel on seeding grid700is considered in turn as a start pixel for a contour, based on an eigenvalue selection process, e.g., as described above. When the current pixel on the curvature image meets the positive maximum eigenvalue criteria, it becomes a start pixel for a contour. The two neighboring pixels in the two directions orthogonal to θ are next checked. If either of those neighbors meets the positive maximum eigenvalue criteria, they are included with the start pixel in a contour, and their neighbor opposite the start pixel is checked for inclusion in the contour as well. This process continues until it terminates at both ends due to a neighbor pixel not meeting the positive eigenvalue criteria. Optionally, contour growth can also terminate when the contour meets a maximum length parameter.FIG. 8illustrates an example of a contour set800, after the initial contour growth phase.

Once an initial contour set has been formed, several filters are available to prune the contour set. A good initial filter examines the smoothed image intensity profile as one moves through and beyond the contour in the direction θ. When the contour is near the skin line, the intensity will generally increase by some minimum value h over a distance d, as one moves past the contour (the parameters h and d will depend to some extent on the imaging device and its specifications and usage). After this initial increase, the profile should be moving through a fairly constant minimum density of breast tissue, and therefore should not dip back towards the initial intensity. Practically, after a few inches of examining the tissue intensity profile, if no dip in intensity is observed, the contour can be confirmed. A pruned contour map900is shown inFIG. 9.

FIG. 9also shows a second level of contour pruning,910. Contour pruning910enforces a directionality constraint on θ, based on location in the image. In the top third of the image, a directionality constraint920requires the contour to be aligned somewhere between pointing to the left and pointing straight down. In the middle third of the image, a directionality constraint930requires the contour to be aligned somewhere between pointing up and to the left at 45 degrees, and down and to the left at 45 degrees. And in the bottom third of the image, a directionality constraint940requires the contour to be aligned somewhere between pointing to the left and pointing straight up. These constraints remove contours that, e.g., follow some internal structure in the breast that is at an extremely unlikely angle for a skin line.

Once the contours have been pruned, they are ready to be used in an initial rough estimate of breast position. The initial estimate has as a goal the rough location of the centroid and extent of the mapped contours. A pruned contour set1000is combined with nine different directional operators1010, spaced evenly between straight up and straight down as viewed on the image, rotating clockwise. Each directional operator is defined as a vector {right arrow over (D)}iat its assigned angle. For each pixel (x,y) that is part of a contour, its contour strength (maximum positive eigenvalue) is converted to vector form {right arrow over (C)}x,yat angle θ. For each directional operator, an overall strength Si, a strength-weighted average row Xi, and a strength-weighted average column Yi are calculated as follows:

This formulation provides a maximum contribution to a contour pixel's eigenvalue that is aligned with the directional operator, decreasing to zero contribution when the alignment is orthogonal, and then clamped at zero by the max( ) function for contours aligned oppositely (negative dot product) with the directional operator.

The initial estimate combines the weighted results from the nine operators to produce estimates for seeding the breast location, as shown inFIG. 11. For instance, the center of the breast along the left side of the image can be estimated as the weighted sum

X=∑i=08⁢Xi/∑i=08⁢Si
and the protrusion of the breast into the image from left to right can be estimated as

Y=∑i=08⁢Yi/∑i=08⁢Si.
Various measures can be used to initially estimate the vertical extent of the breast, for instance defining the upper side XU=X8/S8and the lower side XL=X0/S0. The extent measures XU, XL, and Y are purposely underinclusive to place the initial estimate interior to the actual breast extent. These parameters are used to construct an ellipse1110that intersects the image so as to define an initial segmentation estimate. The next step will refine the initial estimate.

FIG. 12illustrates the setup1200for the first refinement step from the initial estimate1210. A number of points (marked by Xs inFIG. 12, one labeled as E3(0)) are set up equally spaced along the perimeter of ellipse1110. Each point is allowed to consider contours for support that are closer to that point than to other points on ellipse1110. The support regions for the eight points shown are marked respectively P1to P8.

FIG. 12Ashows an enlarged view of support region P3for control point E3(0). Within the support region, each contour centered in that region is examined, and is allowed to make a single contribution to the realignment of the estimated skin line point in support region P3. For each contour k in the support region P3, the system calculates a dot product between the vectorized contour strength eigenvalue {right arrow over (C)}x,yat each contour pixel x,y and the normal to ellipse1110at the control point,1210. Among all of the dot products for contour k, a contour strength Sk is selected as the maximum dot product seen for the contour, and the contour's position Pk is set to the coordinates of the maximum dot product. If the contour strength vector and the normal to ellipse do not agree within at least 45 degrees in orientation, however, the contour's contribution is ignored (see, e.g., contour1220, which has a strong contribution but an errant direction, and so would be ignored). After all contours have been examined in this manner, an iterated control point E3(1) is adjusted using the weighted contour positions. The adjustment can be based on a weighted median or percentile or a weighted sum, such as the following formula that uses a parameter to move the control point some fraction of the weighted residuals:

The updated control points are subsequently used to calculate a new best fit ellipse1310,FIGS. 13A and 13B, for the breast skin line. This procedure is repeated (new equally spaced control points are calculated and theFIG. 12calculations are repeated) until either a maximum number of iterations is reached or until the ellipse parameters change by less than a defined threshold.

Once ellipse updates have converged or stopped, the ellipse is converted to a start model1410, as shown inFIG. 14. The start model describes the breast boundary as a set of equally spaced points lying along the start model ellipse. The start model should lie fairly close to the actual skin line, with the accuracy depending on how close the breast image approximates an ellipse.

For an MLO view such as shown inFIG. 15, additional calculations are performed to provide a start model for the upper skin line and pectoral line430. The pectoral line presents itself as an extremely strong second derivative contour in the upper left corner of the image, with an orientation roughly up and left. The outermost such contour found provides points for a least squares fit to a pectoral line1530.

For the upper skin line, contours in the area above elliptical fit1510and to the right of the pectoral line, and with a generally left orientation are considered. Methods such as those described above for the elliptical fit are used to select and weight the contours, but this time for a least squares line fit to a line1520.

Like for the CC view ofFIG. 14, the start model is converted to a set of equally spaced points. Where line1520and ellipse1510overlap, the outer breast bound is defined at each row as the outermost of line1520and ellipse1510. InFIG. 15, the projections of lines1520and1530do not intersect within the image bounds. In some images, however, these lines will intersect within the view. In such case, the outer breast bound is defined at each row as the outermost of the two lines.

Returning toFIG. 5, the start model as described herein is useful for coarse and fine boundary refinement506and508at finer scales, and without the constraint of fitting simple geometrical models. The pectoral line is also more accurately located at a fine scale at block510. Finally, a full-resolution segmentation mask defines each pixel in the image as background, breast tissue, or muscle.

Unless indicated otherwise, all functions described herein may be performed in either hardware or software, or some combination thereof. In a preferred embodiment, however, the functions are performed by a processor such as a computer or an electronic data processor in accordance with code such as computer program code, software, and/or integrated circuits that are coded to perform such functions, unless otherwise indicated.

For example,FIG. 16is a block diagram of a computing system1600that may also be used in accordance with an embodiment. It should be noted, however, that the computing system1600discussed herein is provided for illustrative purposes only and that other devices may be used. The computing system1600may comprise, for example, a desktop computer, a workstation, a laptop computer, a personal digital assistant, a dedicated unit customized for a particular application, or the like. Accordingly, the components of the computing system1600disclosed herein are for illustrative purposes only and other embodiments of the present invention may include additional or fewer components.

In an embodiment, the computing system1600comprises a processing unit1610equipped with one or more input devices1612(e.g., a mouse, a keyboard, or the like), and one or more output devices, such as a display1614, a printer1616, or the like. Preferably, the processing unit1610includes a central processing unit (CPU)1618, memory1620, a mass storage device1622, a video adapter1624, an I/O interface1626, and a network interface1628connected to a bus1630. The bus1630may be one or more of any type of several bus architectures including a memory bus or memory controller, a peripheral bus, video bus, or the like. The CPU1618may comprise any type of electronic data processor. For example, the CPU1618may comprise a processor (e.g., single core or multi-core) from Intel Corp. or Advanced Micro Devices, Inc., a Reduced Instruction Set Computer (RISC), an Application-Specific Integrated Circuit (ASIC), or the like. The memory1620may comprise any type of system memory such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), read-only memory (ROM), a combination thereof, or the like. In an embodiment, the memory1620may include ROM for use at boot-up, and DRAM for data storage for use while executing programs. The memory1620may include one of more non-transitory memories.

The mass storage device1622may comprise any type of storage device configured to store data, programs, and other information and to make the data, programs, and other information accessible via the bus1628. In an embodiment, the mass storage device1622is configured to store the program to be executed by the CPU1618. The mass storage device1622may comprise, for example, one or more of a hard disk drive, a magnetic disk drive, an optical disk drive, or the like. The mass storage device1622may include one or more non-transitory memories.

The video adapter1624and the I/O interface1626provide interfaces to couple external input and output devices to the processing unit1610. As illustrated inFIG. 16, examples of input and output devices include the display1614coupled to the video adapter1624and the mouse/keyboard1612and the printer1616coupled to the I/O interface1626. Other devices may be coupled to the processing unit1610.

The network interface1628, which may be a wired link and/or a wireless link, allows the processing unit1610to communicate with remote units via the network1632. In an embodiment, the processing unit1610is coupled to a local-area network or a wide-area network to provide communications to remote devices, such as other processing units, the Internet, remote storage facilities, or the like.

It should be noted that the computing system1600may include other components. For example, the computing system1600may include power supplies, cables, a motherboard, removable storage media, cases, a network interface, and the like. These other components, although not shown, are considered part of the computing system1600. Furthermore, it should be noted that any one of the components of the computing system1600may include multiple components. For example, the CPU1618may comprise multiple processors; the display1614may comprise multiple displays, and/or the like. As another example, the computing system1600may include multiple computing systems directly coupled and/or networked.

Additionally, one or more of the components may be remotely located. For example, the display may be remotely located from the processing unit. In this embodiment, display information, e.g., locations and/or types of abnormalities, may be transmitted via the network interface to a display unit or a remote processing unit having a display coupled thereto.

Although several embodiments and alternative implementations have been described, many other modifications and implementation techniques will be apparent to those skilled in the art upon reading this disclosure. Various parameters and thresholds exist and can be varied for a given implementation with given data characteristics, with experimentation and ultimate performance versus computation time tradeoffs necessary to arrive at a desired operating point. Many different statistical variations exist for combining measurements to form an estimate, and can be substituted for the exemplary techniques described herein for combining measurements.

Although the specification may refer to “an”, “one”, “another”, or “some” embodiment(s) in several locations, this does not necessarily mean that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment.