Method and System for Detecting Small Structures in Images

The potential difficulties and pitfalls of many automated detection techniques can be summarized as follows: a. Too little enhancement may preclude the detection of minor microcalcification peaks while too much enhancement may increase significantly the amplitude of small background structures (noise) and thus produce a large number of false detections. An acceptable compromise may not exist in some images, and in those images where it exists, it can change from image to image and can be difficult to determine. b. A small, square region of analysis (moving kernel) where operational parameters are computed, may be inappropriate for the natural shape of microcalcifications and automated detection based on such approaches may depart considerably, in some cases, from the outcome of visual detection. c. A large number of parameters whose values have to be entered manually (e.g., Fam) is not a viable approach for expedient clinical use.

In generalized form, the invention is an image analysis method and apparatus implemented in a computer for automatically detecting small structures in images. The specific embodiment described below is useful as a diagnostic aid that determines, on a digitized mammogram, the location of clusters of microcalcifications whose morphological properties are similar to those observed in malignant microcalcification clusters confirmed by biopsy.

The invention first digitizes the mammogram and preprocesses the digitized image through a nonlinear filter that eliminates very high frequency noise. The resultant image is then stored in the form of a matrix in the memory of a data processing means/computer.

In the computer, the digitized image is segmented into candidate structures by first locating seed pixels, defined as pixels which are brighter than their immediate neighbors. Each seed pixel in the image is used as the basis for constructing/growing two regions. In the first region, adjacent pixels are added to the region if: (1) they have a gradient value higher than the pixels they adjoin (touch) in the grown region and (2) they have a gray level lower than the pixels they adjoin in the grown region. The second region is constructed/grown by adding adjacent pixels if they have a gray level lower than the pixels they adjoin in the grown region.

Following construction of the two regions, various features are measured using the two regions around each seed point. Certain of the features, taken together, characterize each candidate structure and are used as input to a classifier, such as a neural network. The classifier will then distinguish the candidate structures between structures of interest and background. The structures detected by the classifier will then be presented to a clustering algorithm. A detected structure that is less than a threshold distance from the nearest structure in a cluster is included in that cluster. Finally, the results are displayed either on a monitor or on hard copy with a frame around the detected cluster.

US 6,738,500

Type of Offer: Licensing

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